1
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Rosenberg E, Andersen TI, Samajdar R, Petukhov A, Hoke JC, Abanin D, Bengtsson A, Drozdov IK, Erickson C, Klimov PV, Mi X, Morvan A, Neeley M, Neill C, Acharya R, Allen R, Anderson K, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bilmes A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Campero J, Chang HS, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Barba ADT, Demura S, Di Paolo A, Dunsworth A, Earle C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Garcia G, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hill G, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Mandrà S, Martin O, Martin S, McClean JR, McEwen M, Meeks S, Miao KC, Mieszala A, Montazeri S, Movassagh R, Mruczkiewicz W, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Omonije S, Opremcak A, Potter R, Pryadko LP, Quintana C, Rhodes DM, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Sivak V, Skruzny J, Smith WC, Somma RD, Sterling G, Strain D, Szalay M, Thor D, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Khemani V, Gopalakrishnan S, Prosen T, Roushan P. Dynamics of magnetization at infinite temperature in a Heisenberg spin chain. Science 2024; 384:48-53. [PMID: 38574139 DOI: 10.1126/science.adi7877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text]. The first two moments of [Formula: see text] show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Affiliation(s)
- E Rosenberg
- Google Research, Mountain View, CA, USA
- Department of Physics, Cornell University, Ithaca, NY, USA
| | | | - R Samajdar
- Department of Physics, Princeton University, Princeton, NJ, USA
- Princeton Center for Theoretical Science, Princeton University, Princeton, NJ, USA
| | | | - J C Hoke
- Department of Physics, Stanford University, Stanford, CA, USA
| | - D Abanin
- Google Research, Mountain View, CA, USA
| | | | - I K Drozdov
- Google Research, Mountain View, CA, USA
- Department of Physics, University of Connecticut, Storrs, CT, USA
| | | | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - A Morvan
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - R Allen
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - A Bilmes
- Google Research, Mountain View, CA, USA
| | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - J Campero
- Google Research, Mountain View, CA, USA
| | - H-S Chang
- Google Research, Mountain View, CA, USA
| | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | - C Earle
- Google Research, Mountain View, CA, USA
| | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - G Garcia
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | - G Hill
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- QSI, Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - S Mandrà
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | - S Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | - S Meeks
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - S Omonije
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - V Sivak
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | | | - R D Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - D Thor
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | | | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - V Khemani
- Department of Physics, Stanford University, Stanford, CA, USA
| | | | - T Prosen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - P Roushan
- Google Research, Mountain View, CA, USA
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2
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Cheng J, Wu Y, Duan J, Polat G, Hong S, Cheng J. The influence of SRB on corrosion behavior of Cu-based medium-entropy alloy coating sprayed by HVOF. Bioelectrochemistry 2024; 156:108633. [PMID: 38160511 DOI: 10.1016/j.bioelechem.2023.108633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024]
Abstract
In this study, a novel Cu-based (Cu55Al20Ni12Ti8Si5, at.%) medium-entropy alloy (MEA) coating was prepared by high-velocity oxygen-fuel (HVOF) spraying technology. Thermo-Calc was employed to simulate the phase diagram of the alloy system. Phase composition and microstructure of the as-sprayed coating were characterized by means of XRD, FESEM, TEM and STEM/EDX. The effect of sulfate-reducing bacteria (SRB) on the corrosion behavior of the coating and the as-cast Ni-Al bronze (NAB) was investigated using electrochemical measurements and surface characterization. The Thermo-Cala simulation results showed that the alloy system presented a single BCC solid solution phase, while the detailed characterization of microstructure indicated that a few NiTi-rich B2-ordered precipitates could be also found in the as-sprayed coating other than the Cu-rich BCC matrix. Electrochemical studies illustrated that the coating exhibited superior corrosion resistance than the NAB in SRB medium, the corrosion acceleration efficiency induced by SRB of the NAB (95.3 %) was more severe than that of the coating (63.8 %). Surface analysis results demonstrated the occurrence of pitting corrosion and the formation of Cu2S on the coating surface after corroded in SRB medium. Corrosive metabolite HS- induced microbiologically influenced corrosion was considered as the main corrosion acceleration mechanism caused by SRB.
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Affiliation(s)
- Jie Cheng
- College of Materials Science and Engineering, Hohai University, Nanjing 211100, China; College of Materials Science and Engineering, Nanjing Tech University, Nanjing 210009, China
| | - Yuping Wu
- College of Materials Science and Engineering, Hohai University, Nanjing 211100, China.
| | - Jizhou Duan
- Key laboratory of marine environmental corrosion and biofouling, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
| | - Gökhan Polat
- Department of Metallurgical and Materials Engineering, İzmir Katip Çelebi University, İzmir 35620, Turkey
| | - Sheng Hong
- College of Materials Science and Engineering, Hohai University, Nanjing 211100, China
| | - Jiangbo Cheng
- College of Materials Science and Engineering, Hohai University, Nanjing 211100, China
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3
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Zhu X, Hong S, Bu J, Liu Y, Liu C, Li R, Zhang T, Zhang Z, Li L, Zhou X, Hua Z, Zhu B, Hou B. Antiviral memory B cells exhibit enhanced innate immune response facilitated by epigenetic memory. Sci Adv 2024; 10:eadk0858. [PMID: 38552009 PMCID: PMC10980274 DOI: 10.1126/sciadv.adk0858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 02/26/2024] [Indexed: 04/01/2024]
Abstract
The long-lasting humoral immunity induced by viral infections or vaccinations depends on memory B cells with greatly increased affinity to viral antigens, which are evolved from germinal center (GC) responses. However, it is unclear whether antiviral memory B cells represent a distinct subset among the highly heterogeneous memory B cell population. Here, we examined memory B cells induced by a virus-mimicking antigen at both transcriptome and epigenetic levels and found unexpectedly that antiviral memory B cells exhibit an enhanced innate immune response, which appeared to be facilitated by the epigenetic memory that is established through the memory B cell development. In addition, T-bet is associated with the altered chromatin architecture and is required for the formation of the antiviral memory B cells. Thus, antiviral memory B cells are distinct from other GC-derived memory B cells in both physiological functions and epigenetic landmarks.
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Affiliation(s)
- Xiping Zhu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Sheng Hong
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Jiachen Bu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yingping Liu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Can Liu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Runhan Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tiantian Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhuqiang Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Liping Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xuyu Zhou
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhaolin Hua
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bing Zhu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- New Cornerstone Science Laboratory, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Baidong Hou
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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4
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Mi X, Michailidis AA, Shabani S, Miao KC, Klimov PV, Lloyd J, Rosenberg E, Acharya R, Aleiner I, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Malone FD, Martin O, McClean JR, McEwen M, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Roushan P, Smelyanskiy V, Abanin DA. Stable quantum-correlated many-body states through engineered dissipation. Science 2024; 383:1332-1337. [PMID: 38513021 DOI: 10.1126/science.adh9932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 02/13/2024] [Indexed: 03/23/2024]
Abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Affiliation(s)
- X Mi
- Google Research, Mountain View, CA, USA
| | - A A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - S Shabani
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | - J Lloyd
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | | | - R Acharya
- Google Research, Mountain View, CA, USA
| | - I Aleiner
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - C Chou
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | - A G Dau
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | | | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Software and Information (QSI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | | | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | | | | | - A Morvan
- Google Research, Mountain View, CA, USA
| | | | | | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | | | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Google Research, Mountain View, CA, USA
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - R Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z J Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | - P Roushan
- Google Research, Mountain View, CA, USA
| | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
- Department of Physics, Princeton University, Princeton, NJ, USA
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5
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Hong S, Deng H, Zheng Z, Deng Y, Chen X, Gao L, Chen Y, Liu M. The influence of variations in actual evapotranspiration on drought in China's Southeast River basin. Sci Rep 2023; 13:21336. [PMID: 38049499 PMCID: PMC10696048 DOI: 10.1038/s41598-023-48663-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 11/29/2023] [Indexed: 12/06/2023] Open
Abstract
Revealing changes in actual evapotranspiration is essential to understanding regional extreme hydrological events (e.g., droughts). This study utilized the Global Land Evaporation Amsterdam Model (GLEAM) to analyse the spatial and temporal characteristics of actual evapotranspiration over 40 years in the Southeast River basin of China. The relationship between changes in actual evapotranspiration and the drought index was quantified. The results indicated a significant increase in actual evapotranspiration in the Southeast River basin from 1981 to 2020 (2.51 mm/year, p < 0.01). The actual evapotranspiration components were dominated by vegetation transpiration (73.45%) and canopy interception (18.26%). The actual evapotranspiration was closely related to the normalised difference vegetation index (r = 0.78, p < 0.01), and vegetation changes could explain 10.66% of the increase of actual evapotranspiration in the Southeast River basin since 2000. Meanwhile, actual evapotranspiration and standardised precipitation evapotranspiration index (SPEI) showed a highly significant negative spatial correlation, with a Moran's I index of - 0.513. The rise in actual evapotranspiration is an important trigger factor for seasonal droughts in the region. Therefore, these results help deepen the understanding of hydro-climatic process changes in the southeastern coastal region of China.
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Affiliation(s)
- Sheng Hong
- Institute of Geography, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Engineering Research Centre for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China
- Key Laboratory of Humid Subtropical Eco-Geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Key Laboratory for Plant Eco-Physiology, Fujian Normal University, Fuzhou, 350117, China
| | - Haijun Deng
- Institute of Geography, Fujian Normal University, Fuzhou, 350117, China.
- Fujian Provincial Engineering Research Centre for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China.
- Key Laboratory of Humid Subtropical Eco-Geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China.
- Fujian Provincial Key Laboratory for Plant Eco-Physiology, Fujian Normal University, Fuzhou, 350117, China.
| | - Zhouyao Zheng
- Institute of Geography, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Engineering Research Centre for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China
- Key Laboratory of Humid Subtropical Eco-Geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Key Laboratory for Plant Eco-Physiology, Fujian Normal University, Fuzhou, 350117, China
| | - Yu Deng
- Institute of Geography, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Engineering Research Centre for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China
- Key Laboratory of Humid Subtropical Eco-Geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Key Laboratory for Plant Eco-Physiology, Fujian Normal University, Fuzhou, 350117, China
| | - Xingwei Chen
- Institute of Geography, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Engineering Research Centre for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China
- Key Laboratory of Humid Subtropical Eco-Geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Key Laboratory for Plant Eco-Physiology, Fujian Normal University, Fuzhou, 350117, China
| | - Lu Gao
- Institute of Geography, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Engineering Research Centre for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China
- Key Laboratory of Humid Subtropical Eco-Geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Key Laboratory for Plant Eco-Physiology, Fujian Normal University, Fuzhou, 350117, China
| | - Ying Chen
- Institute of Geography, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Engineering Research Centre for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China
- Key Laboratory of Humid Subtropical Eco-Geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Key Laboratory for Plant Eco-Physiology, Fujian Normal University, Fuzhou, 350117, China
| | - Meibing Liu
- Institute of Geography, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Engineering Research Centre for Monitoring and Assessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China
- Key Laboratory of Humid Subtropical Eco-Geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China
- Fujian Provincial Key Laboratory for Plant Eco-Physiology, Fujian Normal University, Fuzhou, 350117, China
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Wei Z, Shi X, Cui D, Wei Z, Hong S. Effect of 3.5 % NaCl solution with different Na 2S concentrations on ultrasonic cavitation erosion behaviors of HVOF sprayed WC-Ni coatings. Ultrason Sonochem 2023; 101:106707. [PMID: 38039594 PMCID: PMC10711220 DOI: 10.1016/j.ultsonch.2023.106707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 11/15/2023] [Accepted: 11/21/2023] [Indexed: 12/03/2023]
Abstract
In this article, the WC-10Ni coatings were fabricated by HVOF spray, then the ultrasonic cavitation erosion performances of the coatings in distilled water and 3.5 wt% NaCl solution with various Na2S concentrations (0, 20 and 200 ppm) were investigated. The results of the cumulative volume loss of the coating in different mediums showed that the coating exhibited enhanced cavitation erosion resistance with the increase of Na2S concentrations in medium. The reason for the improvement on the cavitation erosion performance was the growth of corrosion product films containing sulphide. In comparison with the coating after cavitation erosion in medium without Na2S, no large craters and deep grooves were observed on the eroded coating surface in medium with Na2S. The ultrasonic cavitation damage of the coating manifests as the spall of the metal binder phase (Ni) and exposure of the hard phase (WC).
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Affiliation(s)
- Ziyu Wei
- College of Materials Science and Engineering, Hohai University, 8 Focheng West Road, Nanjing 211100, PR China
| | - Xinlu Shi
- College of Materials Science and Engineering, Hohai University, 8 Focheng West Road, Nanjing 211100, PR China
| | - Dandong Cui
- College of Materials Science and Engineering, Hohai University, 8 Focheng West Road, Nanjing 211100, PR China
| | - Zheng Wei
- College of Materials Science and Engineering, Hohai University, 8 Focheng West Road, Nanjing 211100, PR China
| | - Sheng Hong
- College of Materials Science and Engineering, Hohai University, 8 Focheng West Road, Nanjing 211100, PR China.
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7
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Cai Z, Duan Y, Li W, Liu Z, Gong Z, Hong S, He X, Xuanyuan X, Chen Y, Bi X, Wang W. FANCI serve as a prognostic biomarker correlated with immune infiltrates in skin cutaneous melanoma. Front Immunol 2023; 14:1295831. [PMID: 38077326 PMCID: PMC10703153 DOI: 10.3389/fimmu.2023.1295831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023] Open
Abstract
Background As a member of tumor, Skin cutaneous melanoma (SKCM) poses a serious threat to people's health because of its strong malignancy. Unfortunately, effective treatment methods for SKCM remain lacking. FANCI plays a vital role in the occurrence and metastasis of various tumor types. However, its regulatory role in SKCM is unclear. The purpose of this study was to explore the association of FANCI with SKCM. Methods This study investigated the expression of FANCI in GSE46517, GSE15605, and GSE114445 from the Gene Expression Omnibus database and The Cancer Genome Atlas (TCGA)-SKCM datasets using the package "limma" or "DESeq2" in R environment and also investigated the prognostic significance of FANCI by utilizing the GEPIA database. Additionally, our research made use of real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC) staining to verify FANCI expression between SKCM and normal tissues and developed the knockdown of FANCI in A375 and A875 cells to further analyze the function of FANCI. Finally, this study analyzed the correlation of FANCI and tumor-infiltrating immune cells by CIBERSORT, ESTIMATE, and ssGSEA algorithms. Results The FANCI level was increasing in SKCM tissues from GSE46517, GSE15605, GSE114445, and TCGA-SKCM. However, high FANCI expression correlated with poor overall survival. The RT-qPCR and IHC confirmed the accuracy of bioinformatics. Knocking down FANCI suppresses A375 and A875 cell proliferation, migration, and invasion. FANCI could be involved in the immunological milieu of SKCM by regulating immune responses and infiltrating numerous immune cells, particularly neutrophils, CD8+ T cells, and B cells. Furthermore, patients with SKCM who have a high FANCI expression level are reported to exhibit immunosuppression, whereas those with a low FANCI expression level are more likely to experience positive outcomes from immunotherapy. Conclusions The increased FANCI expression in SKCM can be a prognostic biomarker. Knockdown FANCI can reduce the occurrence and progression of SKCM. The FANCI expression provides a foundation for predicting the immune status and treatment of SKCM.
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Affiliation(s)
- Zhenguo Cai
- Department of Dermatology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Dermatology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yanjuan Duan
- Department of Dermatology, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wen Li
- Department of Dermatology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhuohang Liu
- Department of Neurology, Minhang Hospital, Fudan University/Central Hospital of Minhang District, Shanghai, China
| | - Zijun Gong
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Sheng Hong
- Department of Dermatology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xu He
- Department of Dermatology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xinyang Xuanyuan
- Department of Dermatology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Youdong Chen
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xinling Bi
- Department of Dermatology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Wuqing Wang
- Department of Dermatology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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8
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Shi X, Cui D, Wei Z, Hong S. The influence of sulphide on the ultrasonic cavitation erosion-corrosion behaviors of HVOF-sprayed WC-Cr 3C 2-Ni coating. Ultrason Sonochem 2023; 100:106629. [PMID: 37813045 PMCID: PMC10569981 DOI: 10.1016/j.ultsonch.2023.106629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/08/2023] [Accepted: 10/02/2023] [Indexed: 10/11/2023]
Abstract
The present study emphasizes the role of sulphide on ultrasonic cavitation erosion-corrosion (UCE-C) behaviors of HVOF-sprayed WC-Cr3C2-Ni coating in 3.5 wt% NaCl solution with different sulphide concentrations. The results indicated that the ultrasonic cavitation erosion (UCE) resistance of the coating decreased significantly with increasing sulphide concentration. The coating reacted with oxygen and anion those transferred by UCE, and the cavitation impact force led to the lack of support for tungsten carbide particles, which resulted in the reduction in the mass of the coating. There were two main factors those affected the UCE-C mechanism, in which the passivation film helped to reduce the mass loss of the coating, while the impact force caused by cavitation destroyed the passivation film, led to the accelerated anion diffusion and ultimately accelerated the mass loss of the coating. Mechanical erosion dominated the UCE-C of the coating in all tested solutions.
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Affiliation(s)
- Xinlu Shi
- College of Mechanics and Materials, Hohai University, 8 Focheng West Road, Nanjing 211100, PR China
| | - Dandong Cui
- College of Mechanics and Materials, Hohai University, 8 Focheng West Road, Nanjing 211100, PR China
| | - Zheng Wei
- College of Mechanics and Materials, Hohai University, 8 Focheng West Road, Nanjing 211100, PR China
| | - Sheng Hong
- College of Mechanics and Materials, Hohai University, 8 Focheng West Road, Nanjing 211100, PR China.
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9
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Lu X, Li L, Wu N, Chen W, Hong S, Xu M, Ding Y, Gao Y. BMP9 functions as a negative regulator in the myogenic differentiation of primary mouse myoblasts. Biosci Biotechnol Biochem 2023; 87:1255-1264. [PMID: 37553201 DOI: 10.1093/bbb/zbad104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 07/26/2023] [Indexed: 08/10/2023]
Abstract
BMP9, a member of the TGF-β superfamily, reveals the great translational promise for it has been shown to have the strong effect of osteogenic activity in vitro and in vivo. However, the implantation of certain BMPs (bone morphogenetic proteins) into muscular tissues induces ectopic bone formation. BMPs induce osteoblastic differentiation in skeletal muscle, suggesting that myogenic stem cells, such as myoblasts, are the potential progenitors of osteoblasts during heterotopic bone differentiation. Here, we investigate the role of BMP9 during primary mouse myoblasts differentiation. We found BMP9 enhanced cell proliferation and reduced myogenic differentiation of primary mouse myoblasts. In addition, adenovirus-mediated overexpression of BMP9 delayed muscle regeneration after BaCl2-induced injury. ALK1 knockdown reversed the inhibition of myoblast differentiation induced by BMP9. Our data indicate that BMP9 inhibits myogenic differentiation in primary mouse myoblasts and delays skeletal muscle regeneration after injury.
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Affiliation(s)
- Xiya Lu
- Department of Dermatology, Shanghai Skin Disease Hospital, Shanghai, China
| | - Liang Li
- Department of Dermatologic Surgery, Shanghai Skin Disease Hospital, Shanghai, China
| | - Nanhui Wu
- Department of Dermatopathology, Shanghai Skin Disease Hospital, Shanghai, China
| | - Wenjuan Chen
- Department of Dermatology, Shanghai Skin Disease Hospital, Shanghai, China
| | - Sheng Hong
- Department of Dermatology, Changhai Hospital, Shanghai, China
| | - Mingyuan Xu
- Department of Dermatopathology, Shanghai Skin Disease Hospital, Shanghai, China
| | - Yangfeng Ding
- Department of Dermatology, Shanghai Skin Disease Hospital, Shanghai, China
| | - Yunlu Gao
- Department of Dermatology, Shanghai Skin Disease Hospital, Shanghai, China
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10
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Chen L, Wang X, Chen M, Sun Q, Chen Y, Zhang X, Hong R, Xu Y, Guan J, Hong S, Cao D, Sun T, Li X, Chen L, Diwu J. Self-Aggregated Nanoscale Metal-Organic Framework for Targeted Pulmonary Decorporation of Uranium. Adv Healthc Mater 2023; 12:e2300510. [PMID: 37377120 DOI: 10.1002/adhm.202300510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/17/2023] [Indexed: 06/29/2023]
Abstract
The limited availability of effective agents for removing actinides from the lungs significantly restricts the effectiveness of medical treatments for nuclear emergencies. Inhalation is the primary route of internal contamination in 44.3% of actinide-related accidents, leading to the accumulation of radionuclides in the lungs and resulting in infections and potential tumor formation (tumorigenesis). This study focuses on the synthesis of a nanometal-organic framework (nMOF) material called ZIF-71-COOH, which is achieved by post-synthetic carboxyl functionalization of ZIF-71. The material demonstrates high and selective adsorption of uranyl, while also exhibiting increased particle size (≈2100 nm) when it aggregates in the blood, enabling passive targeting of the lungs through mechanical filtration. This unique property facilitates the rapid enrichment and selective recognition of uranyl, making nano ZIF-71-COOH highly effective in removing uranyl from the lungs. The findings of this study highlight the potential of self-aggregated nMOFs as a promising drug delivery system for targeted uranium decorporation in the lungs.
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Affiliation(s)
- Lei Chen
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Xiaomei Wang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Mengping Chen
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Qiwen Sun
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Yemeng Chen
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Xiaojie Zhang
- Department of Experimental Center, Medical College of Soochow University, Suzhou, 215123, China
| | - Rui Hong
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Yigong Xu
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Jingwen Guan
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Sheng Hong
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Dehan Cao
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Tingfeng Sun
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Ximeng Li
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Lanhua Chen
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
| | - Juan Diwu
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou, 215123, China
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11
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Ward MC, Atlas JL, Carrizosa DR, Milas ZL, Brickman DS, Frenkel CH, Hong S, Heinzerling JH, Prabhu RS, Moeller BJ. Weekly vs. Bolus Cisplatin Concurrent with Definitive Radiotherapy for Squamous Carcinoma of the Head and Neck: A Systematic Review and Network Meta-Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e632-e633. [PMID: 37785889 DOI: 10.1016/j.ijrobp.2023.06.2031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The optimal schedule for cisplatin delivered concurrently with definitive radiation for squamous carcinoma of the head and neck remains controversial. Randomized data in the postoperative setting is mixed, and definitive studies are ongoing. Meanwhile, multiple trials have already compared cetuximab to cisplatin in the definitive setting. Across these trials, the cetuximab dosing was identical, but cisplatin dosing was variable and can be categorized as weekly (40 mg/m2 q1 week) or bolus (100 mg/m2 q3 weeks). We indirectly compared these two cisplatin schedules by performing a network meta-analysis of cetuximab trials. MATERIALS/METHODS We performed a PRISMA-concordant systematic review to identify randomized controlled trials comparing cisplatin to cetuximab for patients with non-metastatic squamous carcinoma of the head and neck treated with definitive radiation therapy. Trials of primary surgery, incorporating induction therapy, or mixing other therapeutics were excluded. The analysis was pre-registered with the Open Science Foundation. Individual patient survival data was extracted from the published overall survival curves using a digitizer, and outcomes were validated against published point-estimates and hazard ratios. A random effects Cox regression was used to perform the individual-patient analysis using a one-step approach under a frequentist framework. Random effects were applied to model heterogeneity in the baseline hazard function and treatment effect. Models were adjusted by HPV and smoking status, which were trial-level covariates. Alternative endpoints (DFS, LRF, DM, etc.) were analyzed qualitatively. IRB approval was not required. RESULTS Five randomized trials were identified, including 1,678 patients. Bolus cisplatin was delivered to 572 patients in 2 trials, and weekly to 271 in 3 trials. The risk of bias was low. Relative to cetuximab, both bolus and weekly cisplatin reduced the risk of death (adjusted HR 0.63, 95% CI 0.46-0.87, p = 0.004 & HR 0.56, 95% CI 0.37-0.86, p = 0.008 respectively). No interaction was identified between regimen and HPV or smoking status. Between-study heterogeneity (δ2) was 0.148 and treatment effect heterogeneity (τ2) was small (<0.0002). There was no statistical difference in OS between bolus vs. weekly regimens (weekly vs. bolus HR 0.90, 95% CI 0.53-1.52, p = 0.345). This Cox model therefore suggested that on average, the absolute difference in 5-year OS is <1.5% between the two regimens, which was not statistically significant. Secondary endpoints and toxicity were not obviously different by regimen, qualitatively. CONCLUSION Using cetuximab as a common reference, there was no significant difference in survival between weekly and bolus cisplatin schedules.
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Affiliation(s)
- M C Ward
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - J L Atlas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D R Carrizosa
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - Z L Milas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D S Brickman
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - C H Frenkel
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - S Hong
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - J H Heinzerling
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - R S Prabhu
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - B J Moeller
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
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12
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Hoke JC, Ippoliti M, Rosenberg E, Abanin D, Acharya R, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Eppens D, Erickson C, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Miao KC, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Mi X, Khemani V, Roushan P. Measurement-induced entanglement and teleportation on a noisy quantum processor. Nature 2023; 622:481-486. [PMID: 37853150 PMCID: PMC10584681 DOI: 10.1038/s41586-023-06505-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/01/2023] [Indexed: 10/20/2023]
Abstract
Measurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the 'arrow of time' that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time3-10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11-13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15-17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
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Sud S, Poellmann M, Garg V, King T, Casey DL, Wang AZ, Hong S, Weiner AA. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Localized Lung Cancer Treated with Radiotherapy or Chemoradiotherapy with Definitive Intent. Int J Radiat Oncol Biol Phys 2023; 117:e60. [PMID: 37785811 DOI: 10.1016/j.ijrobp.2023.06.778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To characterize circulating tumor cell (CTC) kinetics in response to definitive therapy in patients with local or locoregional lung cancer and identify CTC kinetic profiles associated with favorable disease response versus progression. MATERIALS/METHODS In this single-institution prospective correlative biomarker study, we enrolled patients receiving definitive intent radiotherapy (RT) or chemoradiotherapy for non-metastatic lung cancer. Blood specimens were collected prior to RT (baseline), during RT and at follow up visits up to 24 months post RT. Subsequent lines of therapy were administered per standard of care. CTCs were captured and enumerated using a previously reported nanotechnology-based assay functionalized with aEpCAM, aHER-2, and aEGFR to facilitate biomimetic cell rolling and dendrimer-mediated multivalent binding. Disease status was assessed per RECIST 1.1 criteria. CTC kinetics and absolute values were analyzed to identify patterns associated with disease control versus progression. RESULTS We enrolled 24 patients with median follow up of 8 months corresponding to 114 CTC measurements. Seven patients (30%) had biopsy proven disease, while 17 (70%) were diagnosed based on clinical and radiographic features alone. Nineteen patients (79%) received stereotactic body radiation therapy. Median baseline CTC count was 12.6 CTCs/ml (range 0-290) and post RT decreased to median 4 CTCs/ml (0-42.7). For 95% of patients, a favorable kinetic profile (defined as stable CTC count, decreased CTC count or <24 CTCs/ml corresponding to the 80th percentile) during radiotherapy or at the time of first follow up corresponded to local control of the irradiated lesion. Five patients (20%) experienced disease progression within the follow up period. In the two patients with local progression of the irradiated lesion, the CTC count rose >10 fold prior to or at the time of radiographic detection of progression. In the three patients with systemic progression, CTC count rose 1.46-5.8-fold at the time of progression. Notably, four of the five patients with disease progression did not have initial biopsy confirmation of disease but did experience a CTC elevation at the time of progression. CONCLUSION Our data suggests CTCs may serve as a biomarker for response to therapy in patients being treated with RT with definitive intent for early stage or locally advanced lung cancer. This finding is of importance given important limitations in obtaining pathologic confirmation of disease in select patients and challenges distinguishing disease progression versus benign post radiotherapy radiographic changes. Further studies are needed to characterize the predictive and prognostic value of circulating biomarker levels and kinetics in lung cancer.
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Affiliation(s)
- S Sud
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - M Poellmann
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - V Garg
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - T King
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - D L Casey
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - A Z Wang
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC; UT Southwestern Department of Radiation Oncology, Dallas, TX
| | - S Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - A A Weiner
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
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14
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Gu Q, Zhao X, Guo J, Jin Q, Wang T, Xu W, Li L, Zhang J, Zhang W, Hong S, Zhang F, Hou B, Zhou X. The splicing isoform Foxp3Δ2 differentially regulates tTreg and pTreg homeostasis. Cell Rep 2023; 42:112877. [PMID: 37498744 DOI: 10.1016/j.celrep.2023.112877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/09/2023] [Accepted: 07/12/2023] [Indexed: 07/29/2023] Open
Abstract
Foxp3 is the master transcription factor for regulatory T cells (Tregs). Alternative splicing of human Foxp3 results in the expression of two isoforms: the full length and an exon 2-deleted protein. Here, AlphaFold2 predictions and in vitro experiments demonstrate that the N-terminal domain of Foxp3 inhibits DNA binding by moving toward the C terminus and that this movement is mediated by exon 2. Consequently, we find that Foxp3Δ2-bearing thymus-derived Tregs (tTregs) in the peripheral lymphoid organ are less sensitive to T cell receptor (TCR) stimulation due to the enhanced binding of Foxp3Δ2 to the Batf promoter and are hyporesponsive to interleukin-2 (IL-2). In contrast, among RORγt+ peripherally induced Tregs (pTregs) in the large intestine, Foxp3Δ2 pTregs express many more RORγt-related genes, conferring a competitive advantage. Together, our results reveal that alternative splicing of exon 2 generates an active form of Foxp3, which plays a differential role in regulating tTreg and pTreg homeostasis.
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Affiliation(s)
- Qianchong Gu
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China; Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing 100049, China
| | - Xiufeng Zhao
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China; Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing 100049, China
| | - Jie Guo
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China
| | - Qiuzhu Jin
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China; Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing 100049, China
| | - Ting Wang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China; Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing 100049, China
| | - Wei Xu
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China; Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing 100049, China
| | - Liping Li
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China; Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing 100049, China
| | - Jianhua Zhang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China
| | - Wei Zhang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China
| | - Sheng Hong
- Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing 100101, China
| | - Fuping Zhang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China; Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing 100049, China
| | - Baidong Hou
- Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing 100101, China
| | - Xuyu Zhou
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Science (CAS), Beijing 100101, China; Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing 100049, China.
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15
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Hong S, Peng Z, Wu M, Nie Y, Yi Y, Cai H, Zhang XZ. Human-Hair-Derived Natural Particles as Multifunctional Sunscreen for Effective UV Protection. ACS Nano 2023; 17:14943-14953. [PMID: 37485891 DOI: 10.1021/acsnano.3c03504] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Excessive ultraviolet (UV) radiation can lead to a series of skin problems. Although commercial sunscreens can protect skin from UV-induced damage to an extent, the side effects caused by such products are still worrisome. Here, inspired by the natural photoprotection effect of human hair, we extracted the multifunctional particles from human hair as sunscreens for UV protection. Both in vitro and in vivo results indicate that hair-derived particles (HDPs) could effectively protect skin from UV radiation. Besides, HDPs retain the antioxidant capability of melanin in hair, which avoids UV-induced oxidative damage. In addition, the unique shape of HDPs can prevent them from penetrating into the skin, thus avoiding potential toxicity. Moreover, owing to their mesoporous structure, the particles can also be used as drug carriers. With the loading of octocrylene, the particles are more effective in blocking UV radiation. This study provides an ingenious tactic for the design and development of sunscreens from a natural substance.
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Affiliation(s)
- Sheng Hong
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, People's Republic of China
| | - Zhangwen Peng
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, People's Republic of China
| | - Meiying Wu
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, People's Republic of China
| | - Yichu Nie
- Translational Medicine Research Institute, First People's Hospital of Foshan, Foshan 528000, People's Republic of China
| | - Yunfei Yi
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, People's Republic of China
| | - Hui Cai
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, People's Republic of China
| | - Xian-Zheng Zhang
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan 430072, People's Republic of China
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Smitherman EA, Chahine RA, Beukelman T, Lewandowski LB, Rahman AKMF, Wenderfer SE, Curtis JR, Hersh AO, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar‐Smiley F, Barillas‐Arias L, Basiaga M, Baszis K, Becker M, Bell‐Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang‐Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel‐Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie‐Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui‐Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein‐Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PM, McGuire S, McHale I, McMonagle A, McMullen‐Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O'Brien B, O'Brien T, Okeke O, Oliver M, Olson J, O'Neil K, Onel K, Orandi A, Orlando M, Osei‐Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan‐Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas‐Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth‐Wojcicki E, Rouster – Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert‐Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner‐Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Childhood-Onset Lupus Nephritis in the Childhood Arthritis and Rheumatology Research Alliance Registry: Short-Term Kidney Status and Variation in Care. Arthritis Care Res (Hoboken) 2023; 75:1553-1562. [PMID: 36775844 PMCID: PMC10500561 DOI: 10.1002/acr.25002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 07/14/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The goal was to characterize short-term kidney status and describe variation in early care utilization in a multicenter cohort of patients with childhood-onset systemic lupus erythematosus (cSLE) and nephritis. METHODS We analyzed previously collected prospective data from North American patients with cSLE with kidney biopsy-proven nephritis enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry from March 2017 through December 2019. We determined the proportion of patients with abnormal kidney status at the most recent registry visit and applied generalized linear mixed models to identify associated factors. We also calculated frequency of medication use, both during induction and ever recorded. RESULTS We identified 222 patients with kidney biopsy-proven nephritis, with 64% class III/IV nephritis on initial biopsy. At the most recent registry visit at median (interquartile range) of 17 (8-29) months from initial kidney biopsy, 58 of 106 patients (55%) with available data had abnormal kidney status. This finding was associated with male sex (odds ratio [OR] 3.88, 95% confidence interval [95% CI] 1.21-12.46) and age at cSLE diagnosis (OR 1.23, 95% CI 1.01-1.49). Patients with class IV nephritis were more likely than class III to receive cyclophosphamide and rituximab during induction. There was substantial variation in mycophenolate, cyclophosphamide, and rituximab ever use patterns across rheumatology centers. CONCLUSION In this cohort with predominately class III/IV nephritis, male sex and older age at cSLE diagnosis were associated with abnormal short-term kidney status. We also observed substantial variation in contemporary medication use for pediatric lupus nephritis between pediatric rheumatology centers. Additional studies are needed to better understand the impact of this variation on long-term kidney outcomes.
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17
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Andersen TI, Lensky YD, Kechedzhi K, Drozdov IK, Bengtsson A, Hong S, Morvan A, Mi X, Opremcak A, Acharya R, Allen R, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Babbush R, Bacon D, Bardin JC, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hilton J, Hoffmann MR, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Lucero E, Malone FD, Martin O, McClean JR, McCourt T, McEwen M, Miao KC, Mieszala A, Mohseni M, Montazeri S, Mount E, Movassagh R, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Kim EA, Aleiner I, Roushan P. Non-Abelian braiding of graph vertices in a superconducting processor. Nature 2023; 618:264-269. [PMID: 37169834 DOI: 10.1038/s41586-023-05954-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023]
Abstract
Indistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date-including fermions, bosons and Abelian anyons-this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4-8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9-22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons9,10, we implement a generalized stabilizer code and unitary protocol23 to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.
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Zhou Y, Li Q, Wu Y, Li X, Zhou Y, Wang Z, Liang H, Ding F, Hong S, Steinmetz NF, Cai H. Molecularly Stimuli-Responsive Self-Assembled Peptide Nanoparticles for Targeted Imaging and Therapy. ACS Nano 2023; 17:8004-8025. [PMID: 37079378 DOI: 10.1021/acsnano.3c01452] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Self-assembly has emerged as an extensively used method for constructing biomaterials with sizes ranging from nanometers to micrometers. Peptides have been extensively investigated for self-assembly. They are widely applied owing to their desirable biocompatibility, biodegradability, and tunable architecture. The development of peptide-based nanoparticles often requires complex synthetic processes involving chemical modification and supramolecular self-assembly. Stimuli-responsive peptide nanoparticles, also termed "smart" nanoparticles, capable of conformational and chemical changes in response to stimuli, have emerged as a class of promising materials. These smart nanoparticles find a diverse range of biomedical applications, including drug delivery, diagnostics, and biosensors. Stimuli-responsive systems include external stimuli (such as light, temperature, ultrasound, and magnetic fields) and internal stimuli (such as pH, redox environment, salt concentration, and biomarkers), facilitating the generation of a library of self-assembled biomaterials for biomedical imaging and therapy. Thus, in this review, we mainly focus on peptide-based nanoparticles built by self-assembly strategy and systematically discuss their mechanisms in response to various stimuli. Furthermore, we summarize the diverse range of biomedical applications of peptide-based nanomaterials, including diagnosis and therapy, to demonstrate their potential for medical translation.
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Affiliation(s)
- Yang Zhou
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, 66 Gongchang Road, Guangming District, Shenzhen 518107, China
| | - Qianqian Li
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, 66 Gongchang Road, Guangming District, Shenzhen 518107, China
- Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Ye Wu
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, 66 Gongchang Road, Guangming District, Shenzhen 518107, China
| | - Xinyu Li
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, 66 Gongchang Road, Guangming District, Shenzhen 518107, China
| | - Ya Zhou
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, 66 Gongchang Road, Guangming District, Shenzhen 518107, China
| | - Zhu Wang
- Department of Urology, Affiliated People's Hospital of Longhua Shenzhen, Southern Medical University, 38 Jinglong Jianshe Road, Shenzhen, Guangdong 518109, PR China
| | - Hui Liang
- Department of Urology, Affiliated People's Hospital of Longhua Shenzhen, Southern Medical University, 38 Jinglong Jianshe Road, Shenzhen, Guangdong 518109, PR China
| | - Feiqing Ding
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, 66 Gongchang Road, Guangming District, Shenzhen 518107, China
| | - Sheng Hong
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, 66 Gongchang Road, Guangming District, Shenzhen 518107, China
| | - Nicole F Steinmetz
- Department of NanoEngineering, Department of Biongineering, Department of Radiology, Moores Cancer Center, Center for Nano-ImmunoEngineering, Center for Engineering in Cancer, Institute for Materials Discovery and Design, University of California, San Diego, La Jolla, California 92093, United States
| | - Hui Cai
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, 66 Gongchang Road, Guangming District, Shenzhen 518107, China
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19
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Yin L, Zhou Y, Hong S, Ding F, Cai H. Strategies for synthesizing and enhancing the immune response of cancer vaccines based on MUC1 glycopeptide antigens. Chembiochem 2023; 24:e202200805. [PMID: 36825431 DOI: 10.1002/cbic.202200805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 02/25/2023]
Abstract
Cancer vaccines are based on a vaccinology strategy whereby the patient's immune system is harnessed to induce a specific immune response to kill cancer cells and comprises two categories: prophylactic and therapeutic. Glycoprotein mucin 1 (MUC1), which is overexpressed and poorly glycosylated on cancer cells, is one of the most promising candidates for the development of new cancer vaccines. However, it should be noted that mucin-like glycopeptides are poorly immunogenic and unable to elicit effective and long-lasting immune responses. Therefore, MUC1-derived tumor antigens need to be conjugated with immune activators. This review focuses on the synthesis of MUC1 glycopeptides, provides an overview of recently advanced designs of vaccines based on MUC1, and compares the advantages and disadvantages of the various strategies devised to date.
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Affiliation(s)
- Lixin Yin
- Sun Yat-Sen University, School of Pharmaceutical Sciences (Shenzhen), 66 Gongchang Lu, Guangming District, 518107, Shenzhen, CHINA
| | - Yang Zhou
- Sun Yat-Sen University, School of Pharmaceutical Sciences (Shenzhen), 66 Gongchang Lu, Guangming District, 518107, Shenzhen, CHINA
| | - Sheng Hong
- Sun Yat-Sen University, School of Pharmaceutical Sciences (Shenzhen), 66 Gongchang Lu, Guangming District, 518107, Shenzhen, CHINA
| | - Feiqing Ding
- Sun Yat-sen University, School of Pharmaceutical Sciences (Shenzhen), 66 Gongchang Lu, Guangming District, 518107, Shenzhen, CHINA
| | - Hui Cai
- Sun Yat-Sen University, Nanoengineering, 66 Gongchang Lu, Guangming District, 518107, Shenzhen, CHINA
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20
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Akhtar M, Bonus F, Lebrun-Gallagher FR, Johnson NI, Siegele-Brown M, Hong S, Hile SJ, Kulmiya SA, Weidt S, Hensinger WK. A high-fidelity quantum matter-link between ion-trap microchip modules. Nat Commun 2023; 14:531. [PMID: 36754957 PMCID: PMC9908934 DOI: 10.1038/s41467-022-35285-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 11/25/2022] [Indexed: 02/10/2023] Open
Abstract
System scalability is fundamental for large-scale quantum computers (QCs) and is being pursued over a variety of hardware platforms. For QCs based on trapped ions, architectures such as the quantum charge-coupled device (QCCD) are used to scale the number of qubits on a single device. However, the number of ions that can be hosted on a single quantum computing module is limited by the size of the chip being used. Therefore, a modular approach is of critical importance and requires quantum connections between individual modules. Here, we present the demonstration of a quantum matter-link in which ion qubits are transferred between adjacent QC modules. Ion transport between adjacent modules is realised at a rate of 2424 s-1 and with an infidelity associated with ion loss during transport below 7 × 10-8. Furthermore, we show that the link does not measurably impact the phase coherence of the qubit. The quantum matter-link constitutes a practical mechanism for the interconnection of QCCD devices. Our work will facilitate the implementation of modular QCs capable of fault-tolerant utility-scale quantum computation.
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Affiliation(s)
- M. Akhtar
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - F. Bonus
- Universal Quantum Ltd, Brighton, BN1 6SB UK ,grid.83440.3b0000000121901201Department of Physics and Astronomy, University College London, London, WC1E 6BT UK
| | - F. R. Lebrun-Gallagher
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - N. I. Johnson
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - M. Siegele-Brown
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. Hong
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. J. Hile
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. A. Kulmiya
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,grid.5337.20000 0004 1936 7603Quantum Engineering Centre for Doctoral Training, University of Bristol, Bristol, BS8 1TH UK
| | - S. Weidt
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - W. K. Hensinger
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
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21
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Bang S, Kwon H, Yoon C, Rhew S, Shin D, Moon H, Cho H, Ha U, Lee J, Hong S. Development and validation of a machine learning-based CT radiomics model for differentiation of benign and malignant solid renal tumors. Eur Urol 2023. [DOI: 10.1016/s0302-2838(23)01313-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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22
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Choi H, Pyo KH, Lim S, Cho B, Hong S. PP223 Single-cell RNA sequencing in metastatic lung cancer uncovers the efficacy of PD-1/PD-L1 inhibitors on immune cell population. ESMO Open 2022. [DOI: 10.1016/j.esmoop.2022.100719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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23
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Knight, Imwattana K, Lim SC, Hong S, Putsathit P, Collins DA, Riley TV. WS1.6: GENOMIC EPIDEMIOLOGY OF RECURRENT CLOSTRIDIOIDES DIFFICILE INFECTION IN WESTERN AUSTRALIA. J Glob Antimicrob Resist 2022. [DOI: 10.1016/s2213-7165(22)00274-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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24
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Morvan A, Andersen TI, Mi X, Neill C, Petukhov A, Kechedzhi K, Abanin DA, Michailidis A, Acharya R, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores Burgos L, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Grajales Dau A, Gross JA, Habegger S, Hamilton MC, Harrigan MP, Harrington SD, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Malone F, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Nersisyan A, Newman M, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Olenewa R, Opremcak A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shvarts V, Skruzny J, Smith WC, Strain D, Sterling G, Su Y, Szalay M, Torres A, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Xing C, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Aleiner I, Ioffe LB, Roushan P. Formation of robust bound states of interacting microwave photons. Nature 2022; 612:240-245. [PMID: 36477133 PMCID: PMC9729104 DOI: 10.1038/s41586-022-05348-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/14/2022] [Indexed: 12/12/2022]
Abstract
Systems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2-9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.
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Affiliation(s)
- A Morvan
- Google Research, Mountain View, CA, USA
| | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J Basso
- Google Research, Mountain View, CA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | - D Eppens
- Google Research, Mountain View, CA, USA
| | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Computation and Communication Technology, Centre for Quantum Software and Information, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Y Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - F Malone
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | - K C Miao
- Google Research, Mountain View, CA, USA
| | - M Mohseni
- Google Research, Mountain View, CA, USA
| | | | - E Mount
- Google Research, Mountain View, CA, USA
| | | | - O Naaman
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - R Olenewa
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | | | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - D Strain
- Google Research, Mountain View, CA, USA
| | | | - Y Su
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - I Aleiner
- Google Research, Mountain View, CA, USA.
| | - L B Ioffe
- Google Research, Mountain View, CA, USA.
| | - P Roushan
- Google Research, Mountain View, CA, USA.
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Hahn T, Daymont C, Beukelman T, Groh B, Hays K, Bingham CA, Scalzi L, Abel N, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar-Smiley F, Barillas-Arias L, Basiaga M, Baszis K, Becker M, Bell-Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang-Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel-Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie-Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui-Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein-Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PMC, McGuire S, McHale I, McMonagle A, McMullen-Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O’Brien B, O’Brien T, Okeke O, Oliver M, Olson J, O’Neil K, Onel K, Orandi A, Orlando M, Osei-Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan-Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas-Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth-Wojcicki E, Rouster-Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert-Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner-Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Intraarticular steroids as DMARD-sparing agents for juvenile idiopathic arthritis flares: Analysis of the Childhood Arthritis and Rheumatology Research Alliance Registry. Pediatr Rheumatol Online J 2022; 20:107. [PMID: 36434731 PMCID: PMC9701017 DOI: 10.1186/s12969-022-00770-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/08/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Children with juvenile idiopathic arthritis (JIA) who achieve a drug free remission often experience a flare of their disease requiring either intraarticular steroids (IAS) or systemic treatment with disease modifying anti-rheumatic drugs (DMARDs). IAS offer an opportunity to recapture disease control and avoid exposure to side effects from systemic immunosuppression. We examined a cohort of patients treated with IAS after drug free remission and report the probability of restarting systemic treatment within 12 months. METHODS We analyzed a cohort of patients from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry who received IAS for a flare after a period of drug free remission. Historical factors and clinical characteristics and of the patients including data obtained at the time of treatment were analyzed. RESULTS We identified 46 patients who met the inclusion criteria. Of those with follow up data available 49% had restarted systemic treatment 6 months after IAS injection and 70% had restarted systemic treatment at 12 months. The proportion of patients with prior use of a biologic DMARD was the only factor that differed between patients who restarted systemic treatment those who did not, both at 6 months (79% vs 35%, p < 0.01) and 12 months (81% vs 33%, p < 0.05). CONCLUSION While IAS are an option for all patients who flare after drug free remission, it may not prevent the need to restart systemic treatment. Prior use of a biologic DMARD may predict lack of success for IAS. Those who previously received methotrexate only, on the other hand, are excellent candidates for IAS.
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Affiliation(s)
- Timothy Hahn
- Department of Pediatrics, Penn State Children's Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA, 17033-0855, USA.
| | - Carrie Daymont
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Timothy Beukelman
- grid.265892.20000000106344187Department of Pediatrics, University of Alabama at Birmingham, CPPN G10, 1600 7th Ave South, Birmingham, AL 35233 USA
| | - Brandt Groh
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | | | - Catherine April Bingham
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Lisabeth Scalzi
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
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He LN, Fu S, Ma H, Chen C, Zhang X, Li H, Du W, Chen T, Jiang Y, Wang Y, Wang Y, Zhou Y, Lin Z, Yang Y, Huang Y, Zhao H, Fang W, Zhang H, Zhang L, Hong S. Early on-treatment tumor growth rate (EOT-TGR) determines treatment outcomes of advanced non-small-cell lung cancer patients treated with programmed cell death protein 1 axis inhibitor. ESMO Open 2022; 7:100630. [PMID: 36442353 PMCID: PMC9808481 DOI: 10.1016/j.esmoop.2022.100630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/02/2022] [Accepted: 10/09/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Tumor growth rate (TGR), denoted as percentage change in tumor size per month, is a well-established indicator of tumor growth kinetics. The predictive value of early on-treatment TGR (EOT-TGR) for immunotherapy remains unclear. We sought to establish and validate the association of EOT-TGR with treatment outcomes in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 (programmed cell death protein 1/programmed death-ligand 1) therapy. PATIENTS AND METHODS This bicenter retrospective cohort study included a training cohort, a contemporaneously treated internal validation cohort, and an external validation cohort. Computed tomography images were retrieved to calculate EOT-TGR, denoted as tumor burden change per month during a period between baseline and the first imaging evaluation after immunotherapy. Kaplan-Meier methodology and Cox regression analysis were conducted for survival analyses. RESULTS In the pooled cohort (n = 172), 125 patients (72.7%) were males; median age at diagnosis was 58 (range 28-79) years. Based on the training cohort, we determined the optimal cut-off value for EOT-TGR as 10.4%/month. Higher EOT-TGR was significantly associated with inferior overall survival [OS; hazard ratio (HR) 2.93, 95% confidence interval (CI) 1.47-5.83; P = 0.002], worse progression-free survival (PFS; HR 2.44, 95% CI 1.46-4.08; P = 0.001), and lower objective response rate (3.3% versus 20.9%; P = 0.040) and durable clinical benefit rate (6.7% versus 41.9%; P = 0.001). Results were reproducible in the two validation cohorts for OS and PFS. Among 43 patients who had a best response of progressive disease in the training cohort, those with high EOT-TGR had worse OS (HR 2.64; P = 0.041) and were more likely to progress due to target lesions at the first tumor evaluation (85.2% versus 0.0%; P <0.001). CONCLUSIONS Higher EOT-TGR was associated with inferior OS and immunotherapeutic response in patients with aNSCLC undergoing anti-PD-1/PD-L1 therapy. This easy-to-calculate radiologic biomarker may help evaluate the abilities of immunotherapy to prolong survival and assist in tailoring patients' management. TRIAL REGISTRATION ClinicalTrials.govNCT04722406; https://clinicaltrials.gov/ct2/show/NCT04722406.
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Affiliation(s)
- L.-N. He
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - S. Fu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation of Sun Yat-Sen University; Department of Cellular & Molecular Diagnostics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - H. Ma
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
| | - C. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Departments of Radiation Oncology, Guangzhou, China
| | - X. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Li
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Du
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - T. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Jiang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Endoscopy, Guangzhou, China
| | - Y. Zhou
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,VIP Region, Guangzhou, China
| | - Z. Lin
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Yang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Huang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Fang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhang
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China,Prof. Haibo Zhang, Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, 111 Dade Road, Guangzhou, Guangdong 510120, People’s Republic of China. Tel: +86-20-81887233-34830
| | - L. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Prof. Li Zhang, MD, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87343458
| | - S. Hong
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Correspondence to: Prof. Shaodong Hong, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87342480
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Mi X, Sonner M, Niu MY, Lee KW, Foxen B, Acharya R, Aleiner I, Andersen TI, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Brill L, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Debroy DM, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores L, Forati E, Fowler AG, Giang W, Gidney C, Gilboa D, Giustina M, Dau AG, Gross JA, Habegger S, Harrigan MP, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Kafri D, Kechedzhi K, Khattar T, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Lee J, Laws L, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Morvan A, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Newman M, O’Brien TE, Opremcak A, Petukhov A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schuster C, Shearn MJ, Shvarts V, Strain D, Su Y, Szalay M, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Abanin DA, Roushan P. Noise-resilient edge modes on a chain of superconducting qubits. Science 2022; 378:785-790. [DOI: 10.1126/science.abq5769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the one-dimensional kicked Ising model, which exhibits nonlocal Majorana edge modes (MEMs) with
ℤ
2
parity symmetry. We find that any multiqubit Pauli operator overlapping with the MEMs exhibits a uniform late-time decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This characteristic allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Furthermore, the MEMs are found to be resilient against certain symmetry-breaking noise owing to a prethermalization mechanism. Our work elucidates the complex interplay between noise and symmetry-protected edge modes in a solid-state environment.
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Affiliation(s)
- X. Mi
- Google Research, Mountain View, CA, USA
| | - M. Sonner
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - M. Y. Niu
- Google Research, Mountain View, CA, USA
| | - K. W. Lee
- Google Research, Mountain View, CA, USA
| | - B. Foxen
- Google Research, Mountain View, CA, USA
| | | | | | | | - F. Arute
- Google Research, Mountain View, CA, USA
| | - K. Arya
- Google Research, Mountain View, CA, USA
| | - A. Asfaw
- Google Research, Mountain View, CA, USA
| | | | - J. C. Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J. Basso
- Google Research, Mountain View, CA, USA
| | | | | | | | - L. Brill
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Chen
- Google Research, Mountain View, CA, USA
| | - B. Chiaro
- Google Research, Mountain View, CA, USA
| | | | - P. Conner
- Google Research, Mountain View, CA, USA
| | | | | | | | - S. Demura
- Google Research, Mountain View, CA, USA
| | | | - D. Eppens
- Google Research, Mountain View, CA, USA
| | | | - L. Faoro
- Google Research, Mountain View, CA, USA
| | - E. Farhi
- Google Research, Mountain View, CA, USA
| | - R. Fatemi
- Google Research, Mountain View, CA, USA
| | - L. Flores
- Google Research, Mountain View, CA, USA
| | - E. Forati
- Google Research, Mountain View, CA, USA
| | | | - W. Giang
- Google Research, Mountain View, CA, USA
| | - C. Gidney
- Google Research, Mountain View, CA, USA
| | - D. Gilboa
- Google Research, Mountain View, CA, USA
| | | | - A. G. Dau
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - S. Hong
- Google Research, Mountain View, CA, USA
| | - T. Huang
- Google Research, Mountain View, CA, USA
| | - A. Huff
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Jiang
- Google Research, Mountain View, CA, USA
| | - C. Jones
- Google Research, Mountain View, CA, USA
| | - D. Kafri
- Google Research, Mountain View, CA, USA
| | | | | | - S. Kim
- Google Research, Mountain View, CA, USA
| | - A. Y. Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | | | - A. N. Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P. Laptev
- Google Research, Mountain View, CA, USA
| | - K.-M. Lau
- Google Research, Mountain View, CA, USA
| | - J. Lee
- Google Research, Mountain View, CA, USA
| | - L. Laws
- Google Research, Mountain View, CA, USA
| | - W. Liu
- Google Research, Mountain View, CA, USA
| | | | - O. Martin
- Google Research, Mountain View, CA, USA
| | | | - M. McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | | | | | | | - A. Morvan
- Google Research, Mountain View, CA, USA
| | - E. Mount
- Google Research, Mountain View, CA, USA
| | | | - O. Naaman
- Google Research, Mountain View, CA, USA
| | - M. Neeley
- Google Research, Mountain View, CA, USA
| | - C. Neill
- Google Research, Mountain View, CA, USA
| | - M. Newman
- Google Research, Mountain View, CA, USA
| | | | | | | | - R. Potter
- Google Research, Mountain View, CA, USA
| | | | | | - N. Saei
- Google Research, Mountain View, CA, USA
| | - D. Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - D. Strain
- Google Research, Mountain View, CA, USA
| | - Y. Su
- Google Research, Mountain View, CA, USA
| | - M. Szalay
- Google Research, Mountain View, CA, USA
| | - G. Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T. White
- Google Research, Mountain View, CA, USA
| | - Z. Yao
- Google Research, Mountain View, CA, USA
| | - P. Yeh
- Google Research, Mountain View, CA, USA
| | - J. Yoo
- Google Research, Mountain View, CA, USA
| | | | - Y. Zhang
- Google Research, Mountain View, CA, USA
| | - N. Zhu
- Google Research, Mountain View, CA, USA
| | - H. Neven
- Google Research, Mountain View, CA, USA
| | - D. Bacon
- Google Research, Mountain View, CA, USA
| | - J. Hilton
- Google Research, Mountain View, CA, USA
| | - E. Lucero
- Google Research, Mountain View, CA, USA
| | | | - S. Boixo
- Google Research, Mountain View, CA, USA
| | | | - Y. Chen
- Google Research, Mountain View, CA, USA
| | - J. Kelly
- Google Research, Mountain View, CA, USA
| | | | - D. A. Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
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Hong S, Lee J, Heo J, Suh K, Kim S, Kim Y, Kim J, Lee JS. 413P Association of concomitant medications on survival outcomes in cancer patients treated with immune checkpoint inhibitors: Analysis of health insurance review and assessment database. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
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Hall J, Sud S, Casey D, Poellmann M, Bu J, Wang A, Hong S, Shen C. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Locoregional Head and Neck Cancer Receiving Definitive Therapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Song S, Kim J, Nam J, Ko Y, Kim J, Jung S, Kang S, Park J, Seo H, Kim H, Jeong B, Kim T, Choi S, Nam J, Ku J, Joo K, Jang W, Yoon Y, Yun S, Hong S, Oh J. Stage matched head-to-head comparison between urachal carcinoma and urothelial bladder cancer: TNM-stage based analysis from a national multicenter database. EUR UROL SUPPL 2022. [DOI: 10.1016/s2666-1683(22)02591-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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Yu M, Yun M, Lee S, Rajasekaran N, Park K, Kim N, Hong S, Oh S, Lee Y, Lee E, Kim C, Lim S, Choi J, Cho B. 1174P The MET inhibitor ABN401 in combination with the third-generation EGFR-TKI is effective MET-amplified and EGFR-mutant NSCLC with acquired resistance to third-generation EGFR-TKI in preclinical models. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.1297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Zhang QL, Hong S, Dong X, Zheng DW, Liang JL, Bai XF, Wang XN, Han ZY, Zhang XZ. Bioinspired nano-vaccine construction by antigen pre-degradation for boosting cancer personalized immunotherapy. Biomaterials 2022; 287:121628. [PMID: 35704965 DOI: 10.1016/j.biomaterials.2022.121628] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/28/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022]
Abstract
Cancer vaccines-based cancer immunotherapy has drawn widespread concern. However, insufficient cancer antigens and inefficient antigen presentation lead to low immune response rate, which greatly restrict the practical application of cancer vaccines. Here, inspired by intracellular proteasome-mediated protein degradation pathway, we report an antigen presentation simplification strategy by extracellular degradation of antigen proteins into peptides with proteolytic enzyme for improving the utilization of cancer antigens and arousing restricted cancer immunity. The pre-degraded antigen peptides are first validated to exhibit an increased capacity on antigen-presenting cell (APC) stimulation compared with proteins and still reserve antigen specificity and major histocompatibility complex (MHC) affinity. Furthermore, by coordinating the pre-degraded peptides with calcium phosphate nanoparticles (CaP), a CaP-peptide vaccine (CaP-Pep) is constructed, which is verified to induce an efficient personalized immune response in vivo for multi-model anti-cancer therapy. Notably, this bioinspired strategy based on extracellular enzymatic hydrolysis for vaccine construction is not only applicable for multiple types of cancers, but also shows great potential in expanding immunology fields and translational medicine.
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Affiliation(s)
- Qiu-Ling Zhang
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, 430072, PR China
| | - Sheng Hong
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, 430072, PR China
| | - Xue Dong
- Institute for Advanced Studies, Wuhan University, Wuhan, 430072, PR China
| | - Di-Wei Zheng
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, 430072, PR China
| | - Jun-Long Liang
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, 430072, PR China
| | - Xue-Feng Bai
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, 430072, PR China
| | - Xia-Nan Wang
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, 430072, PR China
| | - Zi-Yi Han
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, 430072, PR China
| | - Xian-Zheng Zhang
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, 430072, PR China; Institute for Advanced Studies, Wuhan University, Wuhan, 430072, PR China; Wuhan Research Centre for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, 430071, PR China.
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Ramesh P, Jaishankar D, Cosgrove C, Kosche C, Li A, Hong S, Shivde R, Munir S, Zhang H, Choi J, Le Poole I. 318 Skin rash composition after checkpoint inhibitor therapy varies by therapeutic regimen. J Invest Dermatol 2022. [DOI: 10.1016/j.jid.2022.05.326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Chen B, Hong S, Dai X, Li X, Huang Q, Sun T, Cao D, Zhang H, Chai Z, Diwu J, Wang S. In Vivo Uranium Decorporation by a Tailor-Made Hexadentate Ligand. J Am Chem Soc 2022; 144:11054-11058. [PMID: 35699271 DOI: 10.1021/jacs.2c00688] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The sequestration of uranium, particularly from the deposited bones, has been an incomplete task in chelation therapy for actinide decorporation. Part of the reason is that all previous decorporation ligands are not delicately designed to meet the coordination requirement of uranyl cations. Herein, guided by DFT calculation, we elaborately design a hexadentate ligand (TAM-2LI-MAM2), whose preorganized planar oxo-donor configuration perfectly matches the typical coordination geometry of the uranyl cation. This leads to an ultrahigh binding affinity to uranyl supported by an in vitro desorption experiment of uranyl phosphate. Administration of this ligand by prompt intraperitoneal injection demonstrates its uranyl removal efficiencies from the kidneys and bones are up to 95.4% and 81.2%, respectively, which notably exceeds all the tested chelating agents as well as the clinical drug ZnNa3-DTPA, setting a new record in uranyl decorporation efficacy.
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Affiliation(s)
- Bin Chen
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Sheng Hong
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Xing Dai
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Ximeng Li
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Qi Huang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Tingfeng Sun
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Dehan Cao
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Hailong Zhang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Zhifang Chai
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Juan Diwu
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Shuao Wang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X), and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
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Kang E, Kim YG, Oh JS, Hong S, Lee CK, Yoo B, Ahn SM. POS1247 THE EFFECT OF IMMUNOSUPPRESSIVE AGENTS ON ANTIBODY FORMATION AFTER COVID-19 VACCINATION IN RHEUMATOID ARTHRITIS PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundThere is still controversy about the efficacy of COVID-19 vaccination and its extent in lowering immunogenicity of Rheumatoid Arthritis (RA) patients. The guideline in whether immunosuppressive agents need to be discontinued before the vaccination is continuously updated because it is considered to lower immunogenicity. Furthermore, there is great discussion on the effectiveness of the COVID-19 booster vaccine and interest in antibody generation in different types of vaccine, as in South Korea there are many patients who were prescribed the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine.ObjectivesThus, we investigated the differences of antibody production between patients who received only two doses of ChAdOx1-S nCoV-19 and those who received the mRNA booster vaccine. Also, antibody production under different types of immunosuppressive agents was analyzed.MethodsFrom October 14, 2021 to January 21, 2022 at a tertiary referral center, two patient groups diagnosed with RA were studied prospectively; one group that completed 1st and 2nd doses of ChAdOx1-S nCoV-19 vaccine, second group that completed mRNA booster vaccine as well as two doses of ChAdOx1-S nCoV-19 vaccine. SARS-CoV-2 antibody testing on the semiquantitative anti-SARS-CoV-2 S enzyme immunoassay was done, and differences in antibody titers were analyzed in patients who received different immunosuppressive agents such as csDMARD, TNF inhibitor, JAK inhibitor, Tocilizumab, Abatacept and Corticosteroid. Statistical analysis with a multivariate logistic regression model was performed.ResultsIn a total of 261 patients, 153 patients had completed two doses of ChAdOx1-S nCoV-19, 108 patients had completed third mRNA booster vaccine. Anti-SARS-CoV-2 RBD antibody positive rate (titer>0.8U/mL) was 97%(149/153) and 99%(107/108) respectively, and only 5 patients showed negative result. In the aspect of high antibody titer(>250U/mL), which is the upper limit of the RBD antibody immunoassay, the result showed rate of 31% (47/153) in the non-booster group and 94%(102/108) in the booster group respectively.Among the different immunosuppressive agents and other clinical aspects, multivariate analysis revealed that corticosteroid use (OR 0.91; 95% CI: 0.86-0.98), older age(OR 4.33; 95% CI: 1.34-13.91), and male gender(OR 0.35; 95% CI 0.16-0.75) were significantly associated with low rate of high antibody titer.Furthermore, out of 14 patients who underwent antibody test twice before and after the mRNA booster vaccine, other than four patients who already showed high titer of >250U/mL before the mRNA booster vaccine, 10 patients showed an increase in titer after the booster vaccine and 7 patients were acquired high titer of >250U/mL.Figure 1.Anti-SARS-CoV RBD antibody titer of two groupsTable 1.Analysis of immunosuppressive agents and other clinical aspects for high antibody titer(>250U/mL) after two doses of ChAdOx1-S nCoV-19Univariate analysisMultivariate analysisParameterOR95% CIp valueOR95% CIp valueClinical features Age0.9170.860-0.9780.0080.9170.857-0.9810.012 Sex3.6741.206-11.1910.0224.3301.348-13.9120.014 DAS 281.1440.670-1.9500.622 Duration0.9300.830-1.0430.214Medications csDMARD1.2730.639-2.5331.273 TNF inhibitor2.2110.795-6.1450.128 JAK inhibitor0.6650.275-1.6070.365 Abatacept0.3680.038-3.6020.391 Tocilizumab1.2640.438-3.6480.665 Corticosteroid0.4720.235-0.9490.0350.3490.163-0.7480.007Medication dose Methotrexate0.9930.919-1.0720.855 Corticosteroid0.8490.719-1.0030.054ConclusionAnti-SARS-CoV-2 RBD antibody positive rate was 97% or more regardless of the mRNA booster vaccination. However, patients who received the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine showed high antibody titer (>250U/mL) three times more than those who did not receive the booster shot.Our findings also showed that corticosteroid use, old age, and male gender is significantly associated with low rate of acquiring high antibody titer.Disclosure of InterestsNone declared
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Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0476 PREDICTIVE FACTORS FOR THE DEVELOPMENT OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH IMMUNE THROMBOCYTOPENIA. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.1436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundPatients with immune thrombocytopenia (ITP) have a risk of developing systemic lupus erythematosus (SLE). We sought to examine the clinical characteristics of patients with primary ITP who later developed SLE, and identified the risk factors for the development of SLE.ObjectivesWe retrospectively examined patients who were diagnosed with primary ITP at a tertiary hospital between August 2001 and November 2019. We compared the clinical characteristics according to the development of SLE. Logistic regression analysis was performed to identify the factors associated with the development of SLE.MethodsOf 130 patients with primary ITP, 10 (7.7%) were later diagnosed with SLE during follow-up (median, 30 months [IQR, 15.5–105]). The presence of skin bleeding, organ bleeding, lymphopenia, anemia, and positive antinuclear antibody (ANA) titer (> 1:160) were more common among patients who later developed SLE than did those who did not develop SLE. Multivariate analysis showed that young age (< 40 years; odds ratio [OR], 8.359 [95% confidence interval (CI), 1.230–56.793]; p = 0.033), organ bleeding (OR, 18.349 [95% CI, 2.771–121.517]; p = 0.003), and ANA positivity (>1:160; OR, 7.692 [95% CI, 1.482–39.910]; p = 0.015) were significantly associated with the development of SLE.ResultsYoung age (< 40 years), organ bleeding, and ANA positivity (> 1:160) were risk factors for the development of SLE in patients with primary ITP.ConclusionThese results suggest that continued follow-up for the detection of SLE development is needed for patients with ITP, particularly those with young age, ANA positivity, or organ bleeding.References[1]Zhu, Fang-Xiao, et al. “Risk of systemic lupus erythematosus in patients with idiopathic thrombocytopenic purpura: a population-based cohort study.” Annals of the rheumatic diseases 79.6 (2020): 793-799.Table 1.Factors associated with the development of SLE in patients with primary ITPUnivariateMultivariateOR95% CIP valueOR95% CIP valueYoung agea5.4441.332–22.2500.0188.3591.230–56.7930.033Female4.3330.530–35.4220.17BMI0.8730.717–1.0700.20Skin bleeding8.4191.034–68.5330.046Mucosa bleeding1.2500.247–6.3300.79Organ bleeding14.8643.633–60.815< 0.00118.3492.771–121.5170.003Platelet counts0.9110.828–1.0020.06ANA positivityb16.5003.984–68.341< 0.0017.6921.482–39.9100.015Neutropeniac2.1110.229–19.4990.51Lymphopeniad4.8461.189–19.7590.028Anemiae10.1182.044–50.0910.005SLE: systemic lupus erythematosus, ITP: immune thrombocytopenia, BMI: body mass index, ANA: antinuclear antibody, OR: odds ratio, CI: confidence interval.aYoung age = age < 40 yearsbANA positivity ≥ 1:160cNeutropenia = Absolute neutrophil count < 1500 μLdLymphopenia = Absolute lymphocyte count < 1500 μLeAnemia = Hemoglobin < 12 g/dLDisclosure of InterestsNone declared
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Kim YE, Choi SJ, Lim DH, Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0456 DISEASE FLARE OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH END-STAGE RENAL DISEASE ON DIALYSIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BackgroundThe systemic lupus erythematosus (SLE) disease activity in patients with lupus nephritis (LN) generally declines after the initiation of renal replacement therapy (RRT); this is known as the “burn out” phenomenon that possibly occurs due to the suppression of cellular and humoral immunity in the end-stage renal disease (ESRD) state and elimination of disease pathogenic factor by dialysis [1-4]. However, several studies showed that SLE flares could occur even during RRT [5-8]. Nevertheless, the details of disease flares of SLE in patients under dialysis have not been studied yet.ObjectivesThis study aimed to investigate the clinical features, risk factors, and treatment details of SLE patients experiencing disease flare under RRT.MethodsThe medical records of SLE patients who received dialysis at two tertiary referral hospitals in Seoul and Ulsan, South Korea were reviewed. All patients in this study were either clinically or histologically diagnosed with LNResultsOf a total of 121 patients with SLE on dialysis, 96 (79.3%) were on hemodialysis (HD) and 25 (20.6%) were on peritoneal dialysis (PD). During a median follow-up of 45 months (IQR, 23–120) after the initiation of dialysis, 32 (26.4%) patients experienced SLE flare (HD, n = 25; PD, n = 7). The most common features of SLE flare were hematologic (40.6%) and constitutional manifestations (40.6%). Treatments for disease flares were based on corticosteroids, and 11 (34.3%) patients required additional immunosuppressants including cyclophosphamide and mycophenolate mofetil. There was no case of severe adverse events related to medication. non-renal SLE Disease Activity Index (SLEDAI) score before dialysis initiation (HR 1.235; 95% CI, 1.122–1.359; P = 0.001) was a significant risk factor for disease flare during dialysis.Table 1.Multivariable analysis of factors associated with SLE flare under dialysisHazard ratio95% CIP-valueNon-renal SLEDAI at the initiation of dialysis1.2351.122–1.3590.001Hematologic manifestation prior to dialysis1.2560.690–2.8260.150Cumulative amount of steroid during 1 year prior to the initiation of dialysis1.0400.995–1.0870.086Dialysis modality: hemodialysis0.7660.262–2.2430.630ConclusionMore than one-quarter of SLE patients experienced disease flare during dialysis, which most commonly had hematologic manifestations. Continued follow-up and appropriate treatments including immunosuppressants should be considered for patients with SLE under dialysis.References[1]Coplon NS, Diskin CJ, Petersen J, Swenson RS. The Long-Term Clinical Course of Systemic Lupus Erythematosus in End-Stage Renal Disease. New England Journal of Medicine 1983;308:186-90.[2]Lee P-T, Fang H-C, Chen C-L, Chiou Y-H, Chou K-J, Chung H-M. Poor prognosis of end-stage renal disease in systemic lupus erythematosus: a cohort of Chinese patients. Lupus 2003;12:827-32.[3]Pahl MV, Gollapudi S, Sepassi L, Gollapudi P, Elahimehr R, Vaziri ND. Effect of end-stage renal disease on B-lymphocyte subpopulations, IL-7, BAFF and BAFF receptor expression. Nephrology Dialysis Transplantation 2010;25:205-12.[4]Ribeiro FM, Fabris CL, Bendet I, Lugon JR. Survival of lupus patients on dialysis: a Brazilian cohort. Rheumatology 2013;52:494-500.[5]Okano K, Yumura W, Nitta K et al. Analysis of Lupus Activity in End-Stage Renal Disease Treated by Hemodialysis. Internal Medicine 2001;40:598-602.[6]Barrera-Vargas A, Quintanar-Martínez M, Merayo-Chalico J, Alcocer-Varela J, Gómez-Martín D. Risk factors for systemic lupus erythematosus flares in patients with end-stage renal disease: a case–control study. Rheumatology 2015:kev349.[7]Cucchiari D, Graziani G, Ponticelli C. The dialysis scenario in patients with systemic lupus erythematosus. Nephrology Dialysis Transplantation 2014;29:1507-13.[8]Kang S-H, Chung B-H, Choi S-R et al. Comparison of Clinical Outcomes by Different Renal Replacement Therapy in Patients with End-Stage Renal Disease Secondary to Lupus Nephritis. The Korean Journal of Internal Medicine 2011;26:60.Disclosure of InterestsNone declared
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Nam SH, Ahn SM, Oh JS, Hong S, Lee CK, Yoo B, Kim YG. AB1273 MACROPHAGE ACTIVATION SYNDROME IN RHEUMATIC DISEASE: CLINICAL CHARACTERISTICS AND PROGNOSIS OF 20 PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundMacrophage activation syndrome (MAS) is a hyperinflammatory condition that is known to be secondary hemophagocytic lymphohistiocytosis (HLH) in patients with rheumatic disease.ObjectivesThe aim of study was to evaluate the clinical manifestations and outcomes in patients with MAS with rheumatic disease.MethodsWe performed a retrospective study of 20 adult patients who were diagnosed with MAS from 2012 to 2020. MAS was classified according to the HLH-2004 criteria. Patients’ information, including clinical features, laboratory findings, and treatment regimens, was collected, and the overall survival rate was estimated by the Kaplan–Meier method.ResultsTwenty patients (18 women, 35.6 ± 18.3 years) who met the HLH-2004 criteria also fulfilled the 2016 EULAR/ACR/PRINTO classification criteria for MAS, and HScore was higher than 169 (median, 238.5). Fourteen patients with systemic lupus erythematosus and 6 patients with adult-onset Still’s disease were included. All patients were treated initially with corticosteroids, and 16 patients required additional immunosuppressants. The overall survival at 3 and 6 months was 75.2% and 64.3%. In survivors, renal impairment was less common (23.1% versus 42.9%, p = 0.007), the levels of AST (202.0 versus 72.0 IU/L, p = 0.006) and LDH (1144.0 versus 343.0IU/L, p = 0.001), and platelet count (90.0 versus 46.0 × 109/L, p = 0.016) were higher in compared to non-survivors. Nine patients had opportunistic infections, five of whom died during admission.ConclusionThe mortality of patients with MAS remains high. Renal impairment, levels of AST and LDH, and platelet count might be associated with prognosis.Table 1.Treatments and management characteristics of patients with MASNo.Age/sexDiseaseDisease duration (months)1st Treatment (corticosteroids)2nd Treatment3rd TreatmentCombined infectionAlive/dead119/FSLE11 mg/kgIVIG + PPTCZ, RTXBacteremiaDead220/MSLE01 mg/kg---Alive320/FAOSD11 mg/kgVP16--Alive422/FSLE1100 mgIVIG + PP-PneumoniaDead522/FAOSD0500 mgIVIG--Alive623/FSLE1821 mg/kg---Alive723/FSLE411 mg/kg---Alive830/FSLE1461 mg/kgIVIGCsA-Alive932/FSLE1271 mg/kgIVIG + PPCsA, TCZPneumoniaAlive1035/FAOSD01 mg/kgCsA-Viral infectionAlive1137/FSLE651 mg/kgCsA, VP16-BacteremiaAlive1238/FSLE01 mg/kgIVIG + PPRTX-Dead1340/FAOSD00.5 mg/kgCsA--Alive1443/FSLE601 mg/kgIVIG + PPTCZ, RTX, CsA,PCP,DeadVP16, IFXViral infection1549/FSLE01 mg/kgCYC-BacteremiaAlive1651/FAOSD01 mg/kg---Alive1757/FSLE01 mg/kgIVIG + PPCsA, VP16Fungal infectionDead1861/FSLE21 mg/kgIVIG + PPTCZ-Dead1968/FSLE21 mg/kgIVIG + PPCsAFungal infectionAlive2070/MAOSD01 mg/kgIVIG + PPCsA, VP16Fungal infectionDeadSLE: Systemic lupus erythematosus, IVIG: Intravenous immunoglobulin, PP: Plasmapheresis, TCZ: Tocilizumab, RTX: Rituximab, AOSD: Adult-onset still’s disease, VP16: Etoposide, PCP: Pneumocystis pneumonia, CsA: Cyclosporin, IFX: Infliximab, MCTD: Mixed connective tissue disease.Disclosure of InterestsNone declared
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Kang E, Hong S, Kim YG, Lee CK, Oh JS, Yoo B, Ahn SM. POS0762 LONG-TERM RENAL OUTCOMES OF PATIENTS WITH NON-PROLIFERATIVE LUPUS NEPHRITIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundAlthough proliferative (class III or IV) lupus nephritis (LN) is the most common finding in the classification of LN, pure membranous (class V) or mesangial (class I or II) LN can occur as a form of LN. Even though non-proliferative LN (class I, II, or V) is a less severe form with good outcomes, data on long-term renal prognosis are limited.ObjectivesThis study investigated the long-term outcomes and prognostic factors in non-proliferative LN.MethodsWe retrospectively reviewed the medical records of patients with systemic lupus erythematosus who were diagnosed with LN class I, II, V or II+IV by kidney biopsy between 1997 and 2021 at a tertiary referral center. Clinical and laboratory data were compared between patients with and without poor renal outcomes. Poor renal outcome was defined as an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2 or death due to renal cause. Univariate and multivariate analyses were performed with the Cox proportional hazard model to identify the factors associated with poor renal outcomes.ResultsWe included 71 patients with non-proliferative LN (4: class I; 17: class II; 48: class V, 17; 2: class II+V). Median follow-up duration was 103 months (interquartile range 27–185) and the overall rate of poor renal outcomes at last follow-up was 29% (21/71), including end-stage renal disease (n=2) and renal death (n=1).Univariate analysis indicated that older age (HR 1.05; 95% CI: 1.00–1.09), low eGFR (HR 0.97; 95% CI: 0.95–0.99) and failure to reach complete remission at 6 months (HR 0.332; 95% CI: 0.12–0.92) were significantly associated with poor renal outcomes. Multivariate analysis revealed that low eGFR at 6 months (HR 0.97; 95% CI: 0.95–0.99) was significantly associated with poor renal outcomes.Figure 1.Renal outcomes at last follow upeGFR, estimated glomerular filtration rate (ml/min/1.73m2)Table 1.Univariate and multivariate Cox proportional hazard regression analyses of the factor associated with poor renal outcomesParameterUnivariate analysisMultivariate analysisHR95% CIp valueHR95% CIp valueClinical features Age1.0461.003-1.0910.0361.0020.960-1.0470.921 Sex1.6540.375-7.2980.506 SLEDAI1.0360.965-1.1120.327 Extra renal SLEDAI1.0380.971-1.110.272Renal profiles eGFR at LN diagnosis0.9930.976-1.0110.456 Proteinuria at LN diagnosis1.0001.000-1.0000.444 > 1g/24 hours0.6690.243-1.8410.437 > 3g/24 hours0.6240.229-1.6990.356 eGFR at 6M0.9670.948-0.9860.0010.9680.948-0.9880.002 eGFR at 12M0.9640.947-0.9810.000 Complete remission at 6M0.3320.119-0.9240.0350.5530.179-1.7070.303 Complete remission at 12M0.6670.232-1.9140.451 Transformation1.2460.423-0.7010.692Laboratory data Anti-dsDNA1.0010.999-1.0030.196 C31.0201.000-1.0410.051 C41.0270.969-1.0890.367 Albumin1.1800.661-2.1090.576ClassificationaClass I0.8020.102-6.3030.834Class II1.2980.412-4.0880.656Class V0.8870.308-2.5570.824Class II+V0.0480.000-16850.837Medicationsb ACEi/ARB1.6520.603-4.5280.329 Hydroxychloroquine1.3260.414-4.2420.635 Corticosteroid1.1860.154-9.1080.870 CNI2.4390.464-12.8240.292 MMF3.7880.959-14.9650.057 AZA0.5890.133-2.6110.486a LN classifications were based on the International Society of Pathology/Renal Pathology Society (ISN/RPS) classification.b Medications maintained at least one year since Lupus Nephritis diagnosis.HR, hazard ratio; 95% CI, 95% confidence interval; SLEDAI, systemic lupus erythematosus disease activity index; eGFR, estimated glomerular filtration rate; LN, lupus nephritis; anti-dsDNA, anti-double strand DNA; C3/C4; complement 3/4; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CNI, carcineurin inhibitor; MMF, mycophenolate mofetil; AZA, azathioprine.ConclusionPoor renal outcomes occurred in approximately 30% of patients with non-proliferative LN (class I, II or V) after long-term follow-up.Our findings suggest that more active management may be needed for non-proliferative LN, particularly in patients with low eGFR at 6 months.Disclosure of InterestsNone declared
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Hong S, Huang QX, Zhong Z, Rong L, Zhang XZ. Photo-Initiated Coagulation Activation and Fibrinolysis Inhibition for Synergetic Tumor Vascular Infarction via a Gold Nanorods-Based Nanosystem. CCS Chem 2022. [DOI: 10.31635/ccschem.021.202100908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Sheng Hong
- Key Laboratory of Biomedical Polymers of Ministry of Education, Department of Chemistry, Wuhan University, Wuhan 430072
| | - Qian-Xiao Huang
- Key Laboratory of Biomedical Polymers of Ministry of Education, Department of Chemistry, Wuhan University, Wuhan 430072
| | - Zhenlin Zhong
- Key Laboratory of Biomedical Polymers of Ministry of Education, Department of Chemistry, Wuhan University, Wuhan 430072
| | - Lei Rong
- Key Laboratory of Biomedical Polymers of Ministry of Education, Department of Chemistry, Wuhan University, Wuhan 430072
- Institute of Pharmaceutical Sciences, China Pharmaceutical University, Nanjing 210009
| | - Xian-Zheng Zhang
- Key Laboratory of Biomedical Polymers of Ministry of Education, Department of Chemistry, Wuhan University, Wuhan 430072
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Chidambaram S, Hong S, Simpson M, Osazuwa-Peters N, Ward G, Massa S. Temporal Trends in Oropharyngeal Cancer Incidence, Survival, and Cancer-Directed Surgery Among Elderly Americans. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2021.12.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Soulsby WD, Balmuri N, Cooley V, Gerber LM, Lawson E, Goodman S, Onel K, Mehta B, Abel N, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar-Smiley F, Barillas-Arias L, Basiaga M, Baszis K, Becker M, Bell-Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang-Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel-Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie-Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui-Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein-Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PMC, McGuire S, McHale I, McMonagle A, McMullen-Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O’Brien B, O’Brien T, Okeke O, Oliver M, Olson J, O’Neil K, Onel K, Orandi A, Orlando M, Osei-Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan-Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas-Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth-Wojcicki E, Rouster-Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert-Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner-Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Social determinants of health influence disease activity and functional disability in Polyarticular Juvenile Idiopathic Arthritis. Pediatr Rheumatol Online J 2022; 20:18. [PMID: 35255941 PMCID: PMC8903717 DOI: 10.1186/s12969-022-00676-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/07/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Social determinants of health (SDH) greatly influence outcomes during the first year of treatment in rheumatoid arthritis, a disease similar to polyarticular juvenile idiopathic arthritis (pJIA). We investigated the correlation of community poverty level and other SDH with the persistence of moderate to severe disease activity and functional disability over the first year of treatment in pJIA patients enrolled in the Childhood Arthritis and Rheumatology Research Alliance Registry. METHODS In this cohort study, unadjusted and adjusted generalized linear mixed effects models analyzed the effect of community poverty and other SDH on disease activity, using the clinical Juvenile Arthritis Disease Activity Score-10, and disability, using the Child Health Assessment Questionnaire, measured at baseline, 6, and 12 months. RESULTS One thousand six hundred eighty-four patients were identified. High community poverty (≥20% living below the federal poverty level) was associated with increased odds of functional disability (OR 1.82, 95% CI 1.28-2.60) but was not statistically significant after adjustment (aOR 1.23, 95% CI 0.81-1.86) and was not associated with increased disease activity. Non-white race/ethnicity was associated with higher disease activity (aOR 2.48, 95% CI: 1.41-4.36). Lower self-reported household income was associated with higher disease activity and persistent functional disability. Public insurance (aOR 1.56, 95% CI 1.06-2.29) and low family education (aOR 1.89, 95% CI 1.14-3.12) was associated with persistent functional disability. CONCLUSION High community poverty level was associated with persistent functional disability in unadjusted analysis but not with persistent moderate to high disease activity. Race/ethnicity and other SDH were associated with persistent disease activity and functional disability.
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Affiliation(s)
- William Daniel Soulsby
- University of California, San Francisco, 550 16th Street, 4th Floor, Box #0632, San Francisco, CA, 94158, USA.
| | - Nayimisha Balmuri
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Victoria Cooley
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Linda M. Gerber
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Erica Lawson
- grid.266102.10000 0001 2297 6811University of California, San Francisco, 550 16th Street, 4th Floor, Box #0632, San Francisco, CA 94158 USA
| | - Susan Goodman
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Karen Onel
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Bella Mehta
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
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Dong C, Huang Q, Cheng H, Zheng D, Hong S, Yan Y, Niu M, Xu J, Zhang X. Neisseria meningitidis Opca Protein/MnO 2 Hybrid Nanoparticles for Overcoming the Blood-Brain Barrier to Treat Glioblastoma. Adv Mater 2022; 34:e2109213. [PMID: 34995395 DOI: 10.1002/adma.202109213] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 01/05/2022] [Indexed: 02/05/2023]
Abstract
The major hurdle in glioblastoma therapy is the low efficacy of drugs crossing the blood-brain barrier (BBB). Neisseria meningitidis is known to specifically enrich in the central nervous system through the guidance of an outer membrane invasion protein named Opca. Here, by loading a chemotherapeutic drug methotrexate (MTX) in hollow manganese dioxide (MnO2 ) nanoparticles with surface modification of the Opca protein of Neisseria meningitidis, a bionic nanotherapeutic system (MTX@MnO2 -Opca) is demonstrated to effectively overcome the BBB. The presence of the Opca protein enables the drug to cross the BBB and penetrate into tumor tissues. After accumulating in glioblastoma, the nanotherapeutic system catalyzes the decomposition of excess H2 O2 in the tumor tissue and thereby generates O2 , which alleviates tumor hypoxia and enhances the effect of chemotherapy in the treatment of glioblastoma. This bionic nanotherapeutic system may exhibit great potential in the treatment of glioblastoma.
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Affiliation(s)
- Cheng‐Yuan Dong
- Department of Neurosurgery West China Hospital Sichuan University Chengdu 610041 P. R. China
| | - Qian‐Xiao Huang
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry Wuhan University Wuhan 430072 P. R. China
| | - Han Cheng
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry Wuhan University Wuhan 430072 P. R. China
| | - Di‐Wei Zheng
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry Wuhan University Wuhan 430072 P. R. China
| | - Sheng Hong
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry Wuhan University Wuhan 430072 P. R. China
| | - Yu Yan
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry Wuhan University Wuhan 430072 P. R. China
| | - Mei‐Ting Niu
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry Wuhan University Wuhan 430072 P. R. China
| | - Jian‐Guo Xu
- Department of Neurosurgery West China Hospital Sichuan University Chengdu 610041 P. R. China
| | - Xian‐Zheng Zhang
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry Wuhan University Wuhan 430072 P. R. China
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44
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Ge Y, Cheng J, Xue L, Zhang B, Zhang P, Cui X, Hong S, Wu Y, Zhang X, Liang X. Durability and corrosion behaviors of superhydrophobic amorphous coatings: a contrastive investigation. RSC Adv 2022; 12:32813-32824. [DOI: 10.1039/d2ra06073f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/11/2022] [Indexed: 11/18/2022] Open
Abstract
We present two kinds of superhydrophobic AlNiTi amorphous coatings with concave and convex surface structures. The convex superhydrophobic surface displays higher water-repellence (157.6°), better corrosion resistance, and durability.
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Affiliation(s)
- Yunyun Ge
- College of Mechanics and Materials, Hohai University, Nanjing, 211100, P. R. China
| | - Jiangbo Cheng
- College of Mechanics and Materials, Hohai University, Nanjing, 211100, P. R. China
| | - Lin Xue
- College of Mechanics and Materials, Hohai University, Nanjing, 211100, P. R. China
| | - Baosen Zhang
- School of Materials Engineering, Nanjing Institute of Technology, Nanjing, 211167, P. R. China
| | - Peipei Zhang
- National Institute of Defense Technology Innovation, Academy of Military Sciences PLA China, Beijing, 100010, P. R. China
| | - Xin Cui
- National Institute of Defense Technology Innovation, Academy of Military Sciences PLA China, Beijing, 100010, P. R. China
| | - Sheng Hong
- College of Mechanics and Materials, Hohai University, Nanjing, 211100, P. R. China
| | - Yuping Wu
- College of Mechanics and Materials, Hohai University, Nanjing, 211100, P. R. China
| | - Xiancheng Zhang
- Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, P. R. China
| | - Xiubing Liang
- National Institute of Defense Technology Innovation, Academy of Military Sciences PLA China, Beijing, 100010, P. R. China
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45
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Hong S, L R, Mclellan L, Dabney J, Gerds TA, Rotz S, Kalaycio M, Hanna R, Hamilton BK, Majhail N, Sobecks RM. Comparison of Quality of Life and Outcomes between Haploidentical and Matched Related/Unrelated Donor Allogeneic Hematopoietic Cell Transplantation. Transplant Cell Ther 2022; 28:217.e1-217.e6. [DOI: 10.1016/j.jtct.2022.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/04/2022] [Accepted: 01/12/2022] [Indexed: 11/17/2022]
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46
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Satzinger KJ, Liu YJ, Smith A, Knapp C, Newman M, Jones C, Chen Z, Quintana C, Mi X, Dunsworth A, Gidney C, Aleiner I, Arute F, Arya K, Atalaya J, Babbush R, Bardin JC, Barends R, Basso J, Bengtsson A, Bilmes A, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chiaro B, Collins R, Courtney W, Demura S, Derk AR, Eppens D, Erickson C, Faoro L, Farhi E, Fowler AG, Foxen B, Giustina M, Greene A, Gross JA, Harrigan MP, Harrington SD, Hilton J, Hong S, Huang T, Huggins WJ, Ioffe LB, Isakov SV, Jeffrey E, Jiang Z, Kafri D, Kechedzhi K, Khattar T, Kim S, Klimov PV, Korotkov AN, Kostritsa F, Landhuis D, Laptev P, Locharla A, Lucero E, Martin O, McClean JR, McEwen M, Miao KC, Mohseni M, Montazeri S, Mruczkiewicz W, Mutus J, Naaman O, Neeley M, Neill C, Niu MY, O'Brien TE, Opremcak A, Pató B, Petukhov A, Rubin NC, Sank D, Shvarts V, Strain D, Szalay M, Villalonga B, White TC, Yao Z, Yeh P, Yoo J, Zalcman A, Neven H, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Kitaev A, Knap M, Pollmann F, Roushan P. Realizing topologically ordered states on a quantum processor. Science 2021; 374:1237-1241. [PMID: 34855491 DOI: 10.1126/science.abi8378] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
[Figure: see text].
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Affiliation(s)
| | - Y-J Liu
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany
| | - A Smith
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK.,Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems, University of Nottingham, Nottingham NG7 2RD, UK
| | - C Knapp
- Department of Physics and Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA.,Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA
| | - M Newman
- Google Quantum AI, Mountain View, CA, USA
| | - C Jones
- Google Quantum AI, Mountain View, CA, USA
| | - Z Chen
- Google Quantum AI, Mountain View, CA, USA
| | - C Quintana
- Google Quantum AI, Mountain View, CA, USA
| | - X Mi
- Google Quantum AI, Mountain View, CA, USA
| | | | - C Gidney
- Google Quantum AI, Mountain View, CA, USA
| | - I Aleiner
- Google Quantum AI, Mountain View, CA, USA
| | - F Arute
- Google Quantum AI, Mountain View, CA, USA
| | - K Arya
- Google Quantum AI, Mountain View, CA, USA
| | - J Atalaya
- Google Quantum AI, Mountain View, CA, USA
| | - R Babbush
- Google Quantum AI, Mountain View, CA, USA
| | - J C Bardin
- Google Quantum AI, Mountain View, CA, USA.,Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - R Barends
- Google Quantum AI, Mountain View, CA, USA
| | - J Basso
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Bilmes
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Quantum AI, Mountain View, CA, USA
| | - B Burkett
- Google Quantum AI, Mountain View, CA, USA
| | - N Bushnell
- Google Quantum AI, Mountain View, CA, USA
| | - B Chiaro
- Google Quantum AI, Mountain View, CA, USA
| | - R Collins
- Google Quantum AI, Mountain View, CA, USA
| | - W Courtney
- Google Quantum AI, Mountain View, CA, USA
| | - S Demura
- Google Quantum AI, Mountain View, CA, USA
| | - A R Derk
- Google Quantum AI, Mountain View, CA, USA
| | - D Eppens
- Google Quantum AI, Mountain View, CA, USA
| | - C Erickson
- Google Quantum AI, Mountain View, CA, USA
| | - L Faoro
- Laboratoire de Physique Theorique et Hautes Energies, Sorbonne Université, 75005 Paris, France
| | - E Farhi
- Google Quantum AI, Mountain View, CA, USA
| | - A G Fowler
- Google Quantum AI, Mountain View, CA, USA
| | - B Foxen
- Google Quantum AI, Mountain View, CA, USA
| | - M Giustina
- Google Quantum AI, Mountain View, CA, USA
| | - A Greene
- Google Quantum AI, Mountain View, CA, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - J A Gross
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - J Hilton
- Google Quantum AI, Mountain View, CA, USA
| | - S Hong
- Google Quantum AI, Mountain View, CA, USA
| | - T Huang
- Google Quantum AI, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Quantum AI, Mountain View, CA, USA
| | - S V Isakov
- Google Quantum AI, Mountain View, CA, USA
| | - E Jeffrey
- Google Quantum AI, Mountain View, CA, USA
| | - Z Jiang
- Google Quantum AI, Mountain View, CA, USA
| | - D Kafri
- Google Quantum AI, Mountain View, CA, USA
| | | | - T Khattar
- Google Quantum AI, Mountain View, CA, USA
| | - S Kim
- Google Quantum AI, Mountain View, CA, USA
| | - P V Klimov
- Google Quantum AI, Mountain View, CA, USA
| | - A N Korotkov
- Google Quantum AI, Mountain View, CA, USA.,Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | - D Landhuis
- Google Quantum AI, Mountain View, CA, USA
| | - P Laptev
- Google Quantum AI, Mountain View, CA, USA
| | - A Locharla
- Google Quantum AI, Mountain View, CA, USA
| | - E Lucero
- Google Quantum AI, Mountain View, CA, USA
| | - O Martin
- Google Quantum AI, Mountain View, CA, USA
| | | | - M McEwen
- Google Quantum AI, Mountain View, CA, USA.,Department of Physics, University of California, Santa Barbara, CA, USA
| | - K C Miao
- Google Quantum AI, Mountain View, CA, USA
| | - M Mohseni
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - J Mutus
- Google Quantum AI, Mountain View, CA, USA
| | - O Naaman
- Google Quantum AI, Mountain View, CA, USA
| | - M Neeley
- Google Quantum AI, Mountain View, CA, USA
| | - C Neill
- Google Quantum AI, Mountain View, CA, USA
| | - M Y Niu
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Opremcak
- Google Quantum AI, Mountain View, CA, USA
| | - B Pató
- Google Quantum AI, Mountain View, CA, USA
| | - A Petukhov
- Google Quantum AI, Mountain View, CA, USA
| | - N C Rubin
- Google Quantum AI, Mountain View, CA, USA
| | - D Sank
- Google Quantum AI, Mountain View, CA, USA
| | - V Shvarts
- Google Quantum AI, Mountain View, CA, USA
| | - D Strain
- Google Quantum AI, Mountain View, CA, USA
| | - M Szalay
- Google Quantum AI, Mountain View, CA, USA
| | | | - T C White
- Google Quantum AI, Mountain View, CA, USA
| | - Z Yao
- Google Quantum AI, Mountain View, CA, USA
| | - P Yeh
- Google Quantum AI, Mountain View, CA, USA
| | - J Yoo
- Google Quantum AI, Mountain View, CA, USA
| | - A Zalcman
- Google Quantum AI, Mountain View, CA, USA
| | - H Neven
- Google Quantum AI, Mountain View, CA, USA
| | - S Boixo
- Google Quantum AI, Mountain View, CA, USA
| | - A Megrant
- Google Quantum AI, Mountain View, CA, USA
| | - Y Chen
- Google Quantum AI, Mountain View, CA, USA
| | - J Kelly
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Kitaev
- Google Quantum AI, Mountain View, CA, USA.,Department of Physics and Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA.,Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA
| | - M Knap
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany.,Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany
| | - F Pollmann
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany
| | - P Roushan
- Google Quantum AI, Mountain View, CA, USA
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47
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Zheng DW, Deng WW, Song WF, Wu CC, Liu J, Hong S, Zhuang ZN, Cheng H, Sun ZJ, Zhang XZ. Biomaterial-mediated modulation of oral microbiota synergizes with PD-1 blockade in mice with oral squamous cell carcinoma. Nat Biomed Eng 2021; 6:32-43. [PMID: 34750535 DOI: 10.1038/s41551-021-00807-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 09/10/2021] [Indexed: 12/24/2022]
Abstract
Because a host's immune system is affected by host-microbiota interactions, means of modulating the microbiota could be leveraged to augment the effectiveness of cancer therapies. Here we report that patients with oral squamous cell carcinoma (OSCC) whose tumours contained higher levels of bacteria of the genus Peptostreptococcus had higher probability of long-term survival. We then show that in mice with murine OSCC tumours injected with oral microbiota from patients with OSCCs, antitumour responses were enhanced by the subcutaneous delivery of an adhesive hydrogel incorporating silver nanoparticles (which inhibited the growth of bacteria competing with Peptostreptococcus) alongside the intratumoural delivery of the bacterium P. anaerobius (which upregulated the levels of Peptostreptococcus). We also show that in mice with subcutaneous or orthotopic murine OSCC tumours, combination therapy with the two components (nanoparticle-incorporating hydrogel and exogenous P. anaerobius) synergized with checkpoint inhibition with programmed death-1. Our findings suggest that biomaterials can be designed to modulate human microbiota to augment antitumour immune responses.
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Affiliation(s)
- Di-Wei Zheng
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, P. R. China
| | - Wei-Wei Deng
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education & Department of Oral Maxillofacial Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, P. R. China
| | - Wen-Fang Song
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, P. R. China
| | - Cong-Cong Wu
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education & Department of Oral Maxillofacial Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, P. R. China
| | - Jie Liu
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education & Department of Oral Maxillofacial Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, P. R. China
| | - Sheng Hong
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, P. R. China
| | - Ze-Nan Zhuang
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, P. R. China
| | - Han Cheng
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, P. R. China
| | - Zhi-Jun Sun
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education & Department of Oral Maxillofacial Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, P. R. China.
| | - Xian-Zheng Zhang
- Key Laboratory of Biomedical Polymers of Ministry of Education & Department of Chemistry, Wuhan University, Wuhan, P. R. China.
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48
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Sud S, Hall J, Tan X, Roberts O, Green R, Park S, Poellmann M, Bu J, Hong S, Wang A, Casey D. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients With Oligometastatic Disease Receiving Definitive Radiation Therapy. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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49
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Liu J, Zhou H, Ma W, Zhang Y, Zhou T, Yang Y, Huang J, Zhao Y, Hong S, Zhan J, Zhao H, Huang Y, Fang W, Zhang L. MA03.05 DNA Damage Response (DDR) Gene Mutations and Correlation With Immunotherapy Response in NSCLC Patients. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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50
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Pagliuca S, Gurnari C, Hong S, Kongkiatkamon S, Awada H, Terkawi L, Zawit M, Visconte V, Hamilton B, Carraway H, Majhail N, Maciejewski J. Topic: AS04-MDS Biology and Pathogenesis/AS04h-Immune deregulation. Leuk Res 2021. [DOI: 10.1016/j.leukres.2021.106678.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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