1
|
Jiang Z, Yang T, Xu L. Head-to-head comparison of prostate-specific membrane antigen positron emission tomography/computed tomography and multiparametric magnetic resonance imaging in the detection of biochemical recurrence of prostate cancer: a systematic review and meta-analysis. Clin Radiol 2024; 79:436-445. [PMID: 38582633 DOI: 10.1016/j.crad.2024.02.008] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 04/08/2024]
Abstract
AIM Our main goal of this meta-analytical analysis was to evaluate the diagnostic effectiveness of prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) against multiparametric magnetic resonance imaging (mpMRI) in the context of identifying biochemical recurrence in patients with prostate cancer (PCa). MATERIALS AND METHODS A thorough search covering articles published until March 2023 was carried out across major databases such as PubMed, Embase, and Web of Science. Studies examining the direct comparison of PSMA PET/CT and mpMRI in patients with PCa suffering biochemical recurrence were included in the inclusion criteria. Using the renowned Quality Assessment of Diagnostic Performance Studies-2 technique, each study's methodological rigor was assessed. RESULTS We analyzed data from six eligible studies involving 290 patients in total. The combined data showed that for PSMA PET/CT and mpMRI, respectively, the pooled overall detection rates for recurrent PCa after definitive treatment were 0.69 (95% confidence interval [CI]: 0.45-0.89) and 0.70 (95% CI: 0.44-0.91). The detection rates for local recurrence were specifically 0.52 (95% CI: 0.39-0.65) and 0.62 (95% CI: 0.31-0.89), while they were 0.50 (95% CI: 0.26-0.74) and 0.32 (95% CI: 0.18-0.48) for lymph node metastasis. Notably, there was no discernible difference between the two imaging modalities in terms of the overall detection rate (P = 0.95). The detection rates for local recurrence and lymph node metastasis did not differ statistically significantly (P = 0.55, 0.23). CONCLUSION The performance of PSMA PET/CT and mpMRI in identifying biochemical recurrence in PCa appears to be comparable. However, the meta-analysis' findings came from research with modest sample sizes. In this context, more extensive research should be conducted in the future.
Collapse
Affiliation(s)
- Z Jiang
- Medical School, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China.
| | - T Yang
- Medical School, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China
| | - L Xu
- Medical School, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Fan C, Jiang Z, Teng C, Song X, Li L, Shen W, Jiang Q, Huang D, Lv Y, Du L, Wang G, Hu Y, Man S, Zhang Z, Gao N, Wang F, Shi T, Xin T. Efficacy and safety of intrathecal pemetrexed for TKI-failed leptomeningeal metastases from EGFR+ NSCLC: an expanded, single-arm, phase II clinical trial. ESMO Open 2024; 9:102384. [PMID: 38377785 PMCID: PMC11076967 DOI: 10.1016/j.esmoop.2024.102384] [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: 10/26/2023] [Revised: 01/06/2024] [Accepted: 01/19/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND This study aimed to evaluate the efficacy and safety of intrathecal pemetrexed (IP) for treating patients with leptomeningeal metastases (LM) from non-small-cell lung cancer (NSCLC) who progressed from epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment in an expanded, prospective, single-arm, phase II clinical study (ChiCTR1800016615). PATIENTS AND METHODS Patients with confirmed NSCLC-LM who progressed from TKI received IP (50 mg, day 1/day 5 for 1 week, then every 3 weeks for four cycles, and then once monthly) until disease progression or intolerance. Objectives were to assess overall survival (OS), response rate, and safety. Measurable lesions were assessed by investigator according to RECIST version 1.1. LM were assessed according to the Response Assessment in Neuro-Oncology (RANO) criteria. RESULTS The study included 132 patients; 68% were female and median age was 52 years (31-74 years). The median OS was 12 months (95% confidence interval 10.4-13.6 months), RANO-assessed response rate was 80.3% (106/132), and the most common adverse event was myelosuppression (n = 42; 31.8%), which reversed after symptomatic treatment. The results of subgroup analysis showed that absence of brain parenchymal metastasis, good Eastern Cooperative Oncology Group score, good response to IP treatment, negative cytology after treatment, and patients without neck/back pain/difficult defecation had longer survival. Gender, age, previous intrathecal methotrexate/cytarabine, and whole-brain radiotherapy had no significant influence on OS. CONCLUSIONS This study further showed that IP is an effective and safe treatment method for the EGFR-TKI-failed NSCLC-LM, and should be recommended for these patients in clinical practice and guidelines.
Collapse
Affiliation(s)
- C Fan
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Z Jiang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - C Teng
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - X Song
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - L Li
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - W Shen
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Q Jiang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - D Huang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Y Lv
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - L Du
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - G Wang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Y Hu
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - S Man
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Z Zhang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - N Gao
- Department of Oncology, Heilongjiang Sengong General Hospital, Harbin, People's Republic of China
| | - F Wang
- Department of Oncology, Heilongjiang Sengong General Hospital, Harbin, People's Republic of China
| | - T Shi
- Department of Oncology, Heilongjiang Sengong General Hospital, Harbin, People's Republic of China
| | - T Xin
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin.
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Su H, Xu Z, Bao MDL, Luo S, Liang JW, Pei W, Guan X, Liu Z, Jiang Z, Zhang MG, Zhao ZX, Jin WS, Zhou HT. [The clinical significance of lateral pelvic sentinel lymph node biopsy using indocyanine green fluorescence navigation in laparoscopic lateral pelvic lymph node dissection]. Zhonghua Zhong Liu Za Zhi 2024; 46:140-145. [PMID: 38418188 DOI: 10.3760/cma.j.cn112152-20231026-00265] [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] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Objectives: This study aims to explore the clinical significance of lateral pelvic sentinel lymph node biopsy (SLNB) using indocyanine green (ICG) fluorescence navigation in laparoscopic lateral pelvic lymph node dissection (LLND) and evaluate the accuracy and feasibility of this technique to predict the status of lateral pelvic lymph nodes (LPLNs). Methods: The clinical and pathological characteristics, surgical outcomes, lymph node findings and perioperative complications of 16 rectal cancer patients who underwent SLNB using ICG fluorescence navigation in laparoscopic LLND in the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College during April 2017 and October 2022 were retrospectively collected and analyzed. The patients did not receive preoperative neoadjuvant radiotherapy and presented with LPLNs but without LPLN enlargement (MRI showed the maximum short axes of the LPLNs were ≥5 mm and <10 mm at first visit). Results: All 16 patients were successfully performed SLNB using ICG fluorescence navigation in laparoscopic LLND. Three patients underwent bilateral LLND and 13 patients underwent unilateral LLND. The lateral pelvic sentinel lymph nodes (SLNs) were clearly fluorescent before dissection in 14 patients and the detection rate of SLNs for these patients was 87.5%. Lateral pelvic SLN metastasis was diagnosed in 2 patients and negative results were found in 12 patients by frozen pathological examinations. Among the 14 patients in whom lateral pelvic SLNs were detected, the dissected lateral pelvic non-SLNs were all negative. All dissected LPLNs were negative in two patients without fluorescent lateral pelvic SLNs. The specificity, sensitivity, negative predictive value, and accuracy was 85.7%, 100%, 100%, and 100%, respectively. Conclusions: This study indicates that lateral pelvic SLNB using ICG fluorescence navigation shows promise as a safe and feasible procedure with good accuracy. This technique may replace preventive LLND for locally advanced lower rectal cancer.
Collapse
Affiliation(s)
- H Su
- Department of Gastrointestinal Surgery, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Z Xu
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - M D L Bao
- Department of Pancreatic and Gastric Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - S Luo
- Department of Gastrointestinal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - J W Liang
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - W Pei
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - X Guan
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Z Liu
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Z Jiang
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - M G Zhang
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Z X Zhao
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - W S Jin
- Department of Anorectal Diseases, Third Medical Center of Chinese PLA General Hospital, Beijing 100039, China
| | - H T Zhou
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| |
Collapse
|
6
|
Chen C, Xu J, Jiang Z, Wu GH, Zhang YQ, Zhao Y, Wu ZY. [Association between CD4 +T lymphocyte and body composition with physical frailty among elderly HIV-infected patients in Chongqing City]. Zhonghua Yu Fang Yi Xue Za Zhi 2024; 58:235-240. [PMID: 38387956 DOI: 10.3760/cma.j.cn112150-20230822-00115] [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] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Objective: To identify the association between CD4+T lymphocyte (CD4) counts and physical frailty among HIV-infected people aged 65 years and older, and evaluate whether this association will be modified by the indicators of body composition. Methods: From May to October 2022, 485 elderly HIV-infected patients receiving antiretroviral therapy (ART) were recruited from 7 antiviral treatment sites in Jiangjin District Center for Disease Control and Prevention, Chongqing. The data of basic characteristics (age and gender), living habits (smoking and drinking) and disease history (metabolic diseases, cardiovascular and cerebrovascular diseases, respiratory disease and malignant tumors) were collected through the face-to-face investigation with self-made questionnaires. Fried Frailty Scale was used to evaluate the status of physical frailty. Physical fitness (walking speed, grip strength, height, and weight) and body composition (skeletal muscle mass, body fat mass, and basal metabolic rate) were measured. The antiretroviral treatment data were obtained from the China AIDS Integrated Prevention and Treatment Data information management system. The prevalence of physical frailty was calculated among the HIV-infected patients. The potential effects of CD4 counts on physical frailty were explored by using multivariate logistic regression. Subgroup analyses were repeated in the logistic regression with muscle mass, body fat mass, and other indicators of body composition as subgroup variables to determine whether the association might be modified by body composition. Results: The age of 485 patients were (72±5) years old, of which 48.2% (234 cases) were>70 years old and 70.9% (344 cases) were male, and all of whom had initiated the ART treatment. The prevalence of physical frailty among these patients was 7.4% (36/485). Multivariate logistic regression showed that after adjusting for age, sex, smoking, drinking, body composition index, ART duration, viral load and the number of comorbidities, increased CD4 cell level was associated with decreased prevalent risk of physical frailty among elderly HIV-infected patients. For every increase of 5.0×107 CD4 cells/L, the prevalent risk of physical frailty decreased by 12% [OR (95%CI): 0.88 (0.76-1.01)]. Compared with the low CD4 cell level group, the risk of physical frailty in those with normal CD4 cell level decreased by 69% [OR (95%CI): 0.31 (0.10-0.92)]. Subgroup analysis of body composition indicators showed that the protective effect of normal CD4 cell level on physical frailty was more pronounced in the high skeletal muscle mass and high basal metabolic rate group (Pinteraction<0.05). Conclusion: The prevalence of physical frailty among elderly HIV-infected patients is relatively lower in Chongqing, and the CD4 cell level, skeletal muscle mass and basal metabolic rate are related to physical frailty.
Collapse
Affiliation(s)
- C Chen
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - J Xu
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Z Jiang
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - G H Wu
- Chongqing Center for Disease Control and Prevention, Chongqing 400042, China
| | - Y Q Zhang
- Department of AIDS/STD Control and Prevention, Chongqing Jiangjin District Center for Disease Control and Prevention, Chongqing 402260, China
| | - Y Zhao
- Department of Treatment and Care, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Z Y Wu
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| |
Collapse
|
7
|
Wang H, Jiang Z, Guo Z, Luo G, Ding T, Zhan C. mIgM-mediated splenic marginal zone B cells targeting of folic acid for immunological evasion. Acta Pharm Sin B 2024; 14:808-820. [PMID: 38322341 PMCID: PMC10840397 DOI: 10.1016/j.apsb.2023.09.011] [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: 06/09/2023] [Revised: 08/14/2023] [Accepted: 08/28/2023] [Indexed: 02/08/2024] Open
Abstract
Folic acid is a fully oxidized synthetic folate with high bioavailability and stability which has been extensively prescribed to prevent congenital disabilities. Here we revealed the immunosuppressive effect of folic acid by targeting splenic marginal zone B (MZB) cells. Folic acid demonstrates avid binding with the Fc domain of immunoglobulin M (IgM), targeting IgM positive MZB cells in vivo to destabilize IgM-B cell receptor (BCR) complex and block immune responses. The induced anergy of MZB cells by folic acid provides an immunological escaping window for antigens. Covalent conjugation of folic acid with therapeutic proteins and antibodies induces immunological evasion to mitigate the production of anti-drug antibodies, which is a major obstacle to the long-term treatment of biologics by reducing curative effects and/or causing adverse reactions. Folic acid acts as a safe and effective immunosuppressant via IgM-mediated MZB cells targeting to boost the clinical outcomes of biologics by inhibiting the production of anti-drug antibodies, and also holds the potential to treat other indications that adverse immune responses need to be transiently shut off.
Collapse
Affiliation(s)
- Huan Wang
- Department of Pharmacology, School of Basic Medical Sciences & Department of Pharmacy, Shanghai Pudong Hospital, Fudan University, Shanghai 200032, China
- Department of Pharmaceutical Sciences, School of Pharmacy, Naval Medical University, Shanghai 200433, China
| | - Zhuxuan Jiang
- Department of Pharmacology, School of Basic Medical Sciences & Department of Pharmacy, Shanghai Pudong Hospital, Fudan University, Shanghai 200032, China
| | - Zhiwei Guo
- Department of Pharmacology, School of Basic Medical Sciences & Department of Pharmacy, Shanghai Pudong Hospital, Fudan University, Shanghai 200032, China
- Shanghai Engineering Research Center for Synthetic Immunology, Fudan University, Shanghai 200032, China
| | - Gan Luo
- Department of Pharmacology, School of Basic Medical Sciences & Department of Pharmacy, Shanghai Pudong Hospital, Fudan University, Shanghai 200032, China
| | - Tianhao Ding
- Department of Pharmacology, School of Basic Medical Sciences & Department of Pharmacy, Shanghai Pudong Hospital, Fudan University, Shanghai 200032, China
| | - Changyou Zhan
- Department of Pharmacology, School of Basic Medical Sciences & Department of Pharmacy, Shanghai Pudong Hospital, Fudan University, Shanghai 200032, China
- Shanghai Engineering Research Center for Synthetic Immunology, Fudan University, Shanghai 200032, China
| |
Collapse
|
8
|
Wu L, Ying J, Jiang Z, Zhang L, Cai Y, Zhou C, Xu Y, Lei S. Risk factors in ICU patients with initial acquisition of carbapenemase-resistant Klebsiella Pneumoniae. Int J Tuberc Lung Dis 2023; 27:899-905. [PMID: 38042974 DOI: 10.5588/ijtld.23.0043] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2023] Open
Abstract
OBJECTIVE: To identify the risk factors associated with antimicrobial use on the initial acquisition of carbapenem-resistant Klebsiella pneumoniae (CRKP) in elderly intensive care unit (ICU) patients.METHODS: Respiratory secretion, blood, urine, anal swab and peritoneal drainage samples from all elderly patients with non-colonised CRKP who had been hospitalised from January 2021 to December 2022 were collected, and screened for CRKP colonisation using surveillance culture at the time of the first ICU admission and weekly thereafter in Zhejiang Provincial Hospital of Chinese Medicine, Zhejiang, China. Cumulative antibiotic variables included duration of antibiotic use, total amount of antimicrobials received in grams, total antibiotic consumption (defined daily dose) and the types of antimicrobial exposure. A time-dependent model based on Cox regression analysis was used to investigate the effect of each variable on the initial acquisition of CRKP infection or colonisation.RESULTS: Of 214 patients, 44 were infected or had CRKP colonies and death rate was 34.1%. males were the risk factor for acquiring CRKP in culture (HR 2.12, 95% CI 1.06-4.21; P = 0.033). It is notable that the hazard of acquiring CRKP increased by 9% with every single-point increase in the APACHE II score (HR 1.09, 95% CI 1.01-1.18; P = 0.025). The hazard of acquiring CRKP doubled when carbapenems were administered (HR 1.81, 95% CI 1.42-2.30; P < 0.001), In contrast, exposure to quinolone antimicrobials had a smaller effect on acquiring CRKP (HR 1.07; 95% CI 1.01-1.14; P = 0.024).CONCLUSION: This study found that male sex, APACHE II score and exposure to quinolones and carbapenems were independent risk factors for acquiring CRKP.
Collapse
Affiliation(s)
- L Wu
- Departments of Respiratory and Critical Care Medicine, and
| | - J Ying
- Departments of Obstetrics and Gynecology, The Affiliated Cangnan Hospital of Wenzhou Medical University, Cangnan, Zhejiang
| | - Z Jiang
- Department of Emergency Medicine, The Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang
| | - L Zhang
- Departments of Respiratory and Critical Care Medicine, and
| | - Y Cai
- Departments of Respiratory and Critical Care Medicine, and
| | - C Zhou
- Departments of Respiratory and Critical Care Medicine, and
| | - Y Xu
- Department of Cardiology, Hangzhou Ninth People's Hospital, Hangzhou, Zhejiang
| | - S Lei
- Intensive Care Unit, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| |
Collapse
|
9
|
Kuai YX, Li M, Jiang Z, Chen J, Bai ZJ, Li XZ, Lu GP, Li YH. [Comparison of diagnostic criteria for acute kidney injury in critically ill children]. Zhonghua Er Ke Za Zhi 2023; 61:1011-1017. [PMID: 37899340 DOI: 10.3760/cma.j.cn112140-20230623-00418] [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] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
Objective: The kidney disease: improving global outcome (KDIGO) and pediatric reference change value optimized for acute kidney injury (pROCK) criteria were used to evaluate the incidence, stages and mortality of acute kidney injury (AKI). The differences between the 2 criteria were compared for exploring the value of pROCK criteria in diagnosing pediatric AKI and predicting adverse outcomes. Methods: In the multicenter prospective clinical cohort study, we collected general data and clinical data such as serum creatinine values from 1 120 children admitted to 4 PICUs of Children's Hospital of Soochow University, Children's Hospital of Fudan University, Anhui Provincial Children's Hospital, and Xuzhou Children's Hospital from September 2019 to February 2021. AKI was defined and staged according to the KDIGO and pROCK criteria. The incidence of AKI, the consistency of AKI definite diagnosis and stages, and the mortality in PICU were compared between the 2 groups. The chi-square test or Fisher's exact test was applied for comparison between 2 groups. The Cohen's Kappa and Weighted Kappa analyses were used for evaluating diagnostic consistency. The Cox regression analysis was used to evaluate the correlation between AKI and mortality. Results: A total of 1 120 critically ill children were included, with an age of 33 (10, 84) months. There are 668 boys and 452 girls. The incidence of AKI defined by the KDIGO guideline was higher than that defined by pROCK criteria (27.2%(305/1 120), 14.7%(165/1 120), χ2=52.78, P<0.001). The concordance rates of the 2 criteria for the diagnosis of AKI and AKI staging were 87.0% (κ=0.62) and 79.7% (κ=0.58), respectively. Totally 63 infants with AKI stage 1 defined by the KDIGO guideline were redefined as non-AKI by following the pROCK criteria. The PICU mortality rate of these infants was similar to patients without AKI defined by KDIGO guideline(P=0.761). After adjusting for confounders, AKI defined by KDIGO or pROCK criteria was an independent risk factor of death in PICU (AHR=2.04, 2.73,95%CI 1.27-3.29, 1.74-4.28, both P<0.01), and the risk of death was higher when using the pROCK compared with the KDIGO criteria. As for the KDIGO criteria, mild AKI was not associated with the mortality in PICU (P=0.702), while severe AKI was associated with increased mortality (P<0.001). As for the pROCK criteria, both mild and severe AKI were risk factors of PICU death in children (HR=3.51, 6.70, 95%CI 1.94-6.34, 4.30-10.44, both P<0.001). In addition, The AKI severity was positively associated with the mortality. Conclusions: The AKI incidence and staging varied depending on the used diagnostic criteria. The KDIGO definition is more sensitive, while the pROCK-defined AKI is more strongly associated with high mortality rate.
Collapse
Affiliation(s)
- Y X Kuai
- Department of Nephrology and Immunology, Children's Hospital of Soochow University, Suzhou 215000, China
| | - M Li
- Pediatric Intensive Care Unit, Anhui Provincial Children's Hospital, Hefei 230002, China
| | - Z Jiang
- Pediatric Intensive Care Unit, Xuzhou Children's Hospital, Xuzhou 221002, China
| | - J Chen
- Pediatric Intensive Care Unit, Children's Hospital of Soochow University, Suzhou 215000, China
| | - Z J Bai
- Pediatric Intensive Care Unit, Children's Hospital of Soochow University, Suzhou 215000, China
| | - X Z Li
- Department of Nephrology and Immunology, Children's Hospital of Soochow University, Suzhou 215000, China
| | - G P Lu
- Pediatric Intensive Care Unit, Children's Hospital of Fudan University, Shanghai 201102, China
| | - Y H Li
- Department of Nephrology and Immunology, Children's Hospital of Soochow University, Suzhou 215000, China
| |
Collapse
|
10
|
Jiang Z, Xu XL, Zhuang PY. [Frontier technology and research progress in the diagnostics and therapeutics of voice diseases: report from the Voice Foundation 52nd Anniversary Symposium]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:1024-1028. [PMID: 37840170 DOI: 10.3760/cma.j.cn115330-20230619-00289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Affiliation(s)
- Z Jiang
- Department of Voice Medicine, Zhongshan Hospital, Xiamen University; Key Laboratory of Voice of Xiamen City, Xiamen 361004, China
| | - X L Xu
- Department of Voice Medicine, Zhongshan Hospital, Xiamen University; Key Laboratory of Voice of Xiamen City, Xiamen 361004, China
| | - P Y Zhuang
- Department of Voice Medicine, Zhongshan Hospital, Xiamen University; Key Laboratory of Voice of Xiamen City, Xiamen 361004, China
| |
Collapse
|
11
|
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.
Collapse
|
12
|
Li Y, Zhang J, Cai W, Wang C, Yu Z, Jiang Z, Lai K, Wang Y, Yang G. CREB3L2 Regulates Hemidesmosome Formation during Epithelial Sealing. J Dent Res 2023; 102:1199-1209. [PMID: 37555472 DOI: 10.1177/00220345231176520] [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] [Indexed: 08/10/2023] Open
Abstract
The long-term success rate of dental implants can be improved by establishing a favorable biological sealing with a high-quality epithelial attachment. The application of mesenchymal stem cells (MSCs) holds promise for facilitating the soft tissue integration around implants, but the molecular mechanism is still unclear and the general application of MSC sheet for soft tissue integration is also relatively unexplored. We found that gingival tissue-derived MSC (GMSC) sheet treatment significantly promoted the expression of hemidesmosome (HD)-related genes and proteins in gingival epithelial cells (GECs). The formation of HDs played a key role in strengthening peri-implant epithelium (PIE) sealing. Further, high-throughput transcriptome sequencing showed that GMSC sheet significantly upregulated the PI3K/AKT pathway, confirming that cell adhesion and HD expression in GECs were regulated by GMSC sheet. We observed that the expression of transcription factor CREB3L2 in GECs was downregulated. After treatment with PI3K pathway inhibitor LY294002, CREB3L2 messenger RNA and protein expression levels were upregulated. Further experiments showed that overexpression or knockdown of CREB3L2 could significantly inhibit or promote HD-related genes and proteins, respectively. We confirmed that CREB3L2 was a transcription factor downstream of the PI3K/AKT pathway and participated in the formation of HDs regulated by GMSC sheet. Finally, through the establishment of early implant placement model in rats, we clarified the molecular function of CREB3L2 in PIE sealing as a mechanical transmission molecule in GECs. The application of GMSC sheet-implant complex could enhance the formation of HDs at the implant-PIE interface and decrease the penetration distance of horseradish peroxidase between the implant and PIE. Meanwhile, GMSC sheet reduced the length of CREB3L2 protein expression on PIE. These findings elucidate the potential function and molecular mechanism of MSC sheet regulating the epithelial sealing around implants, providing new insights and ideas for the application of stem cell therapy in regenerative medicine.
Collapse
Affiliation(s)
- Y Li
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center of Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - J Zhang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center of Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - W Cai
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center of Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - C Wang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center of Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Z Yu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center of Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Z Jiang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center of Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - K Lai
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center of Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Y Wang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center of Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - G Yang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center of Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| |
Collapse
|
13
|
Lang Y, Jiang Z, Sun L, Xiang L, Ren L. Hybrid-Supervised Deep Learning for Proton-Acoustic Reconstruction for 3D In Vivo Proton Dose Verification. Int J Radiat Oncol Biol Phys 2023; 117:e682-e683. [PMID: 37786007 DOI: 10.1016/j.ijrobp.2023.06.2145] [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) Proton-acoustic (PA) image has shown great potential to provide real-time 3D dose verification of proton therapy. However, the PA image quality suffers from severe limited view artifacts, which significantly impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for PA reconstruction to address the limited-view issues. MATERIALS/METHODS Our method consists of two stages. In the first stage, a transformer-based network was proposed to reconstruct initial pressure maps from protoacoustic signals. The network was first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the second stage, the PA image was further enhanced by a 3D U-net. The final PA images were converted to dose maps using conversion coefficients derived from CT images. Data from 126 prostate cancer patients treated by proton therapy were collected under an IRB protocol and were split into 86 and 40 patients for model training and testing, respectively. Data of each patient contains the planning CT scan, the corresponding clinical treatment plan, and the dose map calculated by commercial software. The radiofrequency signals were generated by performing proton acoustic simulation based on CT images and the ground truth pressure map derived from the treatment plan. An ultrasound detector matrix with 64 × 64 size and 500kHz central frequency was simulated under the perineum to acquire the signals in the prostate area. In the testing results, the method's accuracy was evaluated using Root-mean-squared-error (RMSE) and structural-similarity-index-measure (SSIM) between the reconstructed and ground truth pressure map and dose distribution. RESULTS Testing results showed that the reconstructed pressure map achieved an average RMSE/SSIM of 0.0292/0.96, demonstrating excellent 3D information with details. Dose maps derived from the pressure map achieved an average RMSE/SSIM of 0.018/0.99 with a gamma index of 94.7% and 95.7% for 1%/3 mm and 1%/5 mm criteria compared to the ground truth dose maps. The reconstruction time was 6s, which can be further reduced using GPU. CONCLUSION Our study achieves start-of-the-art performance in the challenging task of direct reconstruction from limited-view radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in-vivo 3D proton dose verification. Such high-precision 3D online dose verification can substantially reduce the range uncertainties of proton therapy to significantly improve its precision and outcomes.
Collapse
Affiliation(s)
- Y Lang
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD
| | | | - L Sun
- University of California, Irvine, CA
| | - L Xiang
- University of California, Irvine, CA
| | - L Ren
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD
| |
Collapse
|
14
|
Zhang G, Jiang Z, Wang L. A Radiotherapy Positioning Method for Both Coarse Guidance and Precise Verification Based on Integration of AR and Optical Surface Imaging. Int J Radiat Oncol Biol Phys 2023; 117:e743-e744. [PMID: 37786156 DOI: 10.1016/j.ijrobp.2023.06.2280] [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) Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice. We proposed a noninvasive radiotherapy positioning method integrating augmented reality (AR) and optical surface, and evaluated its feasibility in clinical workflow. MATERIALS/METHODS AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, i) the AR-based coarse guidance. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. ii) optical surface-based precise verification. The point clouds were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in 6 degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, i) quality assurance workflow, ii) errors of both steps and correlation analysis, and iii) receiver operating characteristic (ROC). RESULTS The maximum errors in phantom evaluation were 3.4±2.5 mm in Vrt and 1.4±1.0° in Pitch for the coarse guidance step, while 1.6±0.9 mm in Vrt and 0.6±0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6±1.3 vs. 1.7±1.1, p<0.01), Lng (2.4±1.3 vs. 1.4±0.1, p<0.01) and Rtn (0.8±0.5 vs. 0.6±0.4, p = 0.03) while relatively similar in Lat (1.7±1.0 vs. 1.9±1.1, p = 0.13). CONCLUSION The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.
Collapse
Affiliation(s)
- G Zhang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji'nan, China
| | - Z Jiang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji'nan, China
| | - L Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| |
Collapse
|
15
|
Yang FL, Chen X, Zheng F, Liu XX, Sun N, Li RQ, Jiang Z, Han J, Yang J. [Targeting microRNA-125b inhibited the metastasis of Alisertib resistance cells through mediating p53 pathway]. Zhonghua Zhong Liu Za Zhi 2023; 45:499-507. [PMID: 37355468 DOI: 10.3760/cma.j.cn112152-20200511-00438] [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] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
Objective: To clarify the mechanisms involvement in Alisertib-resistant colorectal cells and explore a potential target to overcome Alisertib-resistance. Methods: Drug-resistant colon cancer cell line (named as HCT-8-7T cells) was established and transplanted into immunodeficient mice. The metastasis in vivo were observed. Proliferation and migration of HCT-8-7T cells and their parental cells were assessed by colony formation and Transwell assay, respectively. Glycolytic capacity and glutamine metabolism of cells were analyzed by metabolism assays. The protein and mRNA levels of critical factors which are involved in mediating glycolysis and epithelial-mesenchymal transition (EMT) were examined by western blot and reverse transcription-quantitative real-time polymerase chain reaction(RT-qPCR), respectively. Results: In comparison with the mice transplanted with HCT-8 cells, which were survival with limited metastatic tumor cells in organs, aggressive metastases were observed in liver, lung, kidney and ovary of HCT-8-7T transplanted mice (P<0.05). The levels of ATP [(0.10±0.01) mmol/L], glycolysis [(81.77±8.21) mpH/min] and the capacity of glycolysis [(55.50±3.48) mpH/min] in HCT-8-7T cells were higher than those of HCT-8 cells [(0.04±0.01) mmol/L, (27.77±2.55) mpH/min and(14.00±1.19) mpH/min, respectively, P<0.05]. Meanwhile, the levels of p53 protein and mRNA in HCT-8-7T cells were potently decreased as compared to that in HCT-8 cells (P<0.05). However, the level of miRNA-125b (2.21±0.12) in HCT-8-7T cells was significantly elevated as compared to that in HCT-8 cells (1.00±0.00, P<0.001). In HCT-8-7T cells, forced-expression of p53 reduced the colon number (162.00±24.00) and the migration [(18.53±5.67)%] as compared with those in cells transfected with control vector [274.70±40.50 and (100.00±29.06)%, P<0.05, respectively]. Similarly, miR-125b mimic decreased the glycolysis [(25.28±9.51) mpH/min] in HCT-8-7T cells as compared with that [(54.38±12.70)mpH/min, P=0.003] in HCT-8-7T cells transfected with control. Meanwhile, in comparison with control transfected HCT-8-7T cells, miR-125b mimic also significantly led to an increase in the levels of p53 and β-catenin, in parallel with a decrease in the levels of PFK1 and HK1 in HCT-8-7T cells (P<0.05). Conclusions: Silencing of p53 by miR-125b could be one of the mechanisms that contributes to Alisertib resistance. Targeting miR-125b could be a strategy to overcome Alisertib resistance.
Collapse
Affiliation(s)
- F L Yang
- Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu Province Key Laboratory of Immunity and Metabolism, National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University, Xuzhou 221000, China
| | - X Chen
- Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu Province Key Laboratory of Immunity and Metabolism, National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University, Jiangsu International Joint Laboratory for Immunology and Metabolism, Xuzhou 221000, China
| | - F Zheng
- Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu Province Key Laboratory of Immunity and Metabolism, National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University, Xuzhou 221000, China
| | - X X Liu
- Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu Province Key Laboratory of Immunity and Metabolism, National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University, Jiangsu International Joint Laboratory for Immunology and Metabolism, Xuzhou 221000, China
| | - N Sun
- Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu Province Key Laboratory of Immunity and Metabolism, National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University, Jiangsu International Joint Laboratory for Immunology and Metabolism, Xuzhou 221000, China
| | - R Q Li
- Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu Province Key Laboratory of Immunity and Metabolism, National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University, Jiangsu International Joint Laboratory for Immunology and Metabolism, Xuzhou 221000, China
| | - Z Jiang
- Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu Province Key Laboratory of Immunity and Metabolism, National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University, Xuzhou 221000, China
| | - J Han
- Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu Province Key Laboratory of Immunity and Metabolism, National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University, Jiangsu International Joint Laboratory for Immunology and Metabolism, Xuzhou 221000, China
| | - J Yang
- Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Jiangsu Province Key Laboratory of Immunity and Metabolism, National Experimental Teaching Demonstration Center of Basic Medicine, Xuzhou Medical University, Jiangsu International Joint Laboratory for Immunology and Metabolism, Xuzhou 221000, China
| |
Collapse
|
16
|
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: 6] [Impact Index Per Article: 6.0] [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.
Collapse
|
17
|
Liu L, Zhu M, Wang Y, Wan B, Jiang Z. [Molecular pathological mechanism of liver metabolic disorder in mice with severe spinal muscular atrophy]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:852-858. [PMID: 37313828 DOI: 10.12122/j.issn.1673-4254.2023.05.22] [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] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To explore the molecular pathological mechanism of liver metabolic disorder in severe spinal muscular atrophy (SMA). METHODS The transgenic mice with type Ⅰ SMA (Smn-/- SMN20tg/2tg) and littermate control mice (Smn+/- SMN20tg/2tg) were observed for milk suckling behavior and body weight changes after birth. The mice with type Ⅰ SMA mice were given an intraperitoneal injection of 20% glucose solution or saline (15 μL/12 h), and their survival time was recorded. GO enrichment analysis was performed using the RNA-Seq data of the liver of type Ⅰ SMA and littermate control mice, and the results were verified using quantitative real-time PCR. Bisulfite sequencing was performed to examine CpG island methylation level in Fasn gene promoter region in the liver of the neonatal mice. RESULTS The neonatal mice with type Ⅰ SMA showed normal milk suckling behavior but had lower body weight than the littermate control mice on the second day after birth. Intraperitoneal injection of glucose solution every 12 h significantly improved the median survival time of type Ⅰ SMA mice from 9±1.3 to 11± 1.5 days (P < 0.05). Analysis of the RNA-Seq data of the liver showed that the expression of the target genes of PPARα related to lipid metabolism and mitochondrial β oxidation were down-regulated in the liver of type Ⅰ SMA mice. Type Ⅰ SMA mice had higher methylation level of the Fasn promoter region in the liver than the littermate control mice (76.44% vs 58.67%). In primary cultures of hepatocytes from type Ⅰ SMA mice, treatment with 5-AzaC significantly up-regulated the expressions of the genes related to lipid metabolism by over 1 fold (P < 0.01). CONCLUSION Type Ⅰ SMA mice have liver metabolic disorder, and the down-regulation of the target genes of PPARα related to lipid and glucose metabolism due to persistent DNA methylation contributes to the progression of SMA.
Collapse
Affiliation(s)
- L Liu
- Suzhou Medical College of Soochow University, Suzhou 215000, China
| | - M Zhu
- Suzhou Medical College of Soochow University, Suzhou 215000, China
| | - Y Wang
- Suzhou Medical College of Soochow University, Suzhou 215000, China
| | - B Wan
- Suzhou Medical College of Soochow University, Suzhou 215000, China
| | - Z Jiang
- Suzhou Medical College of Soochow University, Suzhou 215000, China
| |
Collapse
|
18
|
Zhan K, Zhang X, Wang B, Jiang Z, Fang X, Yang S, Jia H, Li L, Cao G, Zhang K, Ma X. Response to: COVID-19 and diabetes-double whammy. QJM 2023; 116:144-145. [PMID: 35178559 DOI: 10.1093/qjmed/hcac048] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 02/10/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- K Zhan
- College of Public Health, Southwest Medical University, Luzhou, Sichuan, China
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - X Zhang
- Department of General Surgery, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - B Wang
- Pulmonary and Critical Care Medicine Center, Chinese PLA Respiratory Disease Institute, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Z Jiang
- Yidu Cloud Technology Co. Ltd, Beijing, China
| | - X Fang
- College of Public Health, Southwest Medical University, Luzhou, Sichuan, China
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - S Yang
- Department of Infectious Diseases, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - H Jia
- College of Public Health, Southwest Medical University, Luzhou, Sichuan, China
| | - L Li
- Department of Respiratory Medicine, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - G Cao
- Department of Respiratory Medicine, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - K Zhang
- Department of Outpatients, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - X Ma
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| |
Collapse
|
19
|
Zhan K, Zhang X, Wang B, Jiang Z, Fang X, Yang S, Jia H, Li L, Cao G, Zhang K, Ma X. Response to: Glycemic control and COVID-19 outcomes: the missing metabolic players. QJM 2023; 116:91-92. [PMID: 35166838 PMCID: PMC9383446 DOI: 10.1093/qjmed/hcac044] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 02/07/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- K Zhan
- From the College of Public Health, Southwest Medical University, Xianglin street 1, Luzhou, Sichuan 646000, China
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China
| | - X Zhang
- Department of General Surgery, Daping Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China
| | - B Wang
- Pulmonary and Critical Care Medicine Center, Chinese PLA Respiratory Disease Institute, Xinqiao Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China
| | - Z Jiang
- Yidu Cloud Technology Co. Ltd, North Huayuan Road 35, Beijing 100071, China
| | - X Fang
- From the College of Public Health, Southwest Medical University, Xianglin street 1, Luzhou, Sichuan 646000, China
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China
| | - S Yang
- Department of Infectious Diseases, Southwest Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China
| | - H Jia
- From the College of Public Health, Southwest Medical University, Xianglin street 1, Luzhou, Sichuan 646000, China
| | - L Li
- Department of Respiratory Medicine, Daping Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China
| | - G Cao
- Department of Respiratory Medicine, Daping Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China
| | - K Zhang
- Department of Outpatients, Daping Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China
| | - X Ma
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China
- Address correspondence to X. Ma, Department of General Surgery, Daping Hospital, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China. ,
| |
Collapse
|
20
|
Wang LL, Hong H, Zhang YR, Shi HB, Chen L, Jiang HB, Jiang Z, Wu Z. [Cost-effectiveness prediction of AIDS interventions among men who have sex with men in Ningbo]. Zhonghua Liu Xing Bing Xue Za Zhi 2022; 43:2008-2014. [PMID: 36572477 DOI: 10.3760/cma.j.cn112338-20220410-00275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Objective: To provide information reference for resource allocation and decision-making in related fields, the cost-effectiveness of HIV input among men who have sex with men (MSM) in Ningbo. Different intervention coverages were compared. Methods: Taking MSM as the target population, data were collected and modeled by Optima HIV for the corresponding HIV health output and the budget under different intervention coverages. Results: According to the estimated size of the MSM population, which was 19 584 in Ningbo in 2020, if the coverage of 2020 baseline intervention is maintained in the next ten years, the number of HIV cases, new HIV infections, and HIV-related deaths among this population will show an upward trend. It is estimated that from 2021 to 2030, 7.9% of new infections and 1.7% of deaths can be avoided and the relevant funding investment comed to 2.4 time the baseline if the intervention coverage rate expanded to 3.0 times the 2020 baseline. After the coverage rate of intervention expanded to 3 times the baseline, it continued to grow, the health effect did not increase. Conclusions: At present, expanding the baseline coverage of HIV-related intervention projects among MSM in Ningbo and increasing capital investment will still reverse HIV-related death and reduce new infections. Moreover, there is a saturation point of the intervention effect. Researchers and policymakers must explore more effective interventions/combinations to obtain more significant health outcomes.
Collapse
Affiliation(s)
- L L Wang
- School of Health Management, Anhui Medical University, Hefei 230032, China
| | - H Hong
- Ningbo municipal Center for Disease Control and Prevention, Ningbo 315010, China
| | - Y R Zhang
- School of Health Management, Anhui Medical University, Hefei 230032, China
| | - H B Shi
- Ningbo municipal Center for Disease Control and Prevention, Ningbo 315010, China
| | - L Chen
- Department of HIV/AIDS and STDS Control and Prevention, Zhejiang Center for Disease Control and Prevention, Hangzhou 310000, China
| | - H B Jiang
- Ningbo municipal Center for Disease Control and Prevention, Ningbo 315010, China
| | - Z Jiang
- Division of Health Education and Behavioral Intervention, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Zunyou Wu
- Division of Health Education and Behavioral Intervention, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| |
Collapse
|
21
|
Pinzón-Arteaga C, Wang Y, Wei Y, Scatolin G, Liu L, Yu L, Jiang Z, Wu J. 234 Bovine blastocyst-like structures derived from pluripotent stem cell cultures. Reprod Fertil Dev 2022. [DOI: 10.1071/rdv35n2ab234] [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: 12/09/2022] Open
|
22
|
Scatolin G, Wang Y, Zhu L, Gutierrez-Castillo E, Jiang Z. 92 A single cell atlas of bovine peri-implantation embryo development. Reprod Fertil Dev 2022. [DOI: 10.1071/rdv35n2ab92] [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: 12/12/2022] Open
|
23
|
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.
Collapse
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.
| |
Collapse
|
24
|
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.
Collapse
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
| | | |
Collapse
|
25
|
Yue JL, Jiang Z, Sun RJ, Fu B, Zhang HD, Pan XL, Liu DY. [Giant esophageal tumor presenting as pharyngeal mass: a report of three cases]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2022; 57:1341-1343. [PMID: 36404662 DOI: 10.3760/cma.j.cn115330-20220321-00124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Affiliation(s)
- J L Yue
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University (Qingdao), Qingdao 266035, China National Health Commission Key Laboratory of Otorhinolaryngology(Shandong University), Jinan 250012, China
| | - Z Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University (Qingdao), Qingdao 266035, China National Health Commission Key Laboratory of Otorhinolaryngology(Shandong University), Jinan 250012, China
| | - R J Sun
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University (Qingdao), Qingdao 266035, China
| | - B Fu
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University (Qingdao), Qingdao 266035, China National Health Commission Key Laboratory of Otorhinolaryngology(Shandong University), Jinan 250012, China
| | - H D Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University (Qingdao), Qingdao 266035, China
| | - X L Pan
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University (Qingdao), Qingdao 266035, China National Health Commission Key Laboratory of Otorhinolaryngology(Shandong University), Jinan 250012, China
| | - D Y Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University (Qingdao), Qingdao 266035, China National Health Commission Key Laboratory of Otorhinolaryngology(Shandong University), Jinan 250012, China
| |
Collapse
|
26
|
Jiang Z, Liang Y, Wang X, Zhuang M, Feng M, Kuang Y. A Radiomics-Based Light Gradient Boosting Machine to Predict Radiation-Induced Toxicities in Nasopharynx Cancer Patients Receiving Chemoradiotherapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.933] [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: 10/31/2022]
|
27
|
Li J, Li X, Jiang Z, Hu C, Liu J, Huo J, Liu B. Predicting the probability of malignant pathological type of kidney cancer based on mass size: A retrospective study. Prog Urol 2022; 32:849-855. [PMID: 36068150 DOI: 10.1016/j.purol.2022.08.007] [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: 11/19/2021] [Revised: 07/30/2022] [Accepted: 08/11/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Different degrees of malignancy of renal cell carcinoma (RCC) correspond to dissimilar therapies, and the prediction of malignancy of kidney cancer based on tumor size is still not fully studied. METHODS We evaluated a total of 50,776 patients with T1-T2, N0, M0 RCC diagnosed between 2004 to 2015 based on the Surveillance, Epidemiology, and End Results database. Three and four fuhrman grade clear cell RCC, three and four fuhrman grade papillary RCC, collecting duct RCC, sarcomatoid differentiation RCC and unclassified RCC were classified as aggressive RCC. The other RCC was classified as indolent RCC. The probability of aggressive and indolent was estimated according to tumor size using a logistic regression model. Differences in survival between subgroups were assessed using the Kaplan-Meier method. RESULTS There were 38,003 cases of indolent tumor and 12,773 cases of aggressive tumor totally. As tumor size increases, the predicted probability of an aggressive tumor also increases. Concretely, kidney cancers of 2cm, 3cm and 4cm were estimated to be 19.6%, 21.6% and 23.7% more likely to be aggressive. And for the same tumor size, clear cell RCC in men is more likely to be invasive relative to women and other kidney cancer pathology types. In addition, both the overall and tumor-specific survival are longer for indolent tumors than for aggressive tumors. CONCLUSION We evaluated the degree of malignancy of different sizes RCC in a retrospective study. This result may be helpful in the choice of initial therapy strategies for kidney cancer patients.
Collapse
Affiliation(s)
- J Li
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China; Jiangsu Province Academy of Traditional Chinese Medicine, Jiangsu, China
| | - X Li
- Kunshan Hospital of Traditional Chinese Medicine, Kunshan, Jiangsu, China
| | - Z Jiang
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China; Jiangsu Province Academy of Traditional Chinese Medicine, Jiangsu, China
| | - C Hu
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China; Jiangsu Province Academy of Traditional Chinese Medicine, Jiangsu, China
| | - J Liu
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China; Jiangsu Province Academy of Traditional Chinese Medicine, Jiangsu, China
| | - J Huo
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China; Jiangsu Province Academy of Traditional Chinese Medicine, Jiangsu, China
| | - B Liu
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, 321, zhongshan Road, 210008 Nanjing, China.
| |
Collapse
|
28
|
Subramanian J, Gregg J, Berktas M, Jiang Z, Li J, Taylor A, Leighl N. EP08.02-080 EGFR Testing Practices, Treatment Choice and Clinical Outcomes in Advanced NSCLC in a Real-World Setting. J Thorac Oncol 2022. [DOI: 10.1016/j.jtho.2022.07.762] [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]
|
29
|
Wang H, Lin S, Wang S, Jiang Z, Ding T, Wei X, Lu Y, Yang F, Zhan C. Folic Acid Enables Targeting Delivery of Lipodiscs by Circumventing IgM-Mediated Opsonization. Nano Lett 2022; 22:6516-6522. [PMID: 35943299 DOI: 10.1021/acs.nanolett.2c01509] [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] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Folic acid (FA) is one of the most widely utilized small-molecule ligands for cancer targeted drug delivery. Natural IgM was recently found to avidly absorb on the surface of FA-functionalized liposomes (FA-sLip), negatively regulating the in vivo performance by efficiently activating complement. Herein, FA-functionalized lipodiscs (FA-Disc) were constructed to successfully circumvent IgM-mediated opsonization and retained binding activity with folate receptors in vivo. The FA moiety along with the bound IgM was restricted to the highly curved rim of lipodiscs, leading to IgM incapability of presenting the membrane-bound conformation to trigger complement activation. The C1q docking, C3 binding, and C5a release were blocked and accelerated blood clearance phenomenon was mitigated of FA-Disc. FA-Disc retained folate binding activity and could effectively target folate receptor positive tumors in vivo. The present study provides a useful solution to avoid the negative regulation by IgM and achieve FA-enabled targeting by exploring disc-shaped nanocarriers.
Collapse
Affiliation(s)
- Huan Wang
- School of Pharmacy, Naval Medical University, Shanghai 200433, P.R. China
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University Shanghai Engineering Research Center for Synthetic Immunology, Shanghai 201399, P.R. China
| | - Shiqi Lin
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University Shanghai Engineering Research Center for Synthetic Immunology, Shanghai 201399, P.R. China
| | - Songli Wang
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University Shanghai Engineering Research Center for Synthetic Immunology, Shanghai 201399, P.R. China
| | - Zhuxuan Jiang
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University Shanghai Engineering Research Center for Synthetic Immunology, Shanghai 201399, P.R. China
| | - Tianhao Ding
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University Shanghai Engineering Research Center for Synthetic Immunology, Shanghai 201399, P.R. China
| | - Xiaoli Wei
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University Shanghai Engineering Research Center for Synthetic Immunology, Shanghai 201399, P.R. China
| | - Ying Lu
- School of Pharmacy, Naval Medical University, Shanghai 200433, P.R. China
| | - Feng Yang
- School of Pharmacy, Naval Medical University, Shanghai 200433, P.R. China
| | - Changyou Zhan
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University Shanghai Engineering Research Center for Synthetic Immunology, Shanghai 201399, P.R. China
| |
Collapse
|
30
|
Zhan K, Zhang X, Wang B, Jiang Z, Fang X, Yang S, Jia H, Li L, Cao G, Zhang K, Ma X. Response to: Comment on short- and long-term prognosis of glycemic control in COVID-19 patients with type 2 diabetes. QJM 2022; 115:569-570. [PMID: 35789280 PMCID: PMC9384456 DOI: 10.1093/qjmed/hcac162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Indexed: 12/03/2022] Open
Affiliation(s)
| | | | | | - Z Jiang
- Yidu Cloud Technology Co. Ltd., Beijing, China
| | - X Fang
- Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - S Yang
- Department of Infectious Diseases, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - H Jia
- From the College of Public Health, Southwest Medical University, Luzhou, Sichuan, China
| | - L Li
- Department of Respiratory Medicine, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - G Cao
- Department of Respiratory Medicine, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - K Zhang
- Department of Outpatients, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - X Ma
- Address correspondence to X. Ma, Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Gaotanyan Street 30, Shapingba District, Chongqing 400038, China. ,
| |
Collapse
|
31
|
Han YC, Sun PC, Jiang Z, Fan ZM, Wang HB. [The surgical management of benign tumors of the lateral skull base with intracranial invasion: experience from a single centre over ten years]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2022; 57:810-818. [PMID: 35866273 DOI: 10.3760/cma.j.cn115330-20210630-00406] [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] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To investigate the clinical features, pathological types, imaging features, and surgical strategies of lateral skull base benign tumors with intracranial invasion. Methods: From January 2011 to March 2021, 36 patients of lateral skull base benign tumors with intracranial invasion were included in this retrospective study. Among the 36 patients, 14 cases were male, 22 cases were female, the aged range from 20-67, with the median age of 48. The clinical manifestations, characteristic imaging findings, pathological types, surgical approach selection, and prognosis were analyzed. Results: 36 cases of lateral skull base tumors with intracranial invasion were all accepted surgeries. 23 cases were neurogenic tumors, facial nerve tumors (n=8), neurogenic tumors in jugular foramen with unknown origin(n=6), hypoglossal schwannoma (n=3), transotic intralabyrinthine schwannoma (n=3), vestibular schwannoma involving the middle ear(n=2), vagal nerve schwannoma(n=1). Other types of tumors included meningioma (n=10) and paraganglioma (Di 1 or 2,n=3). Different pathological types of tumors had different clinical manifestations and imaging manifestations. Sixteen cases were subjected to primary resection, while, other 20 cases underwent staged operation. Among the patients with staged operation, 10 patients had completed the second stage operation, five patients were waiting for the second stage operation, the other five patient's residual intracranial tumor were significantly reduced and the space between tumor and brain tissues widened after the first stage operation, so, the following up with "wait and scan"policy was suggested. The total resection rate of tumors was related to the pathological nature, in which neurogenic tumors were 15/17, and meningiomas were 5/8. The main postoperative complications were cerebrospinal fluid leakage and infection in the operation area. There were two cases of postoperative intracranial infection, and three cases of cerebrospinal fluid leakage occurred in non staged operation cases. Conclusions: Lateral skull base tumors with intracranial invasion are rare. The most common pathological type is schwannoma, followed by meningioma and paraganglioma. For this type of tumor, if there is infection in the operation area and neck invasion is large, it is suggested to choose staged surgery, which can reduce the risk of intracranial infection and the incidence of cerebrospinal fluid leakage. Staged surgery strategy can also reduce the difficulty of second stage surgery, so the operation is much safer than non staged surgery.
Collapse
Affiliation(s)
- Y C Han
- Department of Neurotology and Lateral Skull Base Surgery, Shandong Provincial ENT Hospital, Shandong Institute of Otorhinolaryngology, Jinan 250022, China
| | - P C Sun
- Department of Neurotology and Lateral Skull Base Surgery, Shandong Provincial ENT Hospital, Shandong Institute of Otorhinolaryngology, Jinan 250022, China
| | - Z Jiang
- Department of Neurotology and Lateral Skull Base Surgery, Shandong Provincial ENT Hospital, Shandong Institute of Otorhinolaryngology, Jinan 250022, China
| | - Z M Fan
- Department of Neurotology and Lateral Skull Base Surgery, Shandong Provincial ENT Hospital, Shandong Institute of Otorhinolaryngology, Jinan 250022, China
| | - H B Wang
- Department of Neurotology and Lateral Skull Base Surgery, Shandong Provincial ENT Hospital, Shandong Institute of Otorhinolaryngology, Jinan 250022, China
| |
Collapse
|
32
|
Chen Y, Dong B, Jiang Z, Cai Q, Huang L, Huang H. SuperSonic shear imaging for the differentiation between benign and malignant thyroid nodules: a meta-analysis. J Endocrinol Invest 2022; 45:1327-1339. [PMID: 35229278 DOI: 10.1007/s40618-022-01765-y] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/09/2022] [Indexed: 12/07/2022]
Abstract
PURPOSE To assess the diagnostic value of SuperSonic shear imaging (SSI) for the differentiation between benign and malignant thyroid nodules through meta-analysis. METHODS Online database searches were performed on PubMed, EMBASE, the Cochrane Library, and the Web of Science until 31 July 2021. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the quality of the included studies. Three measures of diagnostic test performance were used to examine the value of SSI, including the summary area under the receiver operating characteristic curve (AUROC), the summary diagnostic odds ratio (DOR), and the summary sensitivity and specificity. Heterogeneity was explored using meta-regression and subgroup analyses. RESULTS Finally, 21 studies with 3376 patients were included in this study. There were a total of 4296 thyroid nodules, in which 1806 malignant nodules and 2490 benign ones were involved. Thyroid nodules exhibited a malignancy rate of 42.0% (range 5.6-79.8%), 95.1% of which were of papillary variant. SSI showed a summary sensitivity of 74% [95% confidence interval (CI) 67-79%], specificity of 82% (95% CI 77-87%) and AUROC of 0.85 (95% CI 0.82-0.88) for the differentiation between benign and malignant thyroid nodules. The summary positive likelihood ratio (LR), negative LR, and DOR were 4.2 (95% CI 3.3-5.3), 0.32 (95% CI 0.26-0.40), and 13 (95% CI 9-18), respectively. CONCLUSIONS SSI showed high accuracy in the diagnostic differentiation between benign and malignant thyroid nodules and can be served as a noninvasive and important adjunct for thyroid nodule evaluation.
Collapse
Affiliation(s)
- Y Chen
- Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China
| | - B Dong
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Z Jiang
- Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China
| | - Q Cai
- Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China
| | - L Huang
- Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China
| | - H Huang
- Department of Endocrinology, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China.
| |
Collapse
|
33
|
Abstract
INTRODUCTION Targeted drug delivery has been widely explored as a promising way to improve the performance of nanomedicines. However, protein corona formed on the nano-surface represents a major issue that has great impacts on the in vivo fate of targeting nanomedicines, which has been overlooked in the past. With the increasing understanding of protein corona in the recent decade, many efforts have been made to improve targeting efficacy. AREAS COVERED In this review, we briefly summarize insights of targeted delivery systems inspired by protein corona, and discuss the promising strategies to regulate protein corona for better targeting. EXPERT OPINION The interaction between nanomedicines and endogenous proteins brings great uncertainty and challenges, but it also provides great opportunities for the development of targeting nanomedicines at the same time. With increasing understanding of protein corona, the strategies to regulate protein corona pave new avenues for the development of targeting nanomedicines.
Collapse
Affiliation(s)
- Zhuxuan Jiang
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, P.R. China
| | - Yuxiu Chu
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, P.R. China
| | - Changyou Zhan
- Center of Medical Research and Innovation, Shanghai Pudong Hospital & Department of Pharmacology, School of Basic Medical Sciences & State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, P.R. China.,Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, P.R. China.,Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, P.R. China
| |
Collapse
|
34
|
Qu W, Jiang Z, Liu Z, Zhu L, Chen X, Liu B, Zhao Y, Li S, Yan H, Qu X, Zang A, Sun Y, Zhou A. P-246 Real-world outcomes in metastatic colorectal patients receiving regorafenib treatment in China. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.04.336] [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: 11/01/2022] Open
|
35
|
Toi M, Boyle F, Im YH, Reinisch M, Molthrop D, Jiang Z, Wei R, Sapunar F, Grimes B, Nabinger S, Johnston S. 59MO Adjuvant abemaciclib combined with endocrine therapy (ET): Efficacy results in monarchE cohort 1. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.03.075] [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: 11/25/2022] Open
|
36
|
Paluch-Shimon S, Neven P, Huober J, Cicin I, Jiang Z, Goetz M, Shimizu C, Huang C, Wei R, Nabinger S, Forrester T, Harbeck N. 63P Efficacy and safety results by menopausal status in monarchE: Adjuvant abemaciclib combined with endocrine therapy in patients with HR+, HER2- high-risk early breast cancer. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.03.079] [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: 11/01/2022] Open
|
37
|
Lan ZY, Li Y, Huang YT, Shi WF, She DY, Jiang Z, Liu L. [Construction of a risk assessment indicator system for re-establishment of imported malaria]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2022; 34:163-171. [PMID: 35537838 DOI: 10.16250/j.32.1374.2022023] [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] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To create a risk assessment indicator system for re-establishment of imported malaria. METHODS The risk assessment indicator system for re-establishment of imported malaria was preliminarily constructed through literature review and thematic discussions. A total of 26 malaria control experts were selected to carry out a two-round Delphi consultation of the indicator system. The active coefficient, authority coefficient and coordination coefficient of the experts and the coefficient of variation on each indicator were calculated for indicator screening and the weight of each indicator was calculated. The reliability of the indicator system was evaluated using Cronbach's coefficient α, and the content validity of the indicator system was evaluated using the authority coefficient of the expert, while the structural validity of the indicator system was evaluated using Kaiser-Meyer-Olkin (KMO) test and factor analysis. RESULTS Two rounds of Delphi expert consultations were completed by 23 malaria control experts, and a risk assessment indicator system for re-establishment of imported malaria was constructed, including 3 primary indicators, 7 secondary indicators, and 21 tertiary indicators. The active coefficient (100.00% vs. 88.46%; P < 0.01) and coordination coefficient of the expert (0.372 vs. 0.286; P < 0.01) were significantly greater in the second round of the Delphi expert consultation than in the first round. After the second round of the Delphi expert consultation, the authority coefficient of the experts ranged from 0.757 to 0.930 on each indicator, and the coefficients of variation were 0.098 to 0.136, 0.112 to 0.276 and 0.139 to 0.335 for the primary, secondary and tertiary indicators, respectively. The overall Cronbach's coefficient α of the indicator system was 0.941, and there were significant differences in the KMO values for primary (KMO value = 0.523; χ2 = 18.192, P < 0.05), secondary (KMO value = 0.694, χ2 = 51.499, P < 0.01) and tertiary indicators (KMO value = 0.519; χ2 = 477.638, P < 0.01), while the cumulative contribution rate of six principal components in the tertiary indicators was 84.23%. The normalized weights of three primary indicators of the source of infection, transmission condition and control capability were 0.337, 0.333 and 0.329, and the three secondary indicators with the greatest normalized weights included the number of imported cases and malaria parasite species (0.160), introduction of imported cases in China and medical care seeking (0.152), vector species and density (0.152), while the five tertiary indicators with the greatest normalized weights included the malaria parasite species of imported cases (0.065), vector populations (0.064), and the time interval from onset to medical care seeking (0.059), number of imported cases (0.056), and the time interval from medical care seeking to definitive diagnosis (0.055). CONCLUSIONS A risk assessment indicator system for re-establishment of imported malaria is successfully created, which provides insights into the assessment of the risk of re-establishment of imported malaria and management of key high-risk factors in malaria-eliminated areas.
Collapse
Affiliation(s)
- Z Y Lan
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang, Guizhou 550004, China
| | - Y Li
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang, Guizhou 550004, China
| | - Y T Huang
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang, Guizhou 550004, China
| | - W F Shi
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang, Guizhou 550004, China
| | - D Y She
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang, Guizhou 550004, China
| | - Z Jiang
- Guiyang Municipal Center for Disease Control and Prevention, Guiyang, Guizhou 550003, China
| | - L Liu
- Guiyang Municipal Center for Disease Control and Prevention, Guiyang, Guizhou 550003, China
| |
Collapse
|
38
|
Zhang R, Dong TL, Liang WL, Cao ZB, Xie Z, Liu KM, Yu F, Fu GF, Zhang YQ, Wang GY, Ma QQ, Wu SB, Li Y, Dong W, Jiang Z, Xu J, Wu ZY, Yao J, Pan PL, Qiu MF. [Analysis of HIV-1 genetic subtype and pretreatment drug resistance among men who have sex with men infected with HIV-1 from 19 cities of 6 provinces in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2022; 43:523-527. [PMID: 35443307 DOI: 10.3760/cma.j.cn112338-20211125-00918] [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] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the distribution of HIV-1 genetic subtypes and pretreatment drug resistance (PDR) among men who have sex with men (MSM) from 19 cities of 6 provinces in China. Methods: From April to November 2019, 574 plasma samples of ART-naive HIV-1 infected MSM were collected from 19 cities in Hebei, Shandong, Jiangsu, Zhejiang, Fujian, and Guangdong provinces, total ribonucleic acid (RNA) was extracted and amplified the HIV-1 pol gene region by nested polymerase chain reaction (PCR) after reverse transcription. Then sequences were used to construct a phylogenetic tree to determine genetic subtypes and submitted to the Stanford drug resistance database for drug resistance analysis. Results: A total of 479 samples were successfully amplified by PCR. The HIV-1 genetic subtypes included CRF01_AE, CRF07_BC, B, CRF55_01B, CRF59_01B, CRF65_cpx, CRF103_01B, CRF67_01B, CRF68_01B and unrecognized subtype, which accounted for 43.4%, 36.3%, 6.3%, 5.9%, 0.8%, 0.8%, 0.4%, 0.4%, 0.2% and 5.5%, respectively. The distribution of genetic subtypes among provinces is statistically different (χ2=44.141, P<0.001). The overall PDR rate was 4.6% (22/479), the drug resistance rate of non-nucleoside reverse transcriptase inhibitors, nucleoside reverse transcriptase inhibitors, and protease inhibitors were 3.5% (17/479), 0.8% (4/479) and 0.2% (1/479), respectively. The PDR rate of recent infections was significantly higher than that of long-term infections (χ2=4.634, P=0.031). Conclusions: The HIV-1 genetic subtypes among MSM infected with HIV-1 from 19 cities of 6 provinces in China are diverse, and the distribution of subtypes is different among provinces. The overall PDR rate is low, while the PDR rate of recent infections was significantly higher than that of long-term infections, suggesting the surveillance of PDR in recent infections should be strengthened.
Collapse
Affiliation(s)
- R Zhang
- National HIV Reference Laboratory, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - T L Dong
- Division of HIV Prevention and Intervention, National Center for AIDS/STD Control and Prevention,Chinese Center for Disease Control and Prevention, Beijing 102206,China
| | - W L Liang
- National HIV Reference Laboratory, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Z B Cao
- Division of HIV Prevention and Intervention, National Center for AIDS/STD Control and Prevention,Chinese Center for Disease Control and Prevention, Beijing 102206,China
| | - Z Xie
- Division of HIV Prevention and Intervention, National Center for AIDS/STD Control and Prevention,Chinese Center for Disease Control and Prevention, Beijing 102206,China
| | - K M Liu
- Division of HIV Prevention and Intervention, National Center for AIDS/STD Control and Prevention,Chinese Center for Disease Control and Prevention, Beijing 102206,China
| | - F Yu
- Danlan Beijing Media Limited, Beijing 100020, China
| | - G F Fu
- Jiangsu Provincial Center for Disease Control and Prevention,Nanjing 210009, China
| | - Y Q Zhang
- Hebei Provincial Center for Disease Control and Prevention,Shijiazhuang 050021, China
| | - G Y Wang
- Shandong Center for Disease Control and Prevention, Ji'nan 250014, China
| | - Q Q Ma
- Zhejiang Provincial Center for Disease Control and Prevention,Hangzhou 310051, China
| | - S B Wu
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012,China
| | - Y Li
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - W Dong
- Division of HIV Prevention and Intervention, National Center for AIDS/STD Control and Prevention,Chinese Center for Disease Control and Prevention, Beijing 102206,China
| | - Z Jiang
- Division of HIV Prevention and Intervention, National Center for AIDS/STD Control and Prevention,Chinese Center for Disease Control and Prevention, Beijing 102206,China
| | - J Xu
- Division of HIV Prevention and Intervention, National Center for AIDS/STD Control and Prevention,Chinese Center for Disease Control and Prevention, Beijing 102206,China
| | - Z Y Wu
- Division of HIV Prevention and Intervention, National Center for AIDS/STD Control and Prevention,Chinese Center for Disease Control and Prevention, Beijing 102206,China
| | - J Yao
- National HIV Reference Laboratory, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - P L Pan
- National HIV Reference Laboratory, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - M F Qiu
- National HIV Reference Laboratory, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| |
Collapse
|
39
|
Jiang K, Wang Y, Jiang Z, Qian B. Study of the Void Structure of PAN Fiber by Small Angle x-ray Scattering and Spline Function. INT POLYM PROC 2022. [DOI: 10.1515/ipp-1987-0031] [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: 11/15/2022]
Abstract
Abstract
The voids in fibers, especially in wet spun fibers are a major factor of fiber structure which greatly affects the fiber properties. A lot of research has shown that the inner voids and surface imperfections were the main causes of strength decreases. Therefore it is very important to determine the void morphology development in fiber processing in order to obtain high quality as-spun fiber.
In this paper we introduce a new method for the determination of void morphology using small angle x-ray scattering with a spline function method in which the average void size, size distribution and the related scattering power as well is obtained.
Collapse
Affiliation(s)
- K. Jiang
- Man-made Fiber Research Institute, China Textile University , Shanghai , China
| | - Y. Wang
- Man-made Fiber Research Institute, China Textile University , Shanghai , China
| | - Z. Jiang
- Man-made Fiber Research Institute, China Textile University , Shanghai , China
| | - B. Qian
- Man-made Fiber Research Institute, China Textile University , Shanghai , China
| |
Collapse
|
40
|
Hu XY, Jiang Z, Zhang MG, Wang XS. [Current research status on pelvic autonomic nerve monitoring in rectal cancer surgery]. Zhonghua Wei Chang Wai Ke Za Zhi 2022; 25:82-88. [PMID: 35067038 DOI: 10.3760/cma.j.cn441530-20210324-00130] [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] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Rectal cancer is a common malignant tumor of the digestive tract, and surgery is the main treatment strategy. Disorders of bowel, anorectal and urogenital function remain common problems after total mesorectal resection (TME), which seriously decreases the quality of life of patients. Surgical nerve damage is one of the main causes of the complications, while TME with pelvic autonomic nerve preservation is an effective way to reduce the occurrence of adverse outcomes. Intraoperative nerve monitoring (IONM) is a promising method to assist the surgeon to identify and protect the pelvic autonomic nerves. Nevertheless, the monitoring methods and technical standards vary, and the clinical use of IONM is still limited. This review aims to summarize the researches on IONM in rectal and pelvic surgery. The electrical nerve stimulation technique and different methods of IONM in rectal cancer surgery are introduced. Also, the authors discuss the limitations of current researches, including methodological disunity and lack of equipment, then prospect the future direction in this field.
Collapse
Affiliation(s)
- X Y Hu
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Z Jiang
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - M G Zhang
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X S Wang
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| |
Collapse
|
41
|
Tang XJ, Duan LJ, Liang WL, Cheng S, Dong TL, Xie Z, Liu KM, Yu F, Chen ZH, Mi GD, Liang L, Yan HJ, Chen L, Lin L, Kang DM, Fu XB, Qiu MF, Jiang Z, Xu ZY, Wu Z. [Application of limiting antigen avidity enzyme immunoassay for estimating HIV-1 incidence in men who have sex with men]. Zhonghua Liu Xing Bing Xue Za Zhi 2022; 43:72-77. [PMID: 35130655 DOI: 10.3760/cma.j.cn112338-20210609-00463] [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] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To estimate the incidence of HIV-1 infection in men who have sex with men (MSM) in key areas of China through HIV-1 limiting antigen avidity enzyme immunoassay (LAg-Avidity EIA), analyze the deviation from the actual results and identify influencing factors, and provided reference for improving the accuracy of estimation results. Methods: Based on the principle of the cohort randomized study design, 20 cities were selected in China based on population size and the number of HIV-positive MSM. The sample size was estimated to be 700 according to the HIV-1 infection rate in MSM. MSM mobile phone app. was used to establish a detection appointment and questionnaire system, and the baseline cross-sectional survey was conducted from April to November 2019. LAg-Avidity EIA was used to identify the recent infected samples. The incidence of HIV-1 infection was calculated and then adjusted based on the estimation formula designed by WHO. The influencing factors were identified by analyzing the sample collection and detection processes. Results: Among the 10 650 blood samples from the participants, 799 were HIV-positive in initial screening, in which 198 samples (24.78%) missed during confirmation test. Only 621 samples were received by the laboratory. After excluding misreported samples, 520 samples were qualified for testing. A total of 155 samples were eventually determined as recent infection through LAg-Avidity EIA; Based on the estimation formula , the incidence of HIV-1 infection in MSM in 20 cities was 4.06% (95%CI:3.27%-4.85%), it increased to 5.53% (95%CI: 4.45%-6.60%)after the adjusting for sample missing rate. When the sample missing rate and misreporting rate were both adjusted, the incidence of HIV-1 infection in the MSM increased to 5.66% (95%CI:4.67%-6.65%). The actual incidence of HIV-1 infection in MSM in the 20 cities might be between 4.06% and 5.66%. Conclusions: Sample missing and misreporting might cause the deviation of the estimation of HIV-1 infection incidence. It is important to ensure the sample source and the quality of sample collection and detection to reduce the deviation in the estimation of HIV-1 infection incidence.
Collapse
Affiliation(s)
- X J Tang
- Division of Prevention and Intervention, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - L J Duan
- National HIV/AIDS Reference Laboratory, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - W L Liang
- National HIV/AIDS Reference Laboratory, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - S Cheng
- Division of Prevention and Intervention, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - T L Dong
- Division of Prevention and Intervention, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Z Xie
- Division of Prevention and Intervention, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - K M Liu
- Division of Prevention and Intervention, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - F Yu
- Danlan Beijing Media Limited, Beijing 100020, China
| | - Z H Chen
- Danlan Beijing Media Limited, Beijing 100020, China
| | - G D Mi
- Danlan Beijing Media Limited, Beijing 100020, China
| | - L Liang
- Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang 050021, China
| | - H J Yan
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - L Chen
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
| | - L Lin
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350001, China
| | - D M Kang
- Shandong Provincial Center for Disease Control and Prevention, Ji'nan 250014, China
| | - X B Fu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - M F Qiu
- National HIV/AIDS Reference Laboratory, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Z Jiang
- Division of Prevention and Intervention, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Z Y Xu
- Division of Prevention and Intervention, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Zunyou Wu
- Division of Prevention and Intervention, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| |
Collapse
|
42
|
Wang X, Li F, Zhu H, Jiang Z, Niu G, Gao Q. A Hierarchical Bayesian Latent Class Model for the Diagnostic Performance of Mini-Mental State Examination and Montreal Cognitive Assessment in Screening Mild Cognitive Impairment Due to Alzheimer's Disease. J Prev Alzheimers Dis 2022; 9:589-600. [PMID: 36281663 DOI: 10.14283/jpad.2022.70] [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] [Indexed: 06/16/2023]
Abstract
BACKGROUND The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are low costing and noninvasive neuropsychological tests in screening Mild Cognitive Impairment (MCI) due to Alzheimer's disease (AD). There is no consensus on which test performs better in detecting MCI due to AD based on the different imperfect reference standards. Therefore, we conducted a meta-analysis to assess the diagnostic performance of MMSE and MoCA for screening MCI due to AD in the absence of a gold standard. METHODS Six electronic databases were searched for relevant studies until April, 2022. A hierarchical Bayesian latent class model was used to estimate the pooled sensitivity and specificity of MoCA and MMSE in the absence of a gold standard. RESULTS 90 eligible studies covering 21273 individuals for MMSE, 26631 individuals for MoCA were included in this meta-analysis. The pooled sensitivity was 0.71(95%CI: 0.67-0.74) for MMSE and 0.85(95%CI: 0.83-0.88) for MoCA, while the pooled specificity was 0.71(95%CI: 0.68-0.74) for MMSE and 0.79(95%CI: 0.76-0.81) for MoCA. MoCA was useful to "rule in" and "rule out" the diagnosis of MCI due to AD with higher positive likelihood ratio (4.07; 95%CI: 3.60-4.62) and lower negative likelihood ratio (0.18; 95%CI: 0.16-0.22). Moreover, the diagnostic odds ratio of MoCA was 22.08(95%CI: 17.24-28.29), which showed significantly favorable diagnostic performance. CONCLUSIONS It suggests that MoCA has greater diagnostic performance than MMSE for differentiating MCI due to AD when the gold standard is absent. However, these results should be taken with caution given the heterogeneity observed.
Collapse
Affiliation(s)
- X Wang
- Qi Gao, PhD, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Beijing 100069, China. Tel.: +010 83911497; E-mail:
| | | | | | | | | | | |
Collapse
|
43
|
Jiang Z, Ma X, Liu S, Tan J, Li Z. Effects of Melatonin on Cardiac Function, Metabolic Stress and Apoptosis of Cardiomyocytes in Rats with Heart Failure after Myocardial Infarction. Indian J Pharm Sci 2022. [DOI: 10.36468/pharmaceutical-sciences.1027] [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: 11/18/2022] Open
|
44
|
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: 47] [Impact Index Per Article: 15.7] [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].
Collapse
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
| |
Collapse
|
45
|
Wang Y, Yu L, Zhu L, Ming H, Wu J, Jiang Z. 3 Derivation of bovine trophoblast stem cells. Reprod Fertil Dev 2021; 34:235. [PMID: 35231282 DOI: 10.1071/rdv34n2ab3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Affiliation(s)
- Y Wang
- School of Animal Science, AgCenter, Louisiana State University, Baton Rouge, LA, USA
| | - L Yu
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - L Zhu
- School of Animal Science, AgCenter, Louisiana State University, Baton Rouge, LA, USA
| | - H Ming
- School of Animal Science, AgCenter, Louisiana State University, Baton Rouge, LA, USA
| | - J Wu
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Z Jiang
- School of Animal Science, AgCenter, Louisiana State University, Baton Rouge, LA, USA
| |
Collapse
|
46
|
Zhu L, Tillquist N, Shi J, Chen Q, Govoni K, Reed S, Zinn S, Jiang Z. 5 Maternal gestational nutrition perturbs small RNA code in offspring sperm in sheep. Reprod Fertil Dev 2021; 34:236. [PMID: 35231304 DOI: 10.1071/rdv34n2ab5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Affiliation(s)
- L Zhu
- School of Animal Science, AgCenter, Louisiana State University, Baton Rouge, LA, USA
| | - N Tillquist
- Department of Animal Science, University of Connecticut, Storrs, CT, USA
| | - J Shi
- School of Medicine, University of California, Riverside, CA, USA
| | - Q Chen
- School of Medicine, University of California, Riverside, CA, USA
| | - K Govoni
- Department of Animal Science, University of Connecticut, Storrs, CT, USA
| | - S Reed
- Department of Animal Science, University of Connecticut, Storrs, CT, USA
| | - S Zinn
- Department of Animal Science, University of Connecticut, Storrs, CT, USA
| | - Z Jiang
- School of Animal Science, AgCenter, Louisiana State University, Baton Rouge, LA, USA
| |
Collapse
|
47
|
Zheng C, Xie K, Li X, Wang G, Luo J, Zhang C, Jiang Z, Wang Y, Luo C, Qiang Y, Hu L, Wang Y, Shen Y. The prognostic value of modified nutric score for patients in cardiothoracic surgery recovery unit: a retrospective cohort study. Clin Nutr ESPEN 2021. [DOI: 10.1016/j.clnesp.2021.09.295] [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: 11/28/2022]
|
48
|
Jiang Z, Diao P, Liang Y, Dai K, Li H, Wang H, Chen Y, Man L, Kuang Y. A Light Gradient Boosting Machine-Enabled Early Prediction of Cardiotoxicity for Breast Cancer Patients. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.771] [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]
|
49
|
Jin F, Chen Y, Jiang Z, Li Y, Zhao C, Liu L, He Q, Li Y. The Correlation Study of Circadian Clock Gene BMAL1 Regulates the Biological Behavior of Human Nasopharyngeal Carcinoma Cell After Radiotherapy. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.785] [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]
|
50
|
Jiang Z, Chen H, Chen L, Huang Q, Zhang Q, Zhou J, Li Q, Wang D, Jiang M, Liu Y, Ma Y, Xiang L. Epidemiology and clinicopathology in genital dermatoses: a retrospective study of 3052 skin biopsy cases. J Eur Acad Dermatol Venereol 2021; 36:e240-e242. [PMID: 34704626 DOI: 10.1111/jdv.17774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/22/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Z Jiang
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - H Chen
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - L Chen
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Q Huang
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Q Zhang
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - J Zhou
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Q Li
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - D Wang
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - M Jiang
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Y Liu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Y Ma
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - L Xiang
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| |
Collapse
|