1
|
Wen W, Zhang H, Wang Z, Gao X, Wu P, Lin J, Zeng N. Enhanced multi-label cardiology diagnosis with channel-wise recurrent fusion. Comput Biol Med 2024; 171:108210. [PMID: 38417383 DOI: 10.1016/j.compbiomed.2024.108210] [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: 01/08/2024] [Revised: 02/08/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
The timely detection of abnormal electrocardiogram (ECG) signals is vital for preventing heart disease. However, traditional automated cardiology diagnostic methods have the limitation of being unable to simultaneously identify multiple diseases in a segment of ECG signals, and do not consider the potential correlations between the 12-lead ECG signals. To address these issues, this paper presents a novel network architecture, denoted as Branched Convolution and Channel Fusion Network (BCCF-Net), designed for the multi-label diagnosis of ECG cardiology to achieve simultaneous identification of multiple diseases. Among them, the BCCF-Net incorporates the Channel-wise Recurrent Fusion (CRF) network, which is designed to enhance the ability to explore potential correlation information between 12 leads. Furthermore, the utilization of the squeeze and excitation (SE) attention mechanism maximizes the potential of the convolutional neural network (CNN). In order to efficiently capture complex patterns in space and time across various scales, the multi branch convolution (MBC) module has been developed. Through extensive experiments on two public datasets with seven subtasks, the efficacy and robustness of the proposed ECG multi-label classification framework have been comprehensively evaluated. The results demonstrate the superior performance of the BCCF-Net compared to other state-of-the-art algorithms. The developed framework holds practical application in clinical settings, allowing for the refined diagnosis of cardiac arrhythmias through ECG signal analysis.
Collapse
Affiliation(s)
- Weimin Wen
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Hongyi Zhang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Xingen Gao
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Juqiang Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
| |
Collapse
|
2
|
Li T, Feng Y, Chen Z, Hou Q, Serrano BR, Barcenas AR, Wu P, Zhao W, Shen M. Effect of quercetin on granulosa cells development from hierarchical follicles in chicken. Br Poult Sci 2024; 65:44-51. [PMID: 37772759 DOI: 10.1080/00071668.2023.2264792] [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: 04/15/2023] [Accepted: 08/21/2023] [Indexed: 09/30/2023]
Abstract
1. The bioflavonoid quercetin is a biologically active component, but its functional regulation of granulosa cells (GCs) during chicken follicular development is little studied. To investigate the effect of quercetin on follicular development in laying hens, an in vitro study was conducted on granulosa cells from hierarchical follicles treated with quercetin.2. The effect of quercetin on cell activity, proliferation and apoptosis of granulosa cells was detected by CCK-8, EdU and apoptosis assays. The effect on progesterone secretion from granulosa cells was investigated by enzyme-linked immunosorbent assay (ELISA). Expression of proliferating cell nuclear antigen (PCNA) mRNA and oestrogen receptors (ERs), as well as the expression of steroid acute regulatory protein (StAR), cytochrome P450 cholesterol side chain cleavage enzyme (P450scc) and 3β-hydroxysteroid dehydrogenase (3β-HSD) mRNA during progesterone synthesis, were measured by real-time quantitative polymerase chain reaction (RT-qPCR). PCNA, StAR and CYP11A1 protein expression levels were detected using Western blotting (WB).3. The results showed that treatment with quercetin in granulosa cells significantly enhanced cell vitality and proliferation, reduced apoptosis and promoted the expression of gene and protein levels of PCNA. The levels of progesterone secretion increased significantly following quercetin treatment, as did the expression levels of StAR and CYP11A1 using the Western Blot (WB) method.4. The mRNA expression levels of ERα were significantly upregulated in the 100 ng/ml and 1000 ng/ml quercetin-treated groups, while there was no significant difference in expression levels of ERβ mRNA.
Collapse
Affiliation(s)
- T Li
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, China
| | - Y Feng
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, China
| | - Z Chen
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, China
| | - Q Hou
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, China
| | - B R Serrano
- Plant Protein and Bionatural Products Research Center, Havana, Cuba
| | - A R Barcenas
- Plant Protein and Bionatural Products Research Center, Havana, Cuba
| | - P Wu
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, China
| | - W Zhao
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, China
| | - M Shen
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, China
- Laying Hen Breeding and Production Laboratory, Jiangsu Institute of Poultry Science, Yangzhou, China
| |
Collapse
|
3
|
Ali ST, Wu P, He D, Tian L, Cowling BJ. Forecasting influenza epidemics in Hong Kong using multiple streams of syndromic and laboratory surveillance data: abridged secondary publication. Hong Kong Med J 2024; 30 Suppl 1:4-8. [PMID: 38413204] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024] Open
Affiliation(s)
- S T Ali
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - P Wu
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - D He
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - L Tian
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - B J Cowling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
4
|
Guo X, Wang Z, Wu P, Li Y, Alsaadi FE, Zeng N. ELTS-Net: An enhanced liver tumor segmentation network with augmented receptive field and global contextual information. Comput Biol Med 2024; 169:107879. [PMID: 38142549 DOI: 10.1016/j.compbiomed.2023.107879] [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: 10/20/2023] [Revised: 11/30/2023] [Accepted: 12/18/2023] [Indexed: 12/26/2023]
Abstract
The liver is one of the organs with the highest incidence rate in the human body, and late-stage liver cancer is basically incurable. Therefore, early diagnosis and lesion location of liver cancer are of important clinical value. This study proposes an enhanced network architecture ELTS-Net based on the 3D U-Net model, to address the limitations of conventional image segmentation methods and the underutilization of image spatial features by the 2D U-Net network structure. ELTS-Net expands upon the original network by incorporating dilated convolutions to increase the receptive field of the convolutional kernel. Additionally, an attention residual module, comprising an attention mechanism and residual connections, replaces the original convolutional module, serving as the primary components of the encoder and decoder. This design enables the network to capture contextual information globally in both channel and spatial dimensions. Furthermore, deep supervision modules are integrated between different levels of the decoder network, providing additional feedback from deeper intermediate layers. This constrains the network weights to the target regions and optimizing segmentation results. Evaluation on the LiTS2017 dataset shows improvements in evaluation metrics for liver and tumor segmentation tasks compared to the baseline 3D U-Net model, achieving 95.2% liver segmentation accuracy and 71.9% tumor segmentation accuracy, with accuracy improvements of 0.9% and 3.1% respectively. The experimental results validate the superior segmentation performance of ELTS-Net compared to other comparison models, offering valuable guidance for clinical diagnosis and treatment.
Collapse
Affiliation(s)
- Xiaoyue Guo
- College of Engineering, Peking University, Beijing 100871, China; Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Yurong Li
- College of Electrical Engineering and Automation, Fuzhou University, Fujian 350116, China; Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fujian 350116, China
| | - Fuad E Alsaadi
- Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
| |
Collapse
|
5
|
Staplin N, Haynes R, Judge PK, Wanner C, Green JB, Emberson J, Preiss D, Mayne KJ, Ng SYA, Sammons E, Zhu D, Hill M, Stevens W, Wallendszus K, Brenner S, Cheung AK, Liu ZH, Li J, Hooi LS, Liu WJ, Kadowaki T, Nangaku M, Levin A, Cherney D, Maggioni AP, Pontremoli R, Deo R, Goto S, Rossello X, Tuttle KR, Steubl D, Petrini M, Seidi S, Landray MJ, Baigent C, Herrington WG, Abat S, Abd Rahman R, Abdul Cader R, Abdul Hafidz MI, Abdul Wahab MZ, Abdullah NK, Abdul-Samad T, Abe M, Abraham N, Acheampong S, Achiri P, Acosta JA, Adeleke A, Adell V, Adewuyi-Dalton R, Adnan N, Africano A, Agharazii M, Aguilar F, Aguilera A, Ahmad M, Ahmad MK, Ahmad NA, Ahmad NH, Ahmad NI, Ahmad Miswan N, Ahmad Rosdi H, Ahmed I, Ahmed S, Ahmed S, Aiello J, Aitken A, AitSadi R, Aker S, Akimoto S, Akinfolarin A, Akram S, Alberici F, Albert C, Aldrich L, Alegata M, Alexander L, Alfaress S, Alhadj Ali M, Ali A, Ali A, Alicic R, Aliu A, Almaraz R, Almasarwah R, Almeida J, Aloisi A, Al-Rabadi L, Alscher D, Alvarez P, Al-Zeer B, Amat M, Ambrose C, Ammar H, An Y, Andriaccio L, Ansu K, Apostolidi A, Arai N, Araki H, Araki S, Arbi A, Arechiga O, Armstrong S, Arnold T, Aronoff S, Arriaga W, Arroyo J, Arteaga D, Asahara S, Asai A, Asai N, Asano S, Asawa M, Asmee MF, Aucella F, Augustin M, Avery A, Awad A, Awang IY, Awazawa M, Axler A, Ayub W, Azhari Z, Baccaro R, Badin C, Bagwell B, Bahlmann-Kroll E, Bahtar AZ, Baigent C, Bains D, Bajaj H, Baker R, Baldini E, Banas B, Banerjee D, Banno S, Bansal S, Barberi S, Barnes S, Barnini C, Barot C, Barrett K, Barrios R, Bartolomei Mecatti B, Barton I, Barton J, Basily W, Bavanandan S, Baxter A, Becker L, Beddhu S, Beige J, Beigh S, Bell S, Benck U, Beneat A, Bennett A, Bennett D, Benyon S, Berdeprado J, Bergler T, Bergner A, Berry M, Bevilacqua M, Bhairoo J, Bhandari S, Bhandary N, Bhatt A, Bhattarai M, Bhavsar M, Bian W, Bianchini F, Bianco S, Bilous R, Bilton J, Bilucaglia D, Bird C, Birudaraju D, Biscoveanu M, Blake C, Bleakley N, Bocchicchia K, Bodine S, Bodington R, Boedecker S, Bolduc M, Bolton S, Bond C, Boreky F, Boren K, Bouchi R, Bough L, Bovan D, Bowler C, Bowman L, Brar N, Braun C, Breach A, Breitenfeldt M, Brenner S, Brettschneider B, Brewer A, Brewer G, Brindle V, Brioni E, Brown C, Brown H, Brown L, Brown R, Brown S, Browne D, Bruce K, Brueckmann M, Brunskill N, Bryant M, Brzoska M, Bu Y, Buckman C, Budoff M, Bullen M, Burke A, Burnette S, Burston C, Busch M, Bushnell J, Butler S, Büttner C, Byrne C, Caamano A, Cadorna J, Cafiero C, Cagle M, Cai J, Calabrese K, Calvi C, Camilleri B, Camp S, Campbell D, Campbell R, Cao H, Capelli I, Caple M, Caplin B, Cardone A, Carle J, Carnall V, Caroppo M, Carr S, Carraro G, Carson M, Casares P, Castillo C, Castro C, Caudill B, Cejka V, Ceseri M, Cham L, Chamberlain A, Chambers J, Chan CBT, Chan JYM, Chan YC, Chang E, Chang E, Chant T, Chavagnon T, Chellamuthu P, Chen F, Chen J, Chen P, Chen TM, Chen Y, Chen Y, Cheng C, Cheng H, Cheng MC, Cherney D, Cheung AK, Ching CH, Chitalia N, Choksi R, Chukwu C, Chung K, Cianciolo G, Cipressa L, Clark S, Clarke H, Clarke R, Clarke S, Cleveland B, Cole E, Coles H, Condurache L, Connor A, Convery K, Cooper A, Cooper N, Cooper Z, Cooperman L, Cosgrove L, Coutts P, Cowley A, Craik R, Cui G, Cummins T, Dahl N, Dai H, Dajani L, D'Amelio A, Damian E, Damianik K, Danel L, Daniels C, Daniels T, Darbeau S, Darius H, Dasgupta T, Davies J, Davies L, Davis A, Davis J, Davis L, Dayanandan R, Dayi S, Dayrell R, De Nicola L, Debnath S, Deeb W, Degenhardt S, DeGoursey K, Delaney M, Deo R, DeRaad R, Derebail V, Dev D, Devaux M, Dhall P, Dhillon G, Dienes J, Dobre M, Doctolero E, Dodds V, Domingo D, Donaldson D, Donaldson P, Donhauser C, Donley V, Dorestin S, Dorey S, Doulton T, Draganova D, Draxlbauer K, Driver F, Du H, Dube F, Duck T, Dugal T, Dugas J, Dukka H, Dumann H, Durham W, Dursch M, Dykas R, Easow R, Eckrich E, Eden G, Edmerson E, Edwards H, Ee LW, Eguchi J, Ehrl Y, Eichstadt K, Eid W, Eilerman B, Ejima Y, Eldon H, Ellam T, Elliott L, Ellison R, Emberson J, Epp R, Er A, Espino-Obrero M, Estcourt S, Estienne L, Evans G, Evans J, Evans S, Fabbri G, Fajardo-Moser M, Falcone C, Fani F, Faria-Shayler P, Farnia F, Farrugia D, Fechter M, Fellowes D, Feng F, Fernandez J, Ferraro P, Field A, Fikry S, Finch J, Finn H, Fioretto P, Fish R, Fleischer A, Fleming-Brown D, Fletcher L, Flora R, Foellinger C, Foligno N, Forest S, Forghani Z, Forsyth K, Fottrell-Gould D, Fox P, Frankel A, Fraser D, Frazier R, Frederick K, Freking N, French H, Froment A, Fuchs B, Fuessl L, Fujii H, Fujimoto A, Fujita A, Fujita K, Fujita Y, Fukagawa M, Fukao Y, Fukasawa A, Fuller T, Funayama T, Fung E, Furukawa M, Furukawa Y, Furusho M, Gabel S, Gaidu J, Gaiser S, Gallo K, Galloway C, Gambaro G, Gan CC, Gangemi C, Gao M, Garcia K, Garcia M, Garofalo C, Garrity M, Garza A, Gasko S, Gavrila M, Gebeyehu B, Geddes A, Gentile G, George A, George J, Gesualdo L, Ghalli F, Ghanem A, Ghate T, Ghavampour S, Ghazi A, Gherman A, Giebeln-Hudnell U, Gill B, Gillham S, Girakossyan I, Girndt M, Giuffrida A, Glenwright M, Glider T, Gloria R, Glowski D, Goh BL, Goh CB, Gohda T, Goldenberg R, Goldfaden R, Goldsmith C, Golson B, Gonce V, Gong Q, Goodenough B, Goodwin N, Goonasekera M, Gordon A, Gordon J, Gore A, Goto H, Goto S, Goto S, Gowen D, Grace A, Graham J, Grandaliano G, Gray M, Green JB, Greene T, Greenwood G, Grewal B, Grifa R, Griffin D, Griffin S, Grimmer P, Grobovaite E, Grotjahn S, Guerini A, Guest C, Gunda S, Guo B, Guo Q, Haack S, Haase M, Haaser K, Habuki K, Hadley A, Hagan S, Hagge S, Haller H, Ham S, Hamal S, Hamamoto Y, Hamano N, Hamm M, Hanburry A, Haneda M, Hanf C, Hanif W, Hansen J, Hanson L, Hantel S, Haraguchi T, Harding E, Harding T, Hardy C, Hartner C, Harun Z, Harvill L, Hasan A, Hase H, Hasegawa F, Hasegawa T, Hashimoto A, Hashimoto C, Hashimoto M, Hashimoto S, Haskett S, Hauske SJ, Hawfield A, Hayami T, Hayashi M, Hayashi S, Haynes R, Hazara A, Healy C, Hecktman J, Heine G, Henderson H, Henschel R, Hepditch A, Herfurth K, Hernandez G, Hernandez Pena A, Hernandez-Cassis C, Herrington WG, Herzog C, Hewins S, Hewitt D, Hichkad L, Higashi S, Higuchi C, Hill C, Hill L, Hill M, Himeno T, Hing A, Hirakawa Y, Hirata K, Hirota Y, Hisatake T, Hitchcock S, Hodakowski A, Hodge W, Hogan R, Hohenstatt U, Hohenstein B, Hooi L, Hope S, Hopley M, Horikawa S, Hosein D, Hosooka T, Hou L, Hou W, Howie L, Howson A, Hozak M, Htet Z, Hu X, Hu Y, Huang J, Huda N, Hudig L, Hudson A, Hugo C, Hull R, Hume L, Hundei W, Hunt N, Hunter A, Hurley S, Hurst A, Hutchinson C, Hyo T, Ibrahim FH, Ibrahim S, Ihana N, Ikeda T, Imai A, Imamine R, Inamori A, Inazawa H, Ingell J, Inomata K, Inukai Y, Ioka M, Irtiza-Ali A, Isakova T, Isari W, Iselt M, Ishiguro A, Ishihara K, Ishikawa T, Ishimoto T, Ishizuka K, Ismail R, Itano S, Ito H, Ito K, Ito M, Ito Y, Iwagaitsu S, Iwaita Y, Iwakura T, Iwamoto M, Iwasa M, Iwasaki H, Iwasaki S, Izumi K, Izumi K, Izumi T, Jaafar SM, Jackson C, Jackson Y, Jafari G, Jahangiriesmaili M, Jain N, Jansson K, Jasim H, Jeffers L, Jenkins A, Jesky M, Jesus-Silva J, Jeyarajah D, Jiang Y, Jiao X, Jimenez G, Jin B, Jin Q, Jochims J, Johns B, Johnson C, Johnson T, Jolly S, Jones L, Jones L, Jones S, Jones T, Jones V, Joseph M, Joshi S, Judge P, Junejo N, Junus S, Kachele M, Kadowaki T, Kadoya H, Kaga H, Kai H, Kajio H, Kaluza-Schilling W, Kamaruzaman L, Kamarzarian A, Kamimura Y, Kamiya H, Kamundi C, Kan T, Kanaguchi Y, Kanazawa A, Kanda E, Kanegae S, Kaneko K, Kaneko K, Kang HY, Kano T, Karim M, Karounos D, Karsan W, Kasagi R, Kashihara N, Katagiri H, Katanosaka A, Katayama A, Katayama M, Katiman E, Kato K, Kato M, Kato N, Kato S, Kato T, Kato Y, Katsuda Y, Katsuno T, Kaufeld J, Kavak Y, Kawai I, Kawai M, Kawai M, Kawase A, Kawashima S, Kazory A, Kearney J, Keith B, Kellett J, Kelley S, Kershaw M, Ketteler M, Khai Q, Khairullah Q, Khandwala H, Khoo KKL, Khwaja A, Kidokoro K, Kielstein J, Kihara M, Kimber C, Kimura S, Kinashi H, Kingston H, Kinomura M, Kinsella-Perks E, Kitagawa M, Kitajima M, Kitamura S, Kiyosue A, Kiyota M, Klauser F, Klausmann G, Kmietschak W, Knapp K, Knight C, Knoppe A, Knott C, Kobayashi M, Kobayashi R, Kobayashi T, Koch M, Kodama S, Kodani N, Kogure E, Koizumi M, Kojima H, Kojo T, Kolhe N, Komaba H, Komiya T, Komori H, Kon SP, Kondo M, Kondo M, Kong W, Konishi M, Kono K, Koshino M, Kosugi T, Kothapalli B, Kozlowski T, Kraemer B, Kraemer-Guth A, Krappe J, Kraus D, Kriatselis C, Krieger C, Krish P, Kruger B, Ku Md Razi KR, Kuan Y, Kubota S, Kuhn S, Kumar P, Kume S, Kummer I, Kumuji R, Küpper A, Kuramae T, Kurian L, Kuribayashi C, Kurien R, Kuroda E, Kurose T, Kutschat A, Kuwabara N, Kuwata H, La Manna G, Lacey M, Lafferty K, LaFleur P, Lai V, Laity E, Lambert A, Landray MJ, Langlois M, Latif F, Latore E, Laundy E, Laurienti D, Lawson A, Lay M, Leal I, Leal I, Lee AK, Lee J, Lee KQ, Lee R, Lee SA, Lee YY, Lee-Barkey Y, Leonard N, Leoncini G, Leong CM, Lerario S, Leslie A, Levin A, Lewington A, Li J, Li N, Li X, Li Y, Liberti L, Liberti ME, Liew A, Liew YF, Lilavivat U, Lim SK, Lim YS, Limon E, Lin H, Lioudaki E, Liu H, Liu J, Liu L, Liu Q, Liu WJ, Liu X, Liu Z, Loader D, Lochhead H, Loh CL, Lorimer A, Loudermilk L, Loutan J, Low CK, Low CL, Low YM, Lozon Z, Lu Y, Lucci D, Ludwig U, Luker N, Lund D, Lustig R, Lyle S, Macdonald C, MacDougall I, Machicado R, MacLean D, Macleod P, Madera A, Madore F, Maeda K, Maegawa H, Maeno S, Mafham M, Magee J, Maggioni AP, Mah DY, Mahabadi V, Maiguma M, Makita Y, Makos G, Manco L, Mangiacapra R, Manley J, Mann P, Mano S, Marcotte G, Maris J, Mark P, Markau S, Markovic M, Marshall C, Martin M, Martinez C, Martinez S, Martins G, Maruyama K, Maruyama S, Marx K, Maselli A, Masengu A, Maskill A, Masumoto S, Masutani K, Matsumoto M, Matsunaga T, Matsuoka N, Matsushita M, Matthews M, Matthias S, Matvienko E, Maurer M, Maxwell P, Mayne KJ, Mazlan N, Mazlan SA, Mbuyisa A, McCafferty K, McCarroll F, McCarthy T, McClary-Wright C, McCray K, McDermott P, McDonald C, McDougall R, McHaffie E, McIntosh K, McKinley T, McLaughlin S, McLean N, McNeil L, Measor A, Meek J, Mehta A, Mehta R, Melandri M, Mené P, Meng T, Menne J, Merritt K, Merscher S, Meshykhi C, Messa P, Messinger L, Miftari N, Miller R, Miller Y, Miller-Hodges E, Minatoguchi M, Miners M, Minutolo R, Mita T, Miura Y, Miyaji M, Miyamoto S, Miyatsuka T, Miyazaki M, Miyazawa I, Mizumachi R, Mizuno M, Moffat S, Mohamad Nor FS, Mohamad Zaini SN, Mohamed Affandi FA, Mohandas C, Mohd R, Mohd Fauzi NA, Mohd Sharif NH, Mohd Yusoff Y, Moist L, Moncada A, Montasser M, Moon A, Moran C, Morgan N, Moriarty J, Morig G, Morinaga H, Morino K, Morisaki T, Morishita Y, Morlok S, Morris A, Morris F, Mostafa S, Mostefai Y, Motegi M, Motherwell N, Motta D, Mottl A, Moys R, Mozaffari S, Muir J, Mulhern J, Mulligan S, Munakata Y, Murakami C, Murakoshi M, Murawska A, Murphy K, Murphy L, Murray S, Murtagh H, Musa MA, Mushahar L, Mustafa R, Mustafar R, Muto M, Nadar E, Nagano R, Nagasawa T, Nagashima E, Nagasu H, Nagelberg S, Nair H, Nakagawa Y, Nakahara M, Nakamura J, Nakamura R, Nakamura T, Nakaoka M, Nakashima E, Nakata J, Nakata M, Nakatani S, Nakatsuka A, Nakayama Y, Nakhoul G, Nangaku M, Naverrete G, Navivala A, Nazeer I, Negrea L, Nethaji C, Newman E, Ng SYA, Ng TJ, Ngu LLS, Nimbkar T, Nishi H, Nishi M, Nishi S, Nishida Y, Nishiyama A, Niu J, Niu P, Nobili G, Nohara N, Nojima I, Nolan J, Nosseir H, Nozawa M, Nunn M, Nunokawa S, Oda M, Oe M, Oe Y, Ogane K, Ogawa W, Ogihara T, Oguchi G, Ohsugi M, Oishi K, Okada Y, Okajyo J, Okamoto S, Okamura K, Olufuwa O, Oluyombo R, Omata A, Omori Y, Ong LM, Ong YC, Onyema J, Oomatia A, Oommen A, Oremus R, Orimo Y, Ortalda V, Osaki Y, Osawa Y, Osmond Foster J, O'Sullivan A, Otani T, Othman N, Otomo S, O'Toole J, Owen L, Ozawa T, Padiyar A, Page N, Pajak S, Paliege A, Pandey A, Pandey R, Pariani H, Park J, Parrigon M, Passauer J, Patecki M, Patel M, Patel R, Patel T, Patel Z, Paul R, Paul R, Paulsen L, Pavone L, Peixoto A, Peji J, Peng BC, Peng K, Pennino L, Pereira E, Perez E, Pergola P, Pesce F, Pessolano G, Petchey W, Petr EJ, Pfab T, Phelan P, Phillips R, Phillips T, Phipps M, Piccinni G, Pickett T, Pickworth S, Piemontese M, Pinto D, Piper J, Plummer-Morgan J, Poehler D, Polese L, Poma V, Pontremoli R, Postal A, Pötz C, Power A, Pradhan N, Pradhan R, Preiss D, Preiss E, Preston K, Prib N, Price L, Provenzano C, Pugay C, Pulido R, Putz F, Qiao Y, Quartagno R, Quashie-Akponeware M, Rabara R, Rabasa-Lhoret R, Radhakrishnan D, Radley M, Raff R, Raguwaran S, Rahbari-Oskoui F, Rahman M, Rahmat K, Ramadoss S, Ramanaidu S, Ramasamy S, Ramli R, Ramli S, Ramsey T, Rankin A, Rashidi A, Raymond L, Razali WAFA, Read K, Reiner H, Reisler A, Reith C, Renner J, Rettenmaier B, Richmond L, Rijos D, Rivera R, Rivers V, Robinson H, Rocco M, Rodriguez-Bachiller I, Rodriquez R, Roesch C, Roesch J, Rogers J, Rohnstock M, Rolfsmeier S, Roman M, Romo A, Rosati A, Rosenberg S, Ross T, Rossello X, Roura M, Roussel M, Rovner S, Roy S, Rucker S, Rump L, Ruocco M, Ruse S, Russo F, Russo M, Ryder M, Sabarai A, Saccà C, Sachson R, Sadler E, Safiee NS, Sahani M, Saillant A, Saini J, Saito C, Saito S, Sakaguchi K, Sakai M, Salim H, Salviani C, Sammons E, Sampson A, Samson F, Sandercock P, Sanguila S, Santorelli G, Santoro D, Sarabu N, Saram T, Sardell R, Sasajima H, Sasaki T, Satko S, Sato A, Sato D, Sato H, Sato H, Sato J, Sato T, Sato Y, Satoh M, Sawada K, Schanz M, Scheidemantel F, Schemmelmann M, Schettler E, Schettler V, Schlieper GR, Schmidt C, Schmidt G, Schmidt U, Schmidt-Gurtler H, Schmude M, Schneider A, Schneider I, Schneider-Danwitz C, Schomig M, Schramm T, Schreiber A, Schricker S, Schroppel B, Schulte-Kemna L, Schulz E, Schumacher B, Schuster A, Schwab A, Scolari F, Scott A, Seeger W, Seeger W, Segal M, Seifert L, Seifert M, Sekiya M, Sellars R, Seman MR, Shah S, Shah S, Shainberg L, Shanmuganathan M, Shao F, Sharma K, Sharpe C, Sheikh-Ali M, Sheldon J, Shenton C, Shepherd A, Shepperd M, Sheridan R, Sheriff Z, Shibata Y, Shigehara T, Shikata K, Shimamura K, Shimano H, Shimizu Y, Shimoda H, Shin K, Shivashankar G, Shojima N, Silva R, Sim CSB, Simmons K, Sinha S, Sitter T, Sivanandam S, Skipper M, Sloan K, Sloan L, Smith R, Smyth J, Sobande T, Sobata M, Somalanka S, Song X, Sonntag F, Sood B, Sor SY, Soufer J, Sparks H, Spatoliatore G, Spinola T, Squyres S, Srivastava A, Stanfield J, Staplin N, Staylor K, Steele A, Steen O, Steffl D, Stegbauer J, Stellbrink C, Stellbrink E, Stevens W, Stevenson A, Stewart-Ray V, Stickley J, Stoffler D, Stratmann B, Streitenberger S, Strutz F, Stubbs J, Stumpf J, Suazo N, Suchinda P, Suckling R, Sudin A, Sugamori K, Sugawara H, Sugawara K, Sugimoto D, Sugiyama H, Sugiyama H, Sugiyama T, Sullivan M, Sumi M, Suresh N, Sutton D, Suzuki H, Suzuki R, Suzuki Y, Suzuki Y, Suzuki Y, Swanson E, Swift P, Syed S, Szerlip H, Taal M, Taddeo M, Tailor C, Tajima K, Takagi M, Takahashi K, Takahashi K, Takahashi M, Takahashi T, Takahira E, Takai T, Takaoka M, Takeoka J, Takesada A, Takezawa M, Talbot M, Taliercio J, Talsania T, Tamori Y, Tamura R, Tamura Y, Tan CHH, Tan EZZ, Tanabe A, Tanabe K, Tanaka A, Tanaka A, Tanaka N, Tang S, Tang Z, Tanigaki K, Tarlac M, Tatsuzawa A, Tay JF, Tay LL, Taylor J, Taylor K, Taylor K, Te A, Tenbusch L, Teng KS, Terakawa A, Terry J, Tham ZD, Tholl S, Thomas G, Thong KM, Tietjen D, Timadjer A, Tindall H, Tipper S, Tobin K, Toda N, Tokuyama A, Tolibas M, Tomita A, Tomita T, Tomlinson J, Tonks L, Topf J, Topping S, Torp A, Torres A, Totaro F, Toth P, Toyonaga Y, Tripodi F, Trivedi K, Tropman E, Tschope D, Tse J, Tsuji K, Tsunekawa S, Tsunoda R, Tucky B, Tufail S, Tuffaha A, Turan E, Turner H, Turner J, Turner M, Tuttle KR, Tye YL, Tyler A, Tyler J, Uchi H, Uchida H, Uchida T, Uchida T, Udagawa T, Ueda S, Ueda Y, Ueki K, Ugni S, Ugwu E, Umeno R, Unekawa C, Uozumi K, Urquia K, Valleteau A, Valletta C, van Erp R, Vanhoy C, Varad V, Varma R, Varughese A, Vasquez P, Vasseur A, Veelken R, Velagapudi C, Verdel K, Vettoretti S, Vezzoli G, Vielhauer V, Viera R, Vilar E, Villaruel S, Vinall L, Vinathan J, Visnjic M, Voigt E, von-Eynatten M, Vourvou M, Wada J, Wada J, Wada T, Wada Y, Wakayama K, Wakita Y, Wallendszus K, Walters T, Wan Mohamad WH, Wang L, Wang W, Wang X, Wang X, Wang Y, Wanner C, Wanninayake S, Watada H, Watanabe K, Watanabe K, Watanabe M, Waterfall H, Watkins D, Watson S, Weaving L, Weber B, Webley Y, Webster A, Webster M, Weetman M, Wei W, Weihprecht H, Weiland L, Weinmann-Menke J, Weinreich T, Wendt R, Weng Y, Whalen M, Whalley G, Wheatley R, Wheeler A, Wheeler J, Whelton P, White K, Whitmore B, Whittaker S, Wiebel J, Wiley J, Wilkinson L, Willett M, Williams A, Williams E, Williams K, Williams T, Wilson A, Wilson P, Wincott L, Wines E, Winkelmann B, Winkler M, Winter-Goodwin B, Witczak J, Wittes J, Wittmann M, Wolf G, Wolf L, Wolfling R, Wong C, Wong E, Wong HS, Wong LW, Wong YH, Wonnacott A, Wood A, Wood L, Woodhouse H, Wooding N, Woodman A, Wren K, Wu J, Wu P, Xia S, Xiao H, Xiao X, Xie Y, Xu C, Xu Y, Xue H, Yahaya H, Yalamanchili H, Yamada A, Yamada N, Yamagata K, Yamaguchi M, Yamaji Y, Yamamoto A, Yamamoto S, Yamamoto S, Yamamoto T, Yamanaka A, Yamano T, Yamanouchi Y, Yamasaki N, Yamasaki Y, Yamasaki Y, Yamashita C, Yamauchi T, Yan Q, Yanagisawa E, Yang F, Yang L, Yano S, Yao S, Yao Y, Yarlagadda S, Yasuda Y, Yiu V, Yokoyama T, Yoshida S, Yoshidome E, Yoshikawa H, Young A, Young T, Yousif V, Yu H, Yu Y, Yuasa K, Yusof N, Zalunardo N, Zander B, Zani R, Zappulo F, Zayed M, Zemann B, Zettergren P, Zhang H, Zhang L, Zhang L, Zhang N, Zhang X, Zhao J, Zhao L, Zhao S, Zhao Z, Zhong H, Zhou N, Zhou S, Zhu D, Zhu L, Zhu S, Zietz M, Zippo M, Zirino F, Zulkipli FH. Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial. Lancet Diabetes Endocrinol 2024; 12:39-50. [PMID: 38061371 PMCID: PMC7615591 DOI: 10.1016/s2213-8587(23)00321-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce progression of chronic kidney disease and the risk of cardiovascular morbidity and mortality in a wide range of patients. However, their effects on kidney disease progression in some patients with chronic kidney disease are unclear because few clinical kidney outcomes occurred among such patients in the completed trials. In particular, some guidelines stratify their level of recommendation about who should be treated with SGLT2 inhibitors based on diabetes status and albuminuria. We aimed to assess the effects of empagliflozin on progression of chronic kidney disease both overall and among specific types of participants in the EMPA-KIDNEY trial. METHODS EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA), and included individuals aged 18 years or older with an estimated glomerular filtration rate (eGFR) of 20 to less than 45 mL/min per 1·73 m2, or with an eGFR of 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher. We explored the effects of 10 mg oral empagliflozin once daily versus placebo on the annualised rate of change in estimated glomerular filtration rate (eGFR slope), a tertiary outcome. We studied the acute slope (from randomisation to 2 months) and chronic slope (from 2 months onwards) separately, using shared parameter models to estimate the latter. Analyses were done in all randomly assigned participants by intention to treat. EMPA-KIDNEY is registered at ClinicalTrials.gov, NCT03594110. FINDINGS Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and then followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroups of eGFR included 2282 (34·5%) participants with an eGFR of less than 30 mL/min per 1·73 m2, 2928 (44·3%) with an eGFR of 30 to less than 45 mL/min per 1·73 m2, and 1399 (21·2%) with an eGFR 45 mL/min per 1·73 m2 or higher. Prespecified subgroups of uACR included 1328 (20·1%) with a uACR of less than 30 mg/g, 1864 (28·2%) with a uACR of 30 to 300 mg/g, and 3417 (51·7%) with a uACR of more than 300 mg/g. Overall, allocation to empagliflozin caused an acute 2·12 mL/min per 1·73 m2 (95% CI 1·83-2·41) reduction in eGFR, equivalent to a 6% (5-6) dip in the first 2 months. After this, it halved the chronic slope from -2·75 to -1·37 mL/min per 1·73 m2 per year (relative difference 50%, 95% CI 42-58). The absolute and relative benefits of empagliflozin on the magnitude of the chronic slope varied significantly depending on diabetes status and baseline levels of eGFR and uACR. In particular, the absolute difference in chronic slopes was lower in patients with lower baseline uACR, but because this group progressed more slowly than those with higher uACR, this translated to a larger relative difference in chronic slopes in this group (86% [36-136] reduction in the chronic slope among those with baseline uACR <30 mg/g compared with a 29% [19-38] reduction for those with baseline uACR ≥2000 mg/g; ptrend<0·0001). INTERPRETATION Empagliflozin slowed the rate of progression of chronic kidney disease among all types of participant in the EMPA-KIDNEY trial, including those with little albuminuria. Albuminuria alone should not be used to determine whether to treat with an SGLT2 inhibitor. FUNDING Boehringer Ingelheim and Eli Lilly.
Collapse
|
6
|
Judge PK, Staplin N, Mayne KJ, Wanner C, Green JB, Hauske SJ, Emberson JR, Preiss D, Ng SYA, Roddick AJ, Sammons E, Zhu D, Hill M, Stevens W, Wallendszus K, Brenner S, Cheung AK, Liu ZH, Li J, Hooi LS, Liu WJ, Kadowaki T, Nangaku M, Levin A, Cherney D, Maggioni AP, Pontremoli R, Deo R, Goto S, Rossello X, Tuttle KR, Steubl D, Massey D, Landray MJ, Baigent C, Haynes R, Herrington WG, Abat S, Abd Rahman R, Abdul Cader R, Abdul Hafidz MI, Abdul Wahab MZ, Abdullah NK, Abdul-Samad T, Abe M, Abraham N, Acheampong S, Achiri P, Acosta JA, Adeleke A, Adell V, Adewuyi-Dalton R, Adnan N, Africano A, Agharazii M, Aguilar F, Aguilera A, Ahmad M, Ahmad MK, Ahmad NA, Ahmad NH, Ahmad NI, Ahmad Miswan N, Ahmad Rosdi H, Ahmed I, Ahmed S, Ahmed S, Aiello J, Aitken A, AitSadi R, Aker S, Akimoto S, Akinfolarin A, Akram S, Alberici F, Albert C, Aldrich L, Alegata M, Alexander L, Alfaress S, Alhadj Ali M, Ali A, Ali A, Alicic R, Aliu A, Almaraz R, Almasarwah R, Almeida J, Aloisi A, Al-Rabadi L, Alscher D, Alvarez P, Al-Zeer B, Amat M, Ambrose C, Ammar H, An Y, Andriaccio L, Ansu K, Apostolidi A, Arai N, Araki H, Araki S, Arbi A, Arechiga O, Armstrong S, Arnold T, Aronoff S, Arriaga W, Arroyo J, Arteaga D, Asahara S, Asai A, Asai N, Asano S, Asawa M, Asmee MF, Aucella F, Augustin M, Avery A, Awad A, Awang IY, Awazawa M, Axler A, Ayub W, Azhari Z, Baccaro R, Badin C, Bagwell B, Bahlmann-Kroll E, Bahtar AZ, Baigent C, Bains D, Bajaj H, Baker R, Baldini E, Banas B, Banerjee D, Banno S, Bansal S, Barberi S, Barnes S, Barnini C, Barot C, Barrett K, Barrios R, Bartolomei Mecatti B, Barton I, Barton J, Basily W, Bavanandan S, Baxter A, Becker L, Beddhu S, Beige J, Beigh S, Bell S, Benck U, Beneat A, Bennett A, Bennett D, Benyon S, Berdeprado J, Bergler T, Bergner A, Berry M, Bevilacqua M, Bhairoo J, Bhandari S, Bhandary N, Bhatt A, Bhattarai M, Bhavsar M, Bian W, Bianchini F, Bianco S, Bilous R, Bilton J, Bilucaglia D, Bird C, Birudaraju D, Biscoveanu M, Blake C, Bleakley N, Bocchicchia K, Bodine S, Bodington R, Boedecker S, Bolduc M, Bolton S, Bond C, Boreky F, Boren K, Bouchi R, Bough L, Bovan D, Bowler C, Bowman L, Brar N, Braun C, Breach A, Breitenfeldt M, Brenner S, Brettschneider B, Brewer A, Brewer G, Brindle V, Brioni E, Brown C, Brown H, Brown L, Brown R, Brown S, Browne D, Bruce K, Brueckmann M, Brunskill N, Bryant M, Brzoska M, Bu Y, Buckman C, Budoff M, Bullen M, Burke A, Burnette S, Burston C, Busch M, Bushnell J, Butler S, Büttner C, Byrne C, Caamano A, Cadorna J, Cafiero C, Cagle M, Cai J, Calabrese K, Calvi C, Camilleri B, Camp S, Campbell D, Campbell R, Cao H, Capelli I, Caple M, Caplin B, Cardone A, Carle J, Carnall V, Caroppo M, Carr S, Carraro G, Carson M, Casares P, Castillo C, Castro C, Caudill B, Cejka V, Ceseri M, Cham L, Chamberlain A, Chambers J, Chan CBT, Chan JYM, Chan YC, Chang E, Chang E, Chant T, Chavagnon T, Chellamuthu P, Chen F, Chen J, Chen P, Chen TM, Chen Y, Chen Y, Cheng C, Cheng H, Cheng MC, Cherney D, Cheung AK, Ching CH, Chitalia N, Choksi R, Chukwu C, Chung K, Cianciolo G, Cipressa L, Clark S, Clarke H, Clarke R, Clarke S, Cleveland B, Cole E, Coles H, Condurache L, Connor A, Convery K, Cooper A, Cooper N, Cooper Z, Cooperman L, Cosgrove L, Coutts P, Cowley A, Craik R, Cui G, Cummins T, Dahl N, Dai H, Dajani L, D'Amelio A, Damian E, Damianik K, Danel L, Daniels C, Daniels T, Darbeau S, Darius H, Dasgupta T, Davies J, Davies L, Davis A, Davis J, Davis L, Dayanandan R, Dayi S, Dayrell R, De Nicola L, Debnath S, Deeb W, Degenhardt S, DeGoursey K, Delaney M, Deo R, DeRaad R, Derebail V, Dev D, Devaux M, Dhall P, Dhillon G, Dienes J, Dobre M, Doctolero E, Dodds V, Domingo D, Donaldson D, Donaldson P, Donhauser C, Donley V, Dorestin S, Dorey S, Doulton T, Draganova D, Draxlbauer K, Driver F, Du H, Dube F, Duck T, Dugal T, Dugas J, Dukka H, Dumann H, Durham W, Dursch M, Dykas R, Easow R, Eckrich E, Eden G, Edmerson E, Edwards H, Ee LW, Eguchi J, Ehrl Y, Eichstadt K, Eid W, Eilerman B, Ejima Y, Eldon H, Ellam T, Elliott L, Ellison R, Emberson J, Epp R, Er A, Espino-Obrero M, Estcourt S, Estienne L, Evans G, Evans J, Evans S, Fabbri G, Fajardo-Moser M, Falcone C, Fani F, Faria-Shayler P, Farnia F, Farrugia D, Fechter M, Fellowes D, Feng F, Fernandez J, Ferraro P, Field A, Fikry S, Finch J, Finn H, Fioretto P, Fish R, Fleischer A, Fleming-Brown D, Fletcher L, Flora R, Foellinger C, Foligno N, Forest S, Forghani Z, Forsyth K, Fottrell-Gould D, Fox P, Frankel A, Fraser D, Frazier R, Frederick K, Freking N, French H, Froment A, Fuchs B, Fuessl L, Fujii H, Fujimoto A, Fujita A, Fujita K, Fujita Y, Fukagawa M, Fukao Y, Fukasawa A, Fuller T, Funayama T, Fung E, Furukawa M, Furukawa Y, Furusho M, Gabel S, Gaidu J, Gaiser S, Gallo K, Galloway C, Gambaro G, Gan CC, Gangemi C, Gao M, Garcia K, Garcia M, Garofalo C, Garrity M, Garza A, Gasko S, Gavrila M, Gebeyehu B, Geddes A, Gentile G, George A, George J, Gesualdo L, Ghalli F, Ghanem A, Ghate T, Ghavampour S, Ghazi A, Gherman A, Giebeln-Hudnell U, Gill B, Gillham S, Girakossyan I, Girndt M, Giuffrida A, Glenwright M, Glider T, Gloria R, Glowski D, Goh BL, Goh CB, Gohda T, Goldenberg R, Goldfaden R, Goldsmith C, Golson B, Gonce V, Gong Q, Goodenough B, Goodwin N, Goonasekera M, Gordon A, Gordon J, Gore A, Goto H, Goto S, Goto S, Gowen D, Grace A, Graham J, Grandaliano G, Gray M, Green JB, Greene T, Greenwood G, Grewal B, Grifa R, Griffin D, Griffin S, Grimmer P, Grobovaite E, Grotjahn S, Guerini A, Guest C, Gunda S, Guo B, Guo Q, Haack S, Haase M, Haaser K, Habuki K, Hadley A, Hagan S, Hagge S, Haller H, Ham S, Hamal S, Hamamoto Y, Hamano N, Hamm M, Hanburry A, Haneda M, Hanf C, Hanif W, Hansen J, Hanson L, Hantel S, Haraguchi T, Harding E, Harding T, Hardy C, Hartner C, Harun Z, Harvill L, Hasan A, Hase H, Hasegawa F, Hasegawa T, Hashimoto A, Hashimoto C, Hashimoto M, Hashimoto S, Haskett S, Hauske SJ, Hawfield A, Hayami T, Hayashi M, Hayashi S, Haynes R, Hazara A, Healy C, Hecktman J, Heine G, Henderson H, Henschel R, Hepditch A, Herfurth K, Hernandez G, Hernandez Pena A, Hernandez-Cassis C, Herrington WG, Herzog C, Hewins S, Hewitt D, Hichkad L, Higashi S, Higuchi C, Hill C, Hill L, Hill M, Himeno T, Hing A, Hirakawa Y, Hirata K, Hirota Y, Hisatake T, Hitchcock S, Hodakowski A, Hodge W, Hogan R, Hohenstatt U, Hohenstein B, Hooi L, Hope S, Hopley M, Horikawa S, Hosein D, Hosooka T, Hou L, Hou W, Howie L, Howson A, Hozak M, Htet Z, Hu X, Hu Y, Huang J, Huda N, Hudig L, Hudson A, Hugo C, Hull R, Hume L, Hundei W, Hunt N, Hunter A, Hurley S, Hurst A, Hutchinson C, Hyo T, Ibrahim FH, Ibrahim S, Ihana N, Ikeda T, Imai A, Imamine R, Inamori A, Inazawa H, Ingell J, Inomata K, Inukai Y, Ioka M, Irtiza-Ali A, Isakova T, Isari W, Iselt M, Ishiguro A, Ishihara K, Ishikawa T, Ishimoto T, Ishizuka K, Ismail R, Itano S, Ito H, Ito K, Ito M, Ito Y, Iwagaitsu S, Iwaita Y, Iwakura T, Iwamoto M, Iwasa M, Iwasaki H, Iwasaki S, Izumi K, Izumi K, Izumi T, Jaafar SM, Jackson C, Jackson Y, Jafari G, Jahangiriesmaili M, Jain N, Jansson K, Jasim H, Jeffers L, Jenkins A, Jesky M, Jesus-Silva J, Jeyarajah D, Jiang Y, Jiao X, Jimenez G, Jin B, Jin Q, Jochims J, Johns B, Johnson C, Johnson T, Jolly S, Jones L, Jones L, Jones S, Jones T, Jones V, Joseph M, Joshi S, Judge P, Junejo N, Junus S, Kachele M, Kadowaki T, Kadoya H, Kaga H, Kai H, Kajio H, Kaluza-Schilling W, Kamaruzaman L, Kamarzarian A, Kamimura Y, Kamiya H, Kamundi C, Kan T, Kanaguchi Y, Kanazawa A, Kanda E, Kanegae S, Kaneko K, Kaneko K, Kang HY, Kano T, Karim M, Karounos D, Karsan W, Kasagi R, Kashihara N, Katagiri H, Katanosaka A, Katayama A, Katayama M, Katiman E, Kato K, Kato M, Kato N, Kato S, Kato T, Kato Y, Katsuda Y, Katsuno T, Kaufeld J, Kavak Y, Kawai I, Kawai M, Kawai M, Kawase A, Kawashima S, Kazory A, Kearney J, Keith B, Kellett J, Kelley S, Kershaw M, Ketteler M, Khai Q, Khairullah Q, Khandwala H, Khoo KKL, Khwaja A, Kidokoro K, Kielstein J, Kihara M, Kimber C, Kimura S, Kinashi H, Kingston H, Kinomura M, Kinsella-Perks E, Kitagawa M, Kitajima M, Kitamura S, Kiyosue A, Kiyota M, Klauser F, Klausmann G, Kmietschak W, Knapp K, Knight C, Knoppe A, Knott C, Kobayashi M, Kobayashi R, Kobayashi T, Koch M, Kodama S, Kodani N, Kogure E, Koizumi M, Kojima H, Kojo T, Kolhe N, Komaba H, Komiya T, Komori H, Kon SP, Kondo M, Kondo M, Kong W, Konishi M, Kono K, Koshino M, Kosugi T, Kothapalli B, Kozlowski T, Kraemer B, Kraemer-Guth A, Krappe J, Kraus D, Kriatselis C, Krieger C, Krish P, Kruger B, Ku Md Razi KR, Kuan Y, Kubota S, Kuhn S, Kumar P, Kume S, Kummer I, Kumuji R, Küpper A, Kuramae T, Kurian L, Kuribayashi C, Kurien R, Kuroda E, Kurose T, Kutschat A, Kuwabara N, Kuwata H, La Manna G, Lacey M, Lafferty K, LaFleur P, Lai V, Laity E, Lambert A, Landray MJ, Langlois M, Latif F, Latore E, Laundy E, Laurienti D, Lawson A, Lay M, Leal I, Leal I, Lee AK, Lee J, Lee KQ, Lee R, Lee SA, Lee YY, Lee-Barkey Y, Leonard N, Leoncini G, Leong CM, Lerario S, Leslie A, Levin A, Lewington A, Li J, Li N, Li X, Li Y, Liberti L, Liberti ME, Liew A, Liew YF, Lilavivat U, Lim SK, Lim YS, Limon E, Lin H, Lioudaki E, Liu H, Liu J, Liu L, Liu Q, Liu WJ, Liu X, Liu Z, Loader D, Lochhead H, Loh CL, Lorimer A, Loudermilk L, Loutan J, Low CK, Low CL, Low YM, Lozon Z, Lu Y, Lucci D, Ludwig U, Luker N, Lund D, Lustig R, Lyle S, Macdonald C, MacDougall I, Machicado R, MacLean D, Macleod P, Madera A, Madore F, Maeda K, Maegawa H, Maeno S, Mafham M, Magee J, Maggioni AP, Mah DY, Mahabadi V, Maiguma M, Makita Y, Makos G, Manco L, Mangiacapra R, Manley J, Mann P, Mano S, Marcotte G, Maris J, Mark P, Markau S, Markovic M, Marshall C, Martin M, Martinez C, Martinez S, Martins G, Maruyama K, Maruyama S, Marx K, Maselli A, Masengu A, Maskill A, Masumoto S, Masutani K, Matsumoto M, Matsunaga T, Matsuoka N, Matsushita M, Matthews M, Matthias S, Matvienko E, Maurer M, Maxwell P, Mayne KJ, Mazlan N, Mazlan SA, Mbuyisa A, McCafferty K, McCarroll F, McCarthy T, McClary-Wright C, McCray K, McDermott P, McDonald C, McDougall R, McHaffie E, McIntosh K, McKinley T, McLaughlin S, McLean N, McNeil L, Measor A, Meek J, Mehta A, Mehta R, Melandri M, Mené P, Meng T, Menne J, Merritt K, Merscher S, Meshykhi C, Messa P, Messinger L, Miftari N, Miller R, Miller Y, Miller-Hodges E, Minatoguchi M, Miners M, Minutolo R, Mita T, Miura Y, Miyaji M, Miyamoto S, Miyatsuka T, Miyazaki M, Miyazawa I, Mizumachi R, Mizuno M, Moffat S, Mohamad Nor FS, Mohamad Zaini SN, Mohamed Affandi FA, Mohandas C, Mohd R, Mohd Fauzi NA, Mohd Sharif NH, Mohd Yusoff Y, Moist L, Moncada A, Montasser M, Moon A, Moran C, Morgan N, Moriarty J, Morig G, Morinaga H, Morino K, Morisaki T, Morishita Y, Morlok S, Morris A, Morris F, Mostafa S, Mostefai Y, Motegi M, Motherwell N, Motta D, Mottl A, Moys R, Mozaffari S, Muir J, Mulhern J, Mulligan S, Munakata Y, Murakami C, Murakoshi M, Murawska A, Murphy K, Murphy L, Murray S, Murtagh H, Musa MA, Mushahar L, Mustafa R, Mustafar R, Muto M, Nadar E, Nagano R, Nagasawa T, Nagashima E, Nagasu H, Nagelberg S, Nair H, Nakagawa Y, Nakahara M, Nakamura J, Nakamura R, Nakamura T, Nakaoka M, Nakashima E, Nakata J, Nakata M, Nakatani S, Nakatsuka A, Nakayama Y, Nakhoul G, Nangaku M, Naverrete G, Navivala A, Nazeer I, Negrea L, Nethaji C, Newman E, Ng SYA, Ng TJ, Ngu LLS, Nimbkar T, Nishi H, Nishi M, Nishi S, Nishida Y, Nishiyama A, Niu J, Niu P, Nobili G, Nohara N, Nojima I, Nolan J, Nosseir H, Nozawa M, Nunn M, Nunokawa S, Oda M, Oe M, Oe Y, Ogane K, Ogawa W, Ogihara T, Oguchi G, Ohsugi M, Oishi K, Okada Y, Okajyo J, Okamoto S, Okamura K, Olufuwa O, Oluyombo R, Omata A, Omori Y, Ong LM, Ong YC, Onyema J, Oomatia A, Oommen A, Oremus R, Orimo Y, Ortalda V, Osaki Y, Osawa Y, Osmond Foster J, O'Sullivan A, Otani T, Othman N, Otomo S, O'Toole J, Owen L, Ozawa T, Padiyar A, Page N, Pajak S, Paliege A, Pandey A, Pandey R, Pariani H, Park J, Parrigon M, Passauer J, Patecki M, Patel M, Patel R, Patel T, Patel Z, Paul R, Paul R, Paulsen L, Pavone L, Peixoto A, Peji J, Peng BC, Peng K, Pennino L, Pereira E, Perez E, Pergola P, Pesce F, Pessolano G, Petchey W, Petr EJ, Pfab T, Phelan P, Phillips R, Phillips T, Phipps M, Piccinni G, Pickett T, Pickworth S, Piemontese M, Pinto D, Piper J, Plummer-Morgan J, Poehler D, Polese L, Poma V, Pontremoli R, Postal A, Pötz C, Power A, Pradhan N, Pradhan R, Preiss D, Preiss E, Preston K, Prib N, Price L, Provenzano C, Pugay C, Pulido R, Putz F, Qiao Y, Quartagno R, Quashie-Akponeware M, Rabara R, Rabasa-Lhoret R, Radhakrishnan D, Radley M, Raff R, Raguwaran S, Rahbari-Oskoui F, Rahman M, Rahmat K, Ramadoss S, Ramanaidu S, Ramasamy S, Ramli R, Ramli S, Ramsey T, Rankin A, Rashidi A, Raymond L, Razali WAFA, Read K, Reiner H, Reisler A, Reith C, Renner J, Rettenmaier B, Richmond L, Rijos D, Rivera R, Rivers V, Robinson H, Rocco M, Rodriguez-Bachiller I, Rodriquez R, Roesch C, Roesch J, Rogers J, Rohnstock M, Rolfsmeier S, Roman M, Romo A, Rosati A, Rosenberg S, Ross T, Rossello X, Roura M, Roussel M, Rovner S, Roy S, Rucker S, Rump L, Ruocco M, Ruse S, Russo F, Russo M, Ryder M, Sabarai A, Saccà C, Sachson R, Sadler E, Safiee NS, Sahani M, Saillant A, Saini J, Saito C, Saito S, Sakaguchi K, Sakai M, Salim H, Salviani C, Sammons E, Sampson A, Samson F, Sandercock P, Sanguila S, Santorelli G, Santoro D, Sarabu N, Saram T, Sardell R, Sasajima H, Sasaki T, Satko S, Sato A, Sato D, Sato H, Sato H, Sato J, Sato T, Sato Y, Satoh M, Sawada K, Schanz M, Scheidemantel F, Schemmelmann M, Schettler E, Schettler V, Schlieper GR, Schmidt C, Schmidt G, Schmidt U, Schmidt-Gurtler H, Schmude M, Schneider A, Schneider I, Schneider-Danwitz C, Schomig M, Schramm T, Schreiber A, Schricker S, Schroppel B, Schulte-Kemna L, Schulz E, Schumacher B, Schuster A, Schwab A, Scolari F, Scott A, Seeger W, Seeger W, Segal M, Seifert L, Seifert M, Sekiya M, Sellars R, Seman MR, Shah S, Shah S, Shainberg L, Shanmuganathan M, Shao F, Sharma K, Sharpe C, Sheikh-Ali M, Sheldon J, Shenton C, Shepherd A, Shepperd M, Sheridan R, Sheriff Z, Shibata Y, Shigehara T, Shikata K, Shimamura K, Shimano H, Shimizu Y, Shimoda H, Shin K, Shivashankar G, Shojima N, Silva R, Sim CSB, Simmons K, Sinha S, Sitter T, Sivanandam S, Skipper M, Sloan K, Sloan L, Smith R, Smyth J, Sobande T, Sobata M, Somalanka S, Song X, Sonntag F, Sood B, Sor SY, Soufer J, Sparks H, Spatoliatore G, Spinola T, Squyres S, Srivastava A, Stanfield J, Staplin N, Staylor K, Steele A, Steen O, Steffl D, Stegbauer J, Stellbrink C, Stellbrink E, Stevens W, Stevenson A, Stewart-Ray V, Stickley J, Stoffler D, Stratmann B, Streitenberger S, Strutz F, Stubbs J, Stumpf J, Suazo N, Suchinda P, Suckling R, Sudin A, Sugamori K, Sugawara H, Sugawara K, Sugimoto D, Sugiyama H, Sugiyama H, Sugiyama T, Sullivan M, Sumi M, Suresh N, Sutton D, Suzuki H, Suzuki R, Suzuki Y, Suzuki Y, Suzuki Y, Swanson E, Swift P, Syed S, Szerlip H, Taal M, Taddeo M, Tailor C, Tajima K, Takagi M, Takahashi K, Takahashi K, Takahashi M, Takahashi T, Takahira E, Takai T, Takaoka M, Takeoka J, Takesada A, Takezawa M, Talbot M, Taliercio J, Talsania T, Tamori Y, Tamura R, Tamura Y, Tan CHH, Tan EZZ, Tanabe A, Tanabe K, Tanaka A, Tanaka A, Tanaka N, Tang S, Tang Z, Tanigaki K, Tarlac M, Tatsuzawa A, Tay JF, Tay LL, Taylor J, Taylor K, Taylor K, Te A, Tenbusch L, Teng KS, Terakawa A, Terry J, Tham ZD, Tholl S, Thomas G, Thong KM, Tietjen D, Timadjer A, Tindall H, Tipper S, Tobin K, Toda N, Tokuyama A, Tolibas M, Tomita A, Tomita T, Tomlinson J, Tonks L, Topf J, Topping S, Torp A, Torres A, Totaro F, Toth P, Toyonaga Y, Tripodi F, Trivedi K, Tropman E, Tschope D, Tse J, Tsuji K, Tsunekawa S, Tsunoda R, Tucky B, Tufail S, Tuffaha A, Turan E, Turner H, Turner J, Turner M, Tuttle KR, Tye YL, Tyler A, Tyler J, Uchi H, Uchida H, Uchida T, Uchida T, Udagawa T, Ueda S, Ueda Y, Ueki K, Ugni S, Ugwu E, Umeno R, Unekawa C, Uozumi K, Urquia K, Valleteau A, Valletta C, van Erp R, Vanhoy C, Varad V, Varma R, Varughese A, Vasquez P, Vasseur A, Veelken R, Velagapudi C, Verdel K, Vettoretti S, Vezzoli G, Vielhauer V, Viera R, Vilar E, Villaruel S, Vinall L, Vinathan J, Visnjic M, Voigt E, von-Eynatten M, Vourvou M, Wada J, Wada J, Wada T, Wada Y, Wakayama K, Wakita Y, Wallendszus K, Walters T, Wan Mohamad WH, Wang L, Wang W, Wang X, Wang X, Wang Y, Wanner C, Wanninayake S, Watada H, Watanabe K, Watanabe K, Watanabe M, Waterfall H, Watkins D, Watson S, Weaving L, Weber B, Webley Y, Webster A, Webster M, Weetman M, Wei W, Weihprecht H, Weiland L, Weinmann-Menke J, Weinreich T, Wendt R, Weng Y, Whalen M, Whalley G, Wheatley R, Wheeler A, Wheeler J, Whelton P, White K, Whitmore B, Whittaker S, Wiebel J, Wiley J, Wilkinson L, Willett M, Williams A, Williams E, Williams K, Williams T, Wilson A, Wilson P, Wincott L, Wines E, Winkelmann B, Winkler M, Winter-Goodwin B, Witczak J, Wittes J, Wittmann M, Wolf G, Wolf L, Wolfling R, Wong C, Wong E, Wong HS, Wong LW, Wong YH, Wonnacott A, Wood A, Wood L, Woodhouse H, Wooding N, Woodman A, Wren K, Wu J, Wu P, Xia S, Xiao H, Xiao X, Xie Y, Xu C, Xu Y, Xue H, Yahaya H, Yalamanchili H, Yamada A, Yamada N, Yamagata K, Yamaguchi M, Yamaji Y, Yamamoto A, Yamamoto S, Yamamoto S, Yamamoto T, Yamanaka A, Yamano T, Yamanouchi Y, Yamasaki N, Yamasaki Y, Yamasaki Y, Yamashita C, Yamauchi T, Yan Q, Yanagisawa E, Yang F, Yang L, Yano S, Yao S, Yao Y, Yarlagadda S, Yasuda Y, Yiu V, Yokoyama T, Yoshida S, Yoshidome E, Yoshikawa H, Young A, Young T, Yousif V, Yu H, Yu Y, Yuasa K, Yusof N, Zalunardo N, Zander B, Zani R, Zappulo F, Zayed M, Zemann B, Zettergren P, Zhang H, Zhang L, Zhang L, Zhang N, Zhang X, Zhao J, Zhao L, Zhao S, Zhao Z, Zhong H, Zhou N, Zhou S, Zhu D, Zhu L, Zhu S, Zietz M, Zippo M, Zirino F, Zulkipli FH. Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial. Lancet Diabetes Endocrinol 2024; 12:51-60. [PMID: 38061372 DOI: 10.1016/s2213-8587(23)00322-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND The EMPA-KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. METHODS EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. FINDINGS Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62-0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16-1·59), representing a 50% (42-58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1). INTERPRETATION In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. FUNDING Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council.
Collapse
|
7
|
Feng YH, Wu P, Tang YY, Liu Y, Wang XW, Qiu YZ, Zhang X. [Risks to predict blood loss and cranial nerve injury in carotid body paraganglioma resection]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:1243-1247. [PMID: 38186100 DOI: 10.3760/cma.j.cn115330-20230919-00099] [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: 01/09/2024]
Abstract
Objective: To investigate clinical and imaging parameters to predict blood loss and cranial nerve injury (CNI) following carotid body paraganglioma (CBP) resection. Methods: A retrospective examination of clinical and imaging data was conducted on 63 patients who underwent CBP resection at Xiangya Hospital of Central South University from January 2016 to December 2022, including 23 males and 40 females, aged 26-87 years old. Three imaging parameters including tumor volume, the angle of contact with the internal carotid artery (ICA), and the distance to the base of skull (DTBOS) were gauged using the IMEDPACS software on CTA and MR imaging. The predictive efficacies of age, gender, Shamblin classification, and three imaging parameters for blood loss and CNI following surgery were analysed. Logistic composite parameter models were constructed and their predictive validity was assessed. Results: Multivariate logistic regression analysis underscored that only tumor volume (OR=1.381,95%CI:1.167-1.507,P=0.001) showed significant statistical correlations with blood loss following surgery. Area under curve (AUC) values of 0.910 for receiver operating characteristic (ROC) curves showed a sensitivity of 1.000 and a specificity of 0.694. Tumor volume (OR=1.126,95%CI:1.030-1.231, P=0.002) and DTBOS (OR=0.225,95%CI:0.081-0.630,P=0.005) were significantly associated with postoperative CNI. The analysis of logistic composite model showed AUC values for tumor volume, DTBOS and combination of the two parameters were 0.858, 0.788, and 0.872, respectively. The model for combination of tumor volume and DTBOS also proved superior in predicting postoperative CNI (Z=3.106, P<0.001), with a sensitivity of 0.833 and a specificity of 0.769. Conclusions: Tumor volume and DTBOS emerged as effective predictors for blood loss and/or CNI in patients with CBP resection. Moreover, the logistic composite parameter model outclassed single-parameter models in terms of their predictive clinical value.
Collapse
Affiliation(s)
- Y H Feng
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
| | - P Wu
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Y Y Tang
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Y Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
| | - X W Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Y Z Qiu
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
| | - X Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
| |
Collapse
|
8
|
Wu P, Cowling BJ, Chiu SS, Wong IOL, Yeung WKY. Cost-effectiveness of prophylaxis with palivizumab among high-risk children in Hong Kong: abridged secondary publication. Hong Kong Med J 2023; 29 Suppl 7:37-38. [PMID: 38148655] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Affiliation(s)
- P Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - B J Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - S S Chiu
- Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - I O L Wong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - W K Y Yeung
- Department of Paediatrics and Adolescent Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong SAR, China
| |
Collapse
|
9
|
Chen J, Meng L, Bu C, Zhang C, Wu P. Feature pyramid network-based computer-aided detection and monitoring treatment response of brain metastases on contrast-enhanced MRI. Clin Radiol 2023; 78:e808-e814. [PMID: 37573242 DOI: 10.1016/j.crad.2023.07.009] [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: 05/03/2023] [Revised: 07/06/2023] [Accepted: 07/12/2023] [Indexed: 08/14/2023]
Abstract
AIM To investigate the value of feature pyramid network (FPN)-based computer-aided detection (CAD) of brain metastases (BMs) before and after non-surgical treatment, and to evaluate its performance in monitoring treatment response of BM on contrast-enhanced (CE) magnetic resonance imaging (MRI). MATERIAL AND METHODS Eighty-five cancer patients newly diagnosed with BM who had undergone initial and follow-up three-dimensional (3D) CE MRI at Liaocheng People's Hospital were included retrospectively in this study. Manual detection (MD) was performed by reviewer 1. Computer-aided detection (CAD) was performed by reviewer 2 using uAI Discover-BMs software. The treatment response was assessed by the two reviewers for each patient separately. A paired chi-square test was used to compare the differences in the detection of BM between MD and CAD. Agreement between MD and CAD in monitoring treatment response was assessed by kappa test. RESULTS The sensitivities of MD and CAD on initial 3D CE MRI were 78.65% and 99.13%, respectively. The sensitivities of MD and CAD on follow-up 3D CE MRI were 76.32% and 98.24%, respectively. There was a very good agreement between Reviewer 1 and Reviewer 2 in evaluating the treatment response of BM. CONCLUSION FPN-based CAD has a higher sensitivity of close to 100% and lower false negatives (FNs) for BM detection, compared to MD. Although CAD had a few shortcomings in reflecting changes of BMs after treatment, it had high performance in monitoring treatment response of BM on CE MRI.
Collapse
Affiliation(s)
- J Chen
- Department of MR, Liaocheng People's Hospital, Liaocheng, Shandong Province, 252000, China.
| | - L Meng
- Department of Radiotherapy, Liaocheng People's Hospital, Liaocheng, Shandong Province, 252000, China
| | - C Bu
- Department of MR, Liaocheng People's Hospital, Liaocheng, Shandong Province, 252000, China
| | - C Zhang
- Department of MR, Liaocheng People's Hospital, Liaocheng, Shandong Province, 252000, China
| | - P Wu
- Philips Healthcare, Shanghai, 200072, China
| |
Collapse
|
10
|
Wang J, Meng Y, Han S, Hu C, Lu Y, Wu P, Han L, Xu Y, Xu K. Predictive value of total ischaemic time and T1 mapping after emergency percutaneous coronary intervention in acute ST-segment elevation myocardial infarction. Clin Radiol 2023; 78:e724-e731. [PMID: 37460337 DOI: 10.1016/j.crad.2023.06.010] [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: 09/17/2022] [Revised: 04/05/2023] [Accepted: 06/12/2023] [Indexed: 09/03/2023]
Abstract
AIM To investigate the predictive value of ischaemic time and cardiac magnetic resonance imaging (CMRI) T1 mapping in acute ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PCI). MATERIALS AND METHODS A total of 127 patients with STEMI treated by primary PCI were studied. All patients underwent CMRI with native T1 and extracellular volume (ECV) measurement, 61 of whom also had 4-month follow-up data. The total ischaemic (symptom onset to balloon, S2B) time expressed in minutes was recorded. CMRI cine, T1 mapping, and late gadolinium enhancement (LGE) images were analysed to evaluate left ventricular (LV) function, T1 value, ECV, and myocardial infract (MI) scar characteristics, respectively. The correlation between S2B time and T1 mapping was evaluated. The predictive values of S2B time and T1 mapping for large final infarct size were estimated. RESULTS The incidence of microvascular obstruction (MVO) increased with the prolongation of ischaemia time. Regardless of MVO or not, ECV in myocardial infarction (ECVMI) was significantly correlated with S2B time (r=0.61, p<0.001), while native T1 in MI (T1MI) was not (r=-0.19, p=0.029). In the 4-month follow-up, native T1MI was improved (1385.1 ± 90.4 versus 1288.6 ± 74 ms, p<0.001). Furthermore, ECVMI was independently associated with final larger infarct size (AUC = 0.89, 95% confidence interval [CI] = 0.81-0.98, p<0.001) in multivariable regression analysis. CONCLUSION ECVMI was correlated with total ischaemic time and was an independent predictor of final larger infarct size.
Collapse
Affiliation(s)
- J Wang
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Y Meng
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - S Han
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - C Hu
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Y Lu
- Department of Cardiac Care Unit, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - P Wu
- Philips Healthcare, Shanghai, China
| | - L Han
- Philips Healthcare, Shanghai, China
| | - Y Xu
- Philips Healthcare, Guangzhou, China
| | - K Xu
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
| |
Collapse
|
11
|
Li H, Wang Z, Lan C, Wu P, Zeng N. A Novel Dynamic Multiobjective Optimization Algorithm With Non-Inductive Transfer Learning Based on Multi-Strategy Adaptive Selection. IEEE Trans Neural Netw Learn Syst 2023; PP:1-15. [PMID: 37506016 DOI: 10.1109/tnnls.2023.3295461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer learning (TL) paradigm to solve dynamic multiobjective optimization problems (DMOPs). In particular, based on a scoring system that evaluates environmental changes, the source domain is adaptively constructed with several optional groups to enrich the knowledge. Along with a group of guide solutions, the importance of historical experiences is estimated via the kernel mean matching (KMM) method, which avoids designing strategies to label individuals. The proposed MSAS-DMOA is comprehensively evaluated on 14 DMOPs, and the results show an overwhelming performance improvement in terms of both convergence and diversity as compared with other four popular DMOAs. In addition, ablation studies are also conducted to validate the superiority of the applied strategies in MSAS-DMOA, which can effectively alleviate the negative transfer phenomenon. Without the conventional labeling procedure, the proposed method also yields satisfactory results, which can provide valuable reference for designing other evolutionary transfer optimization (ETO) algorithms.
Collapse
|
12
|
Chong KC, Chan PKS, Lee TC, Goggins WB, Wu P, Lai CKC, Fung KSC. Meteorologically favourable zones for seasonal influenza A and B in Hong Kong: abridged secondary publication. Hong Kong Med J 2023; 29 Suppl 3:19-22. [PMID: 37357586] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023] Open
Affiliation(s)
- K C Chong
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - P K S Chan
- Department of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - T C Lee
- Hong Kong Observatory, Hong Kong SAR, China
| | - W B Goggins
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - P Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - C K C Lai
- Department of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - K S C Fung
- Department of Pathology, United Christian Hospital, Hong Kong SAR, China
| |
Collapse
|
13
|
Ge HP, Song DF, Wu P, Xu HF. Impact of sarcopenia and low muscle attenuation on outcomes of ovarian cancer: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci 2023; 27:4544-4562. [PMID: 37259736 DOI: 10.26355/eurrev_202305_32461] [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: 06/02/2023]
Abstract
OBJECTIVE The aim of this study was to examine the association of sarcopenia and low muscle attenuation with survival and other clinical outcomes in patients with ovarian cancer. MATERIALS AND METHODS Systematic search was done in PubMed, EMBASE and Scopus databases for observational studies that documented the link between sarcopenia and outcomes of interest in patients with ovarian cancer, with long-term survival as a primary outcome. Other outcomes included risk of recurrence, progression-free survival and complications. Pooled effect sizes were reported as hazards ratio (HR), relative risk ratio (RR) or weighted mean difference (WMD). Random effects model was used for the analysis. RESULTS Twenty-two studies were selected, of which all, except one, were retrospective in design. Low skeletal muscle index (SMI, indicating muscle mass) (HR 1.30, 95% CI: 1.07, 1.58) and low muscle quality (HR 1.24, 95% CI: 1.03, 1.49) were associated with poor long-term survival, but not with the risk of recurrence and progression-free survival. Both low skeletal muscle index (SMI) (RR 1.49, 95% CI: 1.13, 1.98) and low muscle quality (RR 1.99, 95% CI: 1.04, 3.79) were associated with increased risk of complications. CONCLUSIONS Both low skeletal muscle mass and low muscle quality showed significant association with poor long-term survival and an increased risk of complications. However, they do not have a significant association with the risk of recurrence and progression-free survival. There is a need for more prospective studies to confirm these associations.
Collapse
Affiliation(s)
- H-P Ge
- Department of Gynecology, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, China.
| | | | | | | |
Collapse
|
14
|
Zhang H, Tang J, Wu P, Li H, Zeng N. A novel attention-based enhancement framework for face mask detection in complicated scenarios. Signal Process Image Commun 2023:116985. [PMID: 37361462 PMCID: PMC10123022 DOI: 10.1016/j.image.2023.116985] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 02/19/2023] [Accepted: 04/10/2023] [Indexed: 06/28/2023]
Abstract
In the context of COVID-19 pandemic prevention and control, it is of vital significance to realize accurate face mask detection via computer vision technique. In this paper, a novel attention improved Yolo (AI-Yolo) model is proposed, which can handle existing challenges in the complicated real-world scenarios with dense distribution, small-size object detection and interference of similar occlusions. In particular, a selective kernel (SK) module is set to achieve convolution domain soft attention mechanism with split, fusion and selection operations; a spatial pyramid pooling (SPP) module is applied to enhance the expression of local and global features, which enriches the receptive field information; and a feature fusion (FF) module is utilized to promote sufficient fusions of multi-scale features from each resolution branch, which adopts basic convolution operators without excessive computational complexity. In addition, the complete intersection over union (CIoU) loss function is adopted in the training stage for accurate positioning. Experiments are carried out on two challenging public face mask detection datasets, and the results demonstrate the superiority of the proposed AI-Yolo against other seven state-of-the-art object detection algorithms, which achieves the best results in terms of mean average precision and F1 score on both datasets. Furthermore, effectiveness of the meticulously designed modules in AI-Yolo is validated through extensive ablation studies. In a word, the proposed AI-Yolo is competent to accomplish face mask detection tasks under extremely complex situations with precise localization and accurate classification.
Collapse
Affiliation(s)
- Hongyi Zhang
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Jun Tang
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| |
Collapse
|
15
|
Xie T, Wang Z, Li H, Wu P, Huang H, Zhang H, Alsaadi FE, Zeng N. Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis. Comput Biol Med 2023; 159:106947. [PMID: 37099976 PMCID: PMC10116157 DOI: 10.1016/j.compbiomed.2023.106947] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/30/2023] [Accepted: 04/15/2023] [Indexed: 04/28/2023]
Abstract
In this paper, a novel deep learning-based medical imaging analysis framework is developed, which aims to deal with the insufficient feature learning caused by the imperfect property of imaging data. Named as multi-scale efficient network (MEN), the proposed method integrates different attention mechanisms to realize sufficient extraction of both detailed features and semantic information in a progressive learning manner. In particular, a fused-attention block is designed to extract fine-grained details from the input, where the squeeze-excitation (SE) attention mechanism is applied to make the model focus on potential lesion areas. A multi-scale low information loss (MSLIL)-attention block is proposed to compensate for potential global information loss and enhance the semantic correlations among features, where the efficient channel attention (ECA) mechanism is adopted. The proposed MEN is comprehensively evaluated on two COVID-19 diagnostic tasks, and the results show that as compared with some other advanced deep learning models, the proposed method is competitive in accurate COVID-19 recognition, which yields the best accuracy of 98.68% and 98.85%, respectively, and exhibits satisfactory generalization ability as well.
Collapse
Affiliation(s)
- Tingyi Xie
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Huixiang Huang
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Hongyi Zhang
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Fuad E Alsaadi
- Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
| |
Collapse
|
16
|
Liu M, Wang Z, Li H, Wu P, Alsaadi FE, Zeng N. AA-WGAN: Attention augmented Wasserstein generative adversarial network with application to fundus retinal vessel segmentation. Comput Biol Med 2023; 158:106874. [PMID: 37019013 DOI: 10.1016/j.compbiomed.2023.106874] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/15/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
In this paper, a novel attention augmented Wasserstein generative adversarial network (AA-WGAN) is proposed for fundus retinal vessel segmentation, where a U-shaped network with attention augmented convolution and squeeze-excitation module is designed to serve as the generator. In particular, the complex vascular structures make some tiny vessels hard to segment, while the proposed AA-WGAN can effectively handle such imperfect data property, which is competent in capturing the dependency among pixels in the whole image to highlight the regions of interests via the applied attention augmented convolution. By applying the squeeze-excitation module, the generator is able to pay attention to the important channels of the feature maps, and the useless information can be suppressed as well. In addition, gradient penalty method is adopted in the WGAN backbone to alleviate the phenomenon of generating large amounts of repeated images due to excessive concentration on accuracy. The proposed model is comprehensively evaluated on three datasets DRIVE, STARE, and CHASE_DB1, and the results show that the proposed AA-WGAN is a competitive vessel segmentation model as compared with several other advanced models, which obtains the accuracy of 96.51%, 97.19% and 96.94% on each dataset, respectively. The effectiveness of the applied important components is validated by ablation study, which also endows the proposed AA-WGAN with considerable generalization ability.
Collapse
|
17
|
Geerardyn A, Zhu M, Wu P, O'Malley J, Nadol JB, Liberman MC, Nakajima HH, Verhaert N, Quesnel AM. Three-dimensional quantification of fibrosis and ossification after cochlear implantation via virtual re-sectioning: Potential implications for residual hearing. Hear Res 2023; 428:108681. [PMID: 36584546 PMCID: PMC10942756 DOI: 10.1016/j.heares.2022.108681] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/13/2022] [Accepted: 12/18/2022] [Indexed: 12/24/2022]
Abstract
Hearing preservation may be achieved initially in the majority of patients after cochlear implantation, however, a significant proportion of these patients experience delayed hearing loss months or years later. A prior histological report in a case of delayed hearing loss suggested a potential cochlear mechanical origin of this hearing loss due to tissue fibrosis, and older case series highlight the frequent findings of post-implantation fibrosis and neoosteogenesis though without a focus on the impact on residual hearing. Here we present the largest series (N = 20) of 3-dimensionally reconstructed cochleae based on digitally scanned histologic sections from patients who were implanted during their lifetime. All patients were implanted with multichannel electrodes via a cochleostomy or an extended round window insertion. A quantified analysis of intracochlear tissue formation was carried out via virtual re-sectioning orthogonal to the cochlear spiral. Intracochlear tissue formation was present in every case. On average 33% (SD 14%) of the total cochlear volume was occupied by new tissue formation, consisting of 26% (SD 12%) fibrous and 7% (SD 6%) bony tissue. The round window was completely covered by fibro-osseous tissue in 85% of cases and was associated with an obstruction of the cochlear aqueduct in 100%. The basal part of the basilar membrane was at least partially abutted by the electrode or new tissue formation in every case, while the apical region, corresponding with a characteristic frequency of < 500 Hz, appeared normal in 89%. This quantitative analysis shows that after cochlear implantation via extended round window or cochleostomy, intracochlear fibrosis and neoossification are present in all cases at anatomical locations that could impact normal inner ear mechanics.
Collapse
Affiliation(s)
- A Geerardyn
- Department of Otolaryngology - Head & Neck Surgery, Harvard Medical School, Boston, MA, USA; Otopathology Laboratory, Massachusetts Eye and Ear, Boston, MA, USA; ExpORL, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - M Zhu
- Otopathology Laboratory, Massachusetts Eye and Ear, Boston, MA, USA
| | - P Wu
- Department of Otolaryngology - Head & Neck Surgery, Harvard Medical School, Boston, MA, USA; Eaton Peabody Laboratories, Massachusetts Eye and Ear, Boston, MA, USA
| | - J O'Malley
- Otopathology Laboratory, Massachusetts Eye and Ear, Boston, MA, USA
| | - J B Nadol
- Department of Otolaryngology - Head & Neck Surgery, Harvard Medical School, Boston, MA, USA; Otopathology Laboratory, Massachusetts Eye and Ear, Boston, MA, USA
| | - M C Liberman
- Department of Otolaryngology - Head & Neck Surgery, Harvard Medical School, Boston, MA, USA; Otopathology Laboratory, Massachusetts Eye and Ear, Boston, MA, USA; Eaton Peabody Laboratories, Massachusetts Eye and Ear, Boston, MA, USA
| | - H H Nakajima
- Department of Otolaryngology - Head & Neck Surgery, Harvard Medical School, Boston, MA, USA; Eaton Peabody Laboratories, Massachusetts Eye and Ear, Boston, MA, USA
| | - N Verhaert
- ExpORL, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - A M Quesnel
- Department of Otolaryngology - Head & Neck Surgery, Harvard Medical School, Boston, MA, USA; Otopathology Laboratory, Massachusetts Eye and Ear, Boston, MA, USA.
| |
Collapse
|
18
|
Zhang Y, Wu P, Li H, Liu Y, Alsaadi FE, Zeng N. DPF-S2S: A novel dual-pathway-fusion-based sequence-to-sequence text recognition model. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.12.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
19
|
Li H, Wu P, Wang Z, Mao J, Alsaadi FE, Zeng N. A generalized framework of feature learning enhanced convolutional neural network for pathology-image-oriented cancer diagnosis. Comput Biol Med 2022; 151:106265. [PMID: 36401968 DOI: 10.1016/j.compbiomed.2022.106265] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/24/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is proposed for cancer detection from histopathology images. To build a highly generalized computer-aided diagnosis (CAD) system, an information refinement unit employing depth- and point-wise convolutions is meticulously designed, where a dual-domain attention mechanism is adopted to focus primarily on the important areas. By deploying a residual fusion unit, context information is further integrated to extract highly discriminative features with strong representation ability. Experimental results demonstrate the merits of the proposed FLE-CNN in terms of feature extraction, which has achieved average sensitivity, specificity, precision, accuracy and F1 score of 0.9992, 0.9998, 0.9992, 0.9997 and 0.9992 in a five-class cancer detection task, and in comparison to some other advanced deep learning models, above indicators have been improved by 1.23%, 0.31%, 1.24%, 0.5% and 1.26%, respectively. Moreover, the proposed FLE-CNN provides satisfactory results in three important diagnosis, which further validates that FLE-CNN is a competitive CAD model with high generalization ability.
Collapse
Affiliation(s)
- Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Jingfeng Mao
- School of Electrical Engineering, Nantong University, Nantong 226019, China
| | - Fuad E Alsaadi
- Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
| |
Collapse
|
20
|
Li H, Zeng N, Wu P, Clawson K. Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision. Expert Syst Appl 2022; 207:118029. [PMID: 35812003 PMCID: PMC9252868 DOI: 10.1016/j.eswa.2022.118029] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 06/17/2022] [Accepted: 06/29/2022] [Indexed: 05/05/2023]
Abstract
In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19 from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust feature learning ability. In particular, a modified residual network with asymmetric convolution and attention mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic and low-level detailed information. Experimental results on two public COVID-19 radiography databases have demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966 and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios. Consequently, one can conclude that this work has both practical value in providing reliable reference to the radiologist and theoretical significance in developing methods to build robust features with strong presentation ability.
Collapse
Affiliation(s)
- Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361102, China
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361102, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361102, China
| | - Kathy Clawson
- School of Computer Science, University of Sunderland, Saint Peter Campus, United Kingdom
| |
Collapse
|
21
|
Wang RN, Wu P, Yao Q, Huangfu SH, Zhang J, Zhang CX, Li L, Zhou HT, Sun QT, Yan R, Wu ZF, Yang MF, Wang YT, Li SJ. [Impact of different obesity patterns on coronary microvascular function in male patients with non-obstructive coronary artery disease]. Zhonghua Xin Xue Guan Bing Za Zhi 2022; 50:1080-1086. [PMID: 36418276 DOI: 10.3760/cma.j.cn112148-20220914-0071] [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: 06/16/2023]
Abstract
Objective: This study sought to investigate the impact of different obesity patterns on coronary microvascular function in male patients with non-obstructive coronary artery disease. Methods: We retrospectively analyzed clinical data of male patients diagnosed with suspected coronary microvascular dysfunction (CMD) in the First Hospital of Shanxi Medical University between December 2015 and August 2021. All patients underwent the one-day rest and stress 13N-ammonia positron emission tomography myocardial perfusion imaging. Overall obesity was defined by body mass index (BMI) ≥28 kg/m2 and abdominal obesity was defined by waist circumference ≥90 cm. Hyperemic myocardial blood flow (MBF)<2.3 ml·min-1·g-1 or coronary flow reserve (CFR)<2.5 were referred as CMD. All patients were grouped based on their BMI and waist circumference. MBF, CFR, the incidence of CMD, hemodynamic parameters, and cardiac function were compared among the groups. Results: A total of 136 patients were included. According to BMI and waist circumference, patients were categorized into 3 groups: control group (n=45), simple abdominal obesity group (n=53) and compound obesity group (n=38). Resting MBF did not differ between groups (F=0.02,P=0.994). Compared with the control group, hyperemic MBF was significantly lower in the simple abdominal obesity and compound obesity groups ((2.82±0.64) ml·min-1·g-1, (2.44±0.85) ml·min-1·g-1 and (2.49±0.71) ml·min-1·g-1, both P<0.05, respectively). Hyperemic MBF was comparable among the groups of patients with obesity (P=0.772). CFR was significantly lower in the simle abdominal obesity group compared with the control group (2.87±0.99 vs. 3.32±0.62,P=0.012). Compared with the control group, CFR tended to be lower in the compound obesity group (3.02±0.91 vs. 3.32±0.62,P=0.117). The incidence of CMD was significantly higher in both the simple abdominal obesity and compound obesity groups than in the control group (62.3%, 52.6% vs. 22.2%, both P<0.01, respectively). Waist circumference was an independent risk factor for male CMD (OR=1.057, 95%CI: 1.013-1.103, P=0.011). Conclusions: In male patients with non-obstructive coronary artery disease, abdominal obesity is associated with decreased coronary microvascular function. Male patients with simple abdominal obesity face the highest risk of CMD.
Collapse
Affiliation(s)
- R N Wang
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Key Laboratory of Molecular Imaging, Taiyuan 030001, China
| | - P Wu
- Province-Ministry Co-construction Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Taiyuan 030001, China
| | - Q Yao
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Key Laboratory of Molecular Imaging, Taiyuan 030001, China
| | - S H Huangfu
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Key Laboratory of Molecular Imaging, Taiyuan 030001, China
| | - J Zhang
- Department of Cardiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - C X Zhang
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Key Laboratory of Molecular Imaging, Taiyuan 030001, China
| | - L Li
- Province-Ministry Co-construction Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Taiyuan 030001, China
| | - H T Zhou
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Key Laboratory of Molecular Imaging, Taiyuan 030001, China
| | - Q T Sun
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Key Laboratory of Molecular Imaging, Taiyuan 030001, China
| | - R Yan
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Key Laboratory of Molecular Imaging, Taiyuan 030001, China
| | - Z F Wu
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Key Laboratory of Molecular Imaging, Taiyuan 030001, China
| | - M F Yang
- Department of Nuclear Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100043, China
| | - Y T Wang
- Department of Nuclear Medicine, Third Affiliated Hospital of Soochow University (First People's Hospital of Changzhou), Changzhou 213003, China
| | - S J Li
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Key Laboratory of Molecular Imaging, Taiyuan 030001, China
| |
Collapse
|
22
|
Li T, Shen M, Hou R, Zhang L, Huang L, Guo P, Wu P, Zhao G. Effects of phytogenic feed on productive performance,
egg quality, antioxidant activity and lipid metabolism of laying hens. J Anim Feed Sci 2022. [DOI: 10.22358/jafs/154977/2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
23
|
Liu H, Wu P, Xie J, Zhang S, Lu Z. Multifocal amyloidosis of the upper aerodigestive tract. QJM 2022; 115:689-690. [PMID: 35699518 DOI: 10.1093/qjmed/hcac145] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Indexed: 02/05/2023] Open
Affiliation(s)
- H Liu
- Shantou University Medical College, 22 Xinling Road, Shantou, 515000, Guangdong, China
- Department of Otolaryngology-Head and Neck Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Second Road, Guangzhou, Guangdong, 510080, China
| | - P Wu
- Department of Otolaryngology-Head and Neck Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Second Road, Guangzhou, Guangdong, 510080, China
- The Second School of Clinical Medicine, Southern Medical University, 1023 Shatainan Road, Guangzhou, 510515, Guangdong, China
| | - J Xie
- Shantou University Medical College, 22 Xinling Road, Shantou, 515000, Guangdong, China
- Department of Otolaryngology-Head and Neck Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Second Road, Guangzhou, Guangdong, 510080, China
| | - S Zhang
- Department of Otolaryngology-Head and Neck Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Second Road, Guangzhou, Guangdong, 510080, China
| | - Z Lu
- Department of Otolaryngology-Head and Neck Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Second Road, Guangzhou, Guangdong, 510080, China
- The Second School of Clinical Medicine, Southern Medical University, 1023 Shatainan Road, Guangzhou, 510515, Guangdong, China
| |
Collapse
|
24
|
Zhang M, Wu P, Duan YL, Jin L, Yang J, Huang S, Liu Y, Hu B, Zhai XW, Wang HS, Fu Y, Li F, Yang XM, Liu AS, Qin S, Yuan XJ, Dong YS, Liu W, Zhou JW, Zhang LP, Jia YP, Wang J, Qu LJ, Dai YP, Guan GT, Sun LR, Jiang J, Liu R, Jin RM, Wang ZJ, Wang XG, Zhang BX, Chen KL, Zhuang SQ, Zhang J, Zhou CJ, Gao ZF, Zheng MC, Zhang Y. [Mid-term efficacy of China Net Childhood Lymphoma-mature B-cell lymphoma 2017 regimen in the treatment of pediatric Burkitt lymphoma]. Zhonghua Er Ke Za Zhi 2022; 60:1011-1018. [PMID: 36207847 DOI: 10.3760/cma.j.cn112140-20220429-00390] [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: 06/16/2023]
Abstract
Objective: To analyze the clinical characteristics of children with Burkitt lymphoma (BL) and to summarize the mid-term efficacy of China Net Childhood Lymphoma-mature B-cell lymphoma 2017 (CNCL-B-NHL-2017) regimen. Methods: Clinical features of 436 BL patients who were ≤18 years old and treated with the CNCL-B-NHL-2017 regimen from May 2017 to April 2021 were analyzed retrospectively. Clinical characteristics of patients at disease onset were analyzed and the therapeutic effects of patients with different clinical stages and risk groups were compared. Survival analysis was performed by Kaplan-Meier method, and Cox regression was used to identify the prognostic factors. Results: Among 436 patients, there were 368 (84.4%) males and 68 (15.6%) females, the age of disease onset was 6.0 (4.0, 9.0) years old. According to the St. Jude staging system, there were 4 patients (0.9%) with stage Ⅰ, 30 patients (6.9%) with stage Ⅱ, 217 patients (49.8%) with stage Ⅲ, and 185 patients (42.4%) with stage Ⅳ. All patients were stratified into following risk groups: group A (n=1, 0.2%), group B1 (n=46, 10.6%), group B2 (n=19, 4.4%), group C1 (n=285, 65.4%), group C2 (n=85, 19.5%). Sixty-three patients (14.4%) were treated with chemotherapy only and 373 patients (85.6%) were treated with chemotherapy combined with rituximab. Twenty-one patients (4.8%) suffered from progressive disease, 3 patients (0.7%) relapsed, and 13 patients (3.0%) died of treatment-related complications. The follow-up time of all patients was 24.0 (13.0, 35.0) months, the 2-year event free survival (EFS) rate of all patients was (90.9±1.4) %. The 2-year EFS rates of group A, B1, B2, C1 and C2 were 100.0%, 100.0%, (94.7±5.1) %, (90.7±1.7) % and (85.9±4.0) %, respectively. The 2-year EFS rates was higher in group A, B1, and B2 than those in group C1 (χ2=4.16, P=0.041) and group C2 (χ2=7.21, P=0.007). The 2-year EFS rates of the patients treated with chemotherapy alone and those treated with chemotherapy combined with rituximab were (79.3±5.1)% and (92.9±1.4)% (χ2=14.23, P<0.001) respectively. Multivariate analysis showed that stage Ⅳ (including leukemia stage), serum lactate dehydrogenase (LDH)>4-fold normal value, and with residual tumor in the mid-term evaluation were risk factors for poor prognosis (HR=1.38,1.23,8.52,95%CI 1.05-1.82,1.05-1.43,3.96-18.30). Conclusions: The CNCL-B-NHL-2017 regimen show significant effect in the treatment of pediatric BL. The combination of rituximab improve the efficacy further.
Collapse
Affiliation(s)
- M Zhang
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - P Wu
- Department of Hematology, Hunan Children's Hospital, Changsha 410007, China
| | - Y L Duan
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - L Jin
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - J Yang
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - S Huang
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - Y Liu
- Department of Pediatric Lymphoma, Beijing GoBroad Boren Hospital, Beijing 100070, China
| | - B Hu
- Department of Pediatric Lymphoma, Beijing GoBroad Boren Hospital, Beijing 100070, China
| | - X W Zhai
- Department of Hematology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - H S Wang
- Department of Hematology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - Y Fu
- Department of Hematology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - F Li
- Hematology & Oncology Department, Children's Hospital Affiliated to Shandong University, Jinan 250022, China
| | - X M Yang
- Hematology & Oncology Department, Children's Hospital Affiliated to Shandong University, Jinan 250022, China
| | - A S Liu
- Department of Hematology & Oncology, Xi'an Children's Hospital, Xi'an 710002, China
| | - S Qin
- Department of Hematology & Oncology, Xi'an Children's Hospital, Xi'an 710002, China
| | - X J Yuan
- Department of Pediatric Hematology/Oncology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Y S Dong
- Department of Pediatric Hematology/Oncology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - W Liu
- Department of Hematology & Oncology, Zhengzhou Children's Hospital, Zhengzhou 450018, China
| | - J W Zhou
- Department of Hematology & Oncology, Zhengzhou Children's Hospital, Zhengzhou 450018, China
| | - L P Zhang
- Department of Pediatrics, Peking University People's Hospital, Beijing 100044, China
| | - Y P Jia
- Department of Pediatrics, Peking University People's Hospital, Beijing 100044, China
| | - J Wang
- Department of Hematology & Oncology, Anhui Children's Hospital, Hefei 230022, China
| | - L J Qu
- Department of Hematology & Oncology, Anhui Children's Hospital, Hefei 230022, China
| | - Y P Dai
- Department of Pediatric Hematology & Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - G T Guan
- Department of Pediatric Hematology & Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - L R Sun
- Department of Pediatric Hematology & Oncology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - J Jiang
- Department of Pediatric Hematology & Oncology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - R Liu
- Department of Hematology, Children's Hospital, Capital Pediatric Research Institute, Beijing 100020, China
| | - R M Jin
- Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Z J Wang
- Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - X G Wang
- Department of Hematology and Oncology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052
| | - B X Zhang
- Department of Pediatrics, Second Hospital of Hebei Medical University, Shijiazhuang 050004, China
| | - K L Chen
- Department of Hematology and Oncology, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430016, China
| | - S Q Zhuang
- Department of Pediatrics, First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou 362002, China
| | - J Zhang
- Department of Hematology & Oncology, the First People's Hospital of Urumqi, Urumqi 830002, China
| | - C J Zhou
- Pathology Department, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Z F Gao
- Department of Pathology, Peking University Third Hospital, Beijing 100191, China
| | - M C Zheng
- Department of Hematology, Hunan Children's Hospital, Changsha 410007, China
| | - Yonghong Zhang
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| |
Collapse
|
25
|
Yi Y, Sun X, Liang B, Liu G, Wu P, Meyerholz D, Engelhardt J. 257 Rapid health decline in young cystic fibrosis transmembrane conductance regulatorG551D ferrets after discontinuation of cystic fibrosis transmembrane conductance regulator modulator. J Cyst Fibros 2022. [DOI: 10.1016/s1569-1993(22)00947-x] [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: 11/05/2022]
|
26
|
Lu S, Jian H, Zhang Y, Song Z, Zhao Y, Wang P, Jiang L, Gong Y, Zhou J, Dong X, Yang N, Fang J, Zhuang W, Cang S, Ma R, Shi J, Wu P, Lu J, Xiang Z, Shi Z, Zhang L, Wang Y. OA03.07 Safety and Efficacy of D-1553 in Patients with KRAS G12C Mutated Non-Small Cell Lung Cancer: A Phase 1 Trial. J Thorac Oncol 2022. [DOI: 10.1016/j.jtho.2022.07.026] [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: 11/30/2022]
|
27
|
Wei Y, Wu P, Cao C. 563P Single-cell profiling analysis reveals that AIF1-induced M2-to-M1 transition of macrophages suppresses the expression of HPV oncogenes and the progression of cervical carcinoma. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.691] [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
|
28
|
Han CM, Zhang LP, Wu P. [A brief discussion on precision nutrition support for severe burn patients from theory to practice]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2022; 38:701-706. [PMID: 36058692 DOI: 10.3760/cma.j.cn501225-20220517-00189] [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
Severe burns can lead to sustained hypermetabolism in the body, resulting in delayed wound healing, and malnutrition, dysfunction, and even death of patients. It is critical to carry out adequate nutritional risk assessment and provide individualized nutritional support to improve the prognosis of patients with severe burns. This paper describes and summarizes precision nutrition support for severe burn patients from theory to clinical practice.
Collapse
Affiliation(s)
- C M Han
- Department of Burn and Wound Repair, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
| | - L P Zhang
- Department of Burn and Wound Repair, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
| | - P Wu
- Department of Burn and Wound Repair, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
| |
Collapse
|
29
|
Wang F, Xu Y, Xiang Y, Wu P, Shen A, Wang P. The feasibility of amide proton transfer imaging at 3 T for bladder cancer: a preliminary study. Clin Radiol 2022; 77:776-783. [PMID: 35985845 DOI: 10.1016/j.crad.2022.07.002] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/31/2022] [Accepted: 07/04/2022] [Indexed: 11/03/2022]
Abstract
AIM To investigate the optimal amide proton transfer (APT) imaging parameters for bladder cancer (BCa), the influence of different protein concentrations and pH values on APT imaging, and to establish the reliability of APT imaging in healthy volunteers and patients with BCa. MATERIALS AND METHODS The optimal APT imaging parameters for BCa were experimentally optimised using cross-linked bovine serum albumin (BSA) phantoms. BSA phantoms were scanned with different values for the saturation power, saturation duration and number of excitations. Meanwhile, BSA phantoms containing different protein concentrations and solutions of different pH levels were scanned. The interobserver agreement of the asymmetric magnetisation transfer ratio (MTRasym) was assessed in 11 healthy volunteers and 18 patients with BCa. RESULTS The optimal scanning scheme consisted of 1 excitation, a saturation power of 2 μT, and a saturation time of 2 s. The APT signal intensity increased as the protein concentration increased and as the pH decreased. The MTRasym showed good concordance for all subjects. The MTRasym of BCa tissue was significantly higher (1.81 ± 0.71) than that of bladder wall in healthy volunteers (0.34 ± 0.12) and normal bladder wall in patients with BCa (0.31 ± 0.11; p<0.001). There was no significant difference between the bladder wall of healthy volunteers and the normal bladder wall of patients with BCa. CONCLUSION APT imaging showed potential value for application in BCa.
Collapse
Affiliation(s)
- F Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
| | - Y Xu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
| | - Y Xiang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
| | - P Wu
- Philips Healthcare, Shanghai, 200072, China
| | - A Shen
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China
| | - P Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China.
| |
Collapse
|
30
|
|
31
|
Han R, Jones CK, Lee J, Zhang X, Wu P, Vagdargi P, Uneri A, Helm PA, Luciano M, Anderson WS, Siewerdsen JH. Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance. Phys Med Biol 2022; 67:10.1088/1361-6560/ac72ef. [PMID: 35609586 PMCID: PMC9801422 DOI: 10.1088/1361-6560/ac72ef] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/24/2022] [Indexed: 01/03/2023]
Abstract
Objective.The accuracy of navigation in minimally invasive neurosurgery is often challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach). We propose a deep learning-based deformable registration method to address such deformations between preoperative MR and intraoperative CBCT.Approach.The registration method uses a joint image synthesis and registration network (denoted JSR) to simultaneously synthesize MR and CBCT images to the CT domain and perform CT domain registration using a multi-resolution pyramid. JSR was first trained using a simulated dataset (simulated CBCT and simulated deformations) and then refined on real clinical images via transfer learning. The performance of the multi-resolution JSR was compared to a single-resolution architecture as well as a series of alternative registration methods (symmetric normalization (SyN), VoxelMorph, and image synthesis-based registration methods).Main results.JSR achieved median Dice coefficient (DSC) of 0.69 in deep brain structures and median target registration error (TRE) of 1.94 mm in the simulation dataset, with improvement from single-resolution architecture (median DSC = 0.68 and median TRE = 2.14 mm). Additionally, JSR achieved superior registration compared to alternative methods-e.g. SyN (median DSC = 0.54, median TRE = 2.77 mm), VoxelMorph (median DSC = 0.52, median TRE = 2.66 mm) and provided registration runtime of less than 3 s. Similarly in the clinical dataset, JSR achieved median DSC = 0.72 and median TRE = 2.05 mm.Significance.The multi-resolution JSR network resolved deep brain deformations between MR and CBCT images with performance superior to other state-of-the-art methods. The accuracy and runtime support translation of the method to further clinical studies in high-precision neurosurgery.
Collapse
Affiliation(s)
- R Han
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - C K Jones
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America
| | - J Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, United States of America
| | - X Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - P Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - P Vagdargi
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
| | - A Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - P A Helm
- Medtronic Inc., Littleton, MA, United States of America
| | - M Luciano
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States of America
| | - W S Anderson
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States of America
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America,The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America,Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America,Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States of America
| |
Collapse
|
32
|
Stahl M, Roehmel J, Eichinger M, Doellinger F, Naehrlich L, Kopp M, Dittrich AM, Sommerburg O, Ray P, Maniktala A, Duncan M, Xu T, Wu P, Joshi A, Mascia M, Tian S, Wielpütz M, Mall M. WS17.02 Long-term efficacy of lumacaftor/ivacaftor (LUM/IVA) in children aged 2 through 5 years with cystic fibrosis (CF) homozygous for the F508del-CFTR mutation (F/F): a phase 2, open-label extension study. J Cyst Fibros 2022. [DOI: 10.1016/s1569-1993(22)00250-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
33
|
Chen YQ, Tian R, Xu W, Fang M, Wu HG, Peng JH, Xie ZY, Wu P, Ma L, You C, Hu X. [A nationalsurveyandresults analysisof seizure prophylaxis after aneurismal subarachnoid hemorrhage]. Zhonghua Yi Xue Za Zhi 2022; 102:76-79. [PMID: 35701087 DOI: 10.3760/cma.j.cn112137-20211117-02571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Investigate theclinical practice of seizure prophylaxis after aneurysmal subarachnoid hemorrhage in Chinese neurosurgeons.Aquestionnaire for this theme was designed and was sent to respondents through the internet.From July 2021 to October 2021, atotal of forty-three eligible questionnaires were collected. All responders come from affiliated hospitals of medical schools in China. Each of these hospitals admitted more than one hundred patients with aneurysmal subarachnoid hemorrhage per year. Only 9.3% (4/43) of responders disagree with the prophylactic use of anticonvulsants. 86.04% (37/43) of responders perform seizure prophylaxis in clinical practice. Sodium valproate is the most commonly used regimen; 94.59% (35/37) of responders who perform prophylaxis chose this drug. The medication period differs sharply fromlessthan 3 daystolongerthan 14 daysamong different hospitals. The use of EEG was insufficient in Chinese patients. A low seizure rate was reported according to the feedback from Chinese neurosurgeons.In China, seizure prophylaxis after subarachnoid hemorrhage was not yet standardized. Clinicians' mastery of relevant knowledge is still not enough. Carrying out high-quality clinical research can help justify the use of anticonvulsants, which could also positively impact rational drug use.
Collapse
Affiliation(s)
- Y Q Chen
- West China School of Medicine, Sichuan University, Chengdu610041
| | - R Tian
- Departmentof Neurosurgery, West China Hospital, Sichuan University, Chengdu610041
| | - W Xu
- West China School of Medicine, Sichuan University, Chengdu610041
| | - M Fang
- West China School of Medicine, Sichuan University, Chengdu610041
| | - H G Wu
- Department of Neurosurgery, People's Hospital of Leshan, Leshan614000
| | - J H Peng
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou646000
| | - Z Y Xie
- Department of Neurosurgery, the Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou310009
| | - P Wu
- Department of Neurosurgery, the First Affiliated Hospital of Harbin Medical University, Harbin150001
| | - L Ma
- Departmentof Neurosurgery, West China Hospital, Sichuan University, Chengdu610041
| | - C You
- Departmentof Neurosurgery, West China Hospital, Sichuan University, Chengdu610041
| | - X Hu
- Departmentof Neurosurgery, West China Hospital, Sichuan University, Chengdu610041
| |
Collapse
|
34
|
Liu YJ, Wu P, An G, Fang Q, Zheng J, Wang YB. [Research advances on the techniques for diagnosing burn wound depth]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2022; 38:481-485. [PMID: 35599424 DOI: 10.3760/cma.j.cn501120-20210518-00195] [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: 06/15/2023]
Abstract
The accurate diagnosis of burn wound depth is particularly important for evaluating the disease prognosis of burn patients. In the past, the diagnosis of burn wound depth often relied on the subjective judgment of doctors. With the continuous development of diagnostic technology, the methods for judging the depth of burn wound have also been updated. This paper mainly summarizes the research progress in the applications of indocyanine green angiography, laser Doppler imaging, laser speckle contrast imaging, and artificial intelligence in the diagnosis of burn wound depth, and compares the advantages and disadvantages of these techniques, so as to provide ideas for accurate diagnosis of burn wound depth.
Collapse
Affiliation(s)
- Y J Liu
- The First Clinical Medical College,Shandong University of Traditional Chinese Medicine, Jinan 250061, China
| | - P Wu
- Department of Plastic Surgery, the First Affiliated Hospital (Shandong Provincial Qianfoshan Hospital), Shandong First Medical University, Jinan Clinical Medicine Research Center for Tissue Engineering Skin Regeneration and Wound Repair, Jinan 250014, China
| | - G An
- Department of Plastic Surgery, the First Affiliated Hospital (Shandong Provincial Qianfoshan Hospital), Shandong First Medical University, Jinan Clinical Medicine Research Center for Tissue Engineering Skin Regeneration and Wound Repair, Jinan 250014, China
| | - Q Fang
- The First Clinical Medical College,Shandong University of Traditional Chinese Medicine, Jinan 250061, China
| | - J Zheng
- The First Clinical Medical College,Shandong University of Traditional Chinese Medicine, Jinan 250061, China
| | - Y B Wang
- Department of Plastic Surgery, the First Affiliated Hospital (Shandong Provincial Qianfoshan Hospital), Shandong First Medical University, Jinan Clinical Medicine Research Center for Tissue Engineering Skin Regeneration and Wound Repair, Jinan 250014, China
| |
Collapse
|
35
|
Wu P, Zhou LN, Xing Y, Sun HP, Wan LJ, Zhou CY, Zhang DD, Zhou XF, Zhang H, Chen MY, Wang YF, Wang NN, Liu WJ, Xu TL, Fu YW, Liu LJ, Yuan D, Chen M, Wang H. [Establishment of morphological reference values for the differential count of white blood cells in peripheral blood smear, as well as nucleated cells and megakaryocytes in bone marrow smear]. Zhonghua Yi Xue Za Zhi 2022; 102:506-512. [PMID: 35184504 DOI: 10.3760/cma.j.cn112137-20210819-01887] [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: 06/14/2023]
Abstract
Objective: To establish the morphological reference values for the differential count of white blood cells in peripheral blood smear as well as nucleated cells and megakaryocytes in bone marrow smear. Methods: From April 2012 to June 2020, 4 221 healthy donors for hematopoietic stem cell transplantation in Hebei Yanda Lu Daopei Hospital were selected. The median age was 36 (3-72) years old, including 2 520 males and 1 701 females. They were divided into four groups according to age: children group, with age≤14 years old [n=334, 11 (3-14) years old], youth group, with age >14 years old and <45 years old [n=2 855, 33 (15-44) years old], middle-aged adult group, with age ≥45 years old and < 60 years old [n=929, 49 (45-59) years old], and older adult group, with age ≥60 years old [n=103, 62 (60-72) years old]. Gender subgroups were established in each age group. According to different hematopoietic characteristics, the children group were divided into two subgroups: children group 1 [n=48, 6 (3-7) years old] and children group 2 [n=286, 11 (8-14) years old]. According to the clinical routine, 100 white blood cells in peripheral blood, 200 nucleated cells in bone marrow, and cell numbers/4.5 cm2 for megakaryocytes were classified and counted. The results of cell count in different age and gender groups were compared, and the reference values of morphological classification were established for different groups with statistical or clinical significance. Results: Due to the existence of statistically significant differences between children and adult groups and different gender subgroups in adults (all P<0.05), the reference values were established for children group and adult gender subgroups. The counts of segmented neutrophils and lymphocytes in peripheral blood were 46.65(43.97-49.32)% and 44.00(10.60-65.10)% in children group 1, 50.73(49.50-51.96)% and 39.55 (38.36-40.74)% in children group 2, and 57.00 (39.00-75.23) % and 33.00 (17.00-52.00) % in adult group, respectively. Bone marrow segmented neutrophils, orthochromatic erythroblasts, and mature lymphocytes were 11.54 (10.68-12.41)%, 14.20 (13.19-15.21)%, and 23.99 (22.06-25.92)% in children group 1, 12.50 (7.00-21.50)%, 15.00(9.50-25.50)%, and 21.02 (20.24-21.81)% in children group 2, 13.50 (7.50-21.00)%, 16.50 (10.50-26.00)%, and 15.50 (7.50-26.00)% in adult male group, and 14.50 (8.00-24.50)%, 14.50 (9.00-23.00)%, and 17.50 (8.50-29.00)% in adult female group, respectively. The myelopoiesis/erythropoiesis ratio in children group, adult male group and adult female group was 1.86∶1 (1.14∶1-3.23∶1), 1.96∶1 (1.12∶1-3.19∶1), 2.22∶1 (1.30∶1-3.69∶1), respectively. The numbers of granular megakaryocytes and thromocytogenic megakaryocytes were 138 (25-567) cells/4.5cm2 and 86 (13-328) cells/4.5 cm2 in children group, and 92 (13-338) cells/4.5 cm2 and 38 (3-162) cells/4.5 cm2 in adult group, respectively. Conclusion: The morphological reference values for the differential count of white blood cells in peripheral blood smear as well as nucleated cells and megakaryocytes in bone marrow smear are successfully established, which is helpful to improve the application of morphological examination in disease screening, diagnosis and monitoring.
Collapse
Affiliation(s)
- P Wu
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - L N Zhou
- Department of Clinical Laboratory, Peking University First Hospital, Beijing 100034, China
| | - Y Xing
- Department of Clinical Laboratory, Peking University First Hospital, Beijing 100034, China
| | - H P Sun
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - L J Wan
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - C Y Zhou
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - D D Zhang
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - X F Zhou
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - H Zhang
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - M Y Chen
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - Y F Wang
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - N N Wang
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - W J Liu
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - T L Xu
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - Y W Fu
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - L J Liu
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - D Yuan
- Department of Clinical Laboratory, Peking University First Hospital, Beijing 100034, China
| | - M Chen
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| | - H Wang
- Department of Clinical Laboratory, Hebei Yanda Lu Daopei Hospital, Sanhe 065201, China
| |
Collapse
|
36
|
Mao J, Li D, Yin S, Wu P, Gao M, Wen S, Xu Q. Management of calcaneus fractures by a new “Below-the-ankle” ilizarov frame: A series of 10 cases. Niger J Clin Pract 2022; 25:1143-1148. [DOI: 10.4103/njcp.njcp_1762_21] [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: 11/04/2022]
|
37
|
Wu P, Li H, Zeng N, Li F. FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public. Image Vis Comput 2022; 117:104341. [PMID: 34848910 PMCID: PMC8612756 DOI: 10.1016/j.imavis.2021.104341] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/08/2021] [Accepted: 11/19/2021] [Indexed: 05/21/2023]
Abstract
Coronavirus disease 2019 (COVID-19) is a world-wide epidemic and efficient prevention and control of this disease has become the focus of global scientific communities. In this paper, a novel face mask detection framework FMD-Yolo is proposed to monitor whether people wear masks in a right way in public, which is an effective way to block the virus transmission. In particular, the feature extractor employs Im-Res2Net-101 which combines Res2Net module and deep residual network, where utilization of hierarchical convolutional structure, deformable convolution and non-local mechanisms enables thorough information extraction from the input. Afterwards, an enhanced path aggregation network En-PAN is applied for feature fusion, where high-level semantic information and low-level details are sufficiently merged so that the model robustness and generalization ability can be enhanced. Moreover, localization loss is designed and adopted in model training phase, and Matrix NMS method is used in the inference stage to improve the detection efficiency and accuracy. Benchmark evaluation is performed on two public databases with the results compared with other eight state-of-the-art detection algorithms. At IoU = 0.5 level, proposed FMD-Yolo has achieved the best precision AP50 of 92.0% and 88.4% on the two datasets, and AP75 at IoU = 0.75 has improved 5.5% and 3.9% respectively compared with the second one, which demonstrates the superiority of FMD-Yolo in face mask detection with both theoretical values and practical significance.
Collapse
Affiliation(s)
- Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Fengping Li
- Institute of Laser and Optoelectronics Intelligent Manufacturing, Wenzhou University, Wenzhou 325035, China
| |
Collapse
|
38
|
Han R, Jones CK, Lee J, Wu P, Vagdargi P, Uneri A, Helm PA, Luciano M, Anderson WS, Siewerdsen JH. Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance. Med Image Anal 2022; 75:102292. [PMID: 34784539 PMCID: PMC10229200 DOI: 10.1016/j.media.2021.102292] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE The accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue - e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach. We report an unsupervised, deep learning-based registration framework to resolve such deformations between preoperative MR and intraoperative CT with fast runtime for neurosurgical guidance. METHOD The framework incorporates subnetworks for MR and CT image synthesis with a dual-channel registration subnetwork (with synthesis uncertainty providing spatially varying weights on the dual-channel loss) to estimate a diffeomorphic deformation field from both the MR and CT channels. An end-to-end training is proposed that jointly optimizes both the synthesis and registration subnetworks. The proposed framework was investigated using three datasets: (1) paired MR/CT with simulated deformations; (2) paired MR/CT with real deformations; and (3) a neurosurgery dataset with real deformation. Two state-of-the-art methods (Symmetric Normalization and VoxelMorph) were implemented as a basis of comparison, and variations in the proposed dual-channel network were investigated, including single-channel registration, fusion without uncertainty weighting, and conventional sequential training of the synthesis and registration subnetworks. RESULTS The proposed method achieved: (1) Dice coefficient = 0.82±0.07 and TRE = 1.2 ± 0.6 mm on paired MR/CT with simulated deformations; (2) Dice coefficient = 0.83 ± 0.07 and TRE = 1.4 ± 0.7 mm on paired MR/CT with real deformations; and (3) Dice = 0.79 ± 0.13 and TRE = 1.6 ± 1.0 mm on the neurosurgery dataset with real deformations. The dual-channel registration with uncertainty weighting demonstrated superior performance (e.g., TRE = 1.2 ± 0.6 mm) compared to single-channel registration (TRE = 1.6 ± 1.0 mm, p < 0.05 for CT channel and TRE = 1.3 ± 0.7 mm for MR channel) and dual-channel registration without uncertainty weighting (TRE = 1.4 ± 0.8 mm, p < 0.05). End-to-end training of the synthesis and registration subnetworks also improved performance compared to the conventional sequential training strategy (TRE = 1.3 ± 0.6 mm). Registration runtime with the proposed network was ∼3 s. CONCLUSION The deformable registration framework based on dual-channel MR/CT registration with spatially varying weights and end-to-end training achieved geometric accuracy and runtime that was superior to state-of-the-art baseline methods and various ablations of the proposed network. The accuracy and runtime of the method may be compatible with the requirements of high-precision neurosurgery.
Collapse
Affiliation(s)
- R Han
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - C K Jones
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States
| | - J Lee
- Department of Radiation Oncology, Johns Hopkins University, Baltimore, MD, United States
| | - P Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - P Vagdargi
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States
| | - A Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - P A Helm
- Medtronic Inc., Littleton, MA, United States
| | - M Luciano
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States
| | - W S Anderson
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States; The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States; Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States; Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States.
| |
Collapse
|
39
|
Shen M, Li T, Lu J, Qu L, Wang K, Hou Q, Zhang Z, Guo X, Zhao W, Wu P. Effects of Supplementation of Moringa Oleifera Leaf Powder on Some Reproductive Performance in Laying Hens. Braz J Poult Sci 2022. [DOI: 10.1590/1806-9061-2021-1537] [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] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- M Shen
- Jiangsu University of Science and Technology, P.R.China; Chinese Academy of Agricultural Sciences, P.R.China
| | - T Li
- Jiangsu University of Science and Technology, P.R.China
| | - J Lu
- Chinese Academy of Agricultural Sciences, P.R.China
| | - L Qu
- Chinese Academy of Agricultural Sciences, P.R.China
| | - K Wang
- Chinese Academy of Agricultural Sciences, P.R.China
| | - Q Hou
- Jiangsu University of Science and Technology, P.R.China
| | - Z Zhang
- Jiangsu University of Science and Technology, P.R.China
| | - X Guo
- Jiangsu University of Science and Technology, P.R.China; Chinese Academy of Agricultural Sciences, P.R. China
| | - W Zhao
- Jiangsu University of Science and Technology, P.R.China; Chinese Academy of Agricultural Sciences, P.R. China
| | - P Wu
- Jiangsu University of Science and Technology, P.R.China; Chinese Academy of Agricultural Sciences, P.R. China
| |
Collapse
|
40
|
Yi Y, Sun X, Liang B, Wu P, Wang H, Norris A, Engelhardt J. 628: Abnormalities in glucose metabolism differ between early and late onset of CF pancreatitis in CFTR-G551D-KI ferrets. J Cyst Fibros 2021. [DOI: 10.1016/s1569-1993(21)02051-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
41
|
Hu Q, Xing JY, Wu P, Huang TT, Yang XD. [Role of the ES-62 protein derived from Acanthocheilonema viteae in regulation of immune dysregulation diseases: a review]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2021; 34:204-211. [PMID: 35537846 DOI: 10.16250/j.32.1374.2021141] [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
ES-62 is a phosphorylcholine-containing, 62 kDa glycoprotein derived from the excretory-secretory product of Acanthocheilonema viteae, which is effective for the prevention and treatment of immune dysregulation diseases through triggering activation of immune cells, such as dendritic cells, mononuclear macrophages and regulatory B cells and mediating immune responses. Recently, the role of the ES-62 protein in the management of allergic, autoimmune and metabolic diseases has been paid much attention. This review summarizes the regulatory role of the ES-62 protein in immune dysregulation diseases and the underlying mechanisms, so as to provide insights into future experimental studies.
Collapse
Affiliation(s)
- Q Hu
- Anhui Provincial Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui 233030, China
| | - J Y Xing
- Anhui Provincial Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui 233030, China
| | - P Wu
- Anhui Provincial Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui 233030, China
| | - T T Huang
- Anhui Provincial Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui 233030, China
| | - X D Yang
- Anhui Provincial Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui 233030, China
- Department of Microbiology and Parasitology, Bengbu Medical College, Bengbu, Anhui 233030, China
| |
Collapse
|
42
|
Sun X, Liang B, Yi Y, Wang H, Wu P, Bartels D, Engelhardt J. 613: Impact of VX-770 on fertility, pregnancy, and lactation in second-generation CFTRG551D/G551D ferrets. J Cyst Fibros 2021. [DOI: 10.1016/s1569-1993(21)02036-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
43
|
Uneri A, Wu P, Jones CK, Vagdargi P, Han R, Helm PA, Luciano MG, Anderson WS, Siewerdsen JH. Deformable 3D-2D registration for high-precision guidance and verification of neuroelectrode placement. Phys Med Biol 2021; 66. [PMID: 34644684 DOI: 10.1088/1361-6560/ac2f89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 10/13/2021] [Indexed: 11/11/2022]
Abstract
Purpose.Accurate neuroelectrode placement is essential to effective monitoring or stimulation of neurosurgery targets. This work presents and evaluates a method that combines deep learning and model-based deformable 3D-2D registration to guide and verify neuroelectrode placement using intraoperative imaging.Methods.The registration method consists of three stages: (1) detection of neuroelectrodes in a pair of fluoroscopy images using a deep learning approach; (2) determination of correspondence and initial 3D localization among neuroelectrode detections in the two projection images; and (3) deformable 3D-2D registration of neuroelectrodes according to a physical device model. The method was evaluated in phantom, cadaver, and clinical studies in terms of (a) the accuracy of neuroelectrode registration and (b) the quality of metal artifact reduction (MAR) in cone-beam CT (CBCT) in which the deformably registered neuroelectrode models are taken as input to the MAR.Results.The combined deep learning and model-based deformable 3D-2D registration approach achieved 0.2 ± 0.1 mm accuracy in cadaver studies and 0.6 ± 0.3 mm accuracy in clinical studies. The detection network and 3D correspondence provided initialization of 3D-2D registration within 2 mm, which facilitated end-to-end registration runtime within 10 s. Metal artifacts, quantified as the standard deviation in voxel values in tissue adjacent to neuroelectrodes, were reduced by 72% in phantom studies and by 60% in first clinical studies.Conclusions.The method combines the speed and generalizability of deep learning (for initialization) with the precision and reliability of physical model-based registration to achieve accurate deformable 3D-2D registration and MAR in functional neurosurgery. Accurate 3D-2D guidance from fluoroscopy could overcome limitations associated with deformation in conventional navigation, and improved MAR could improve CBCT verification of neuroelectrode placement.
Collapse
Affiliation(s)
- A Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, United States of America
| | - P Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, United States of America
| | - C K Jones
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - P Vagdargi
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - R Han
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, United States of America
| | - P A Helm
- Medtronic, Littleton, MA 01460, United States of America
| | - M G Luciano
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore, MD 21287, United States of America
| | - W S Anderson
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore, MD 21287, United States of America
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, United States of America.,Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, United States of America.,Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, United States of America.,Department of Neurosurgery, Johns Hopkins Medicine, Baltimore, MD 21287, United States of America
| |
Collapse
|
44
|
Wang SY, Yang XD, Gao HY, Xing JY, Hu Q, Huang TT, Wu P, Zhao YT, Liu HW, Liu WY, Wang HN, Zhou R, Chu L. [Analysis of components of proteins from Echinococcus granulosus cyst fluid]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2021; 33:476-482. [PMID: 34791845 DOI: 10.16250/j.32.1374.2021111] [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: 11/27/2022]
Abstract
OBJECTIVE To analyze the components of proteins from Echinococcus granulosus cyst fluid using the shotgun method, and to identify the active components with potential regulatory effects for immune dysregulation diseases. METHODS The E. granulosus cyst fluid was collected aseptically from the hepatic cysts of patients with cystic echinococcosis, and characterized by liquid chromatography (LC) tandem mass spectrometry (MS/MS) following digestion with trypsin. The protein data were searched using the software MaxQuant version 1.6.1.0 and the cellular components, molecular functions, and biological processes of the identified proteins were analyzed using the Gene Ontology (GO) method. RESULTS The E. granulosus cyst fluid separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) had a relative molecular mass of 25 to 70 kDa. LS-MS/MS analysis identified 37 proteins, including 32 known proteins and 5 unknown proteins. At least 4 proteins were preliminarily found to exhibit potential regulatory effects for immune dysregulation diseases, including antigen B, glutathione-S-transferase (GST), thioredoxin peroxidase (TPX) and malate dehydrogenase (MDH). GO enrichment analysis showed that the identified proteins had 149 molecular functions and were involved in 341 biological processes. CONCLUSIONS E. granulosus cyst fluid has a variety of protein components, and four known proteins are preliminarily identified to be associated with immune dysregulation diseases.
Collapse
Affiliation(s)
- S Y Wang
- Department of Pediatrics, The First Affiliated Hospital of Bengbu Medical College, Bengbu 233000, China.,Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China.,Co-first authors
| | - X D Yang
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China.,Department of Microbiology and Parasitology, Bengbu Medical College, China.,Co-first authors
| | - H Y Gao
- Department of Pediatrics, The First Affiliated Hospital of Bengbu Medical College, Bengbu 233000, China.,Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China
| | - J Y Xing
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China
| | - Q Hu
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China
| | - T T Huang
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China
| | - P Wu
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China
| | - Y T Zhao
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China
| | - H W Liu
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China
| | - W Y Liu
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China
| | - H N Wang
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, China
| | - R Zhou
- Department of Pediatrics, The First Affiliated Hospital of Bengbu Medical College, Bengbu 233000, China
| | - L Chu
- Department of General Surgery, The Second Affiliated Hospital of Bengbu Medical College, China
| |
Collapse
|
45
|
Muijsers HEC, Wu P, Van Der Heijden OWH, Wijnberger LDE, Van Bijsterveldt C, Buijs C, Pagels J, Toennies P, Heiden S, Roeleveld N, Maas AHEM. Home blood pressure monitoring detects unrevealed hypertension in women with a history of preeclampsia: results of the BP-PRESELF study. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.2344] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background/Introduction
The risk of cardiovascular disease more than doubles after hypertensive disorders of pregnancy. As early onset chronic hypertension contributes to cardiovascular risk, implementation of screening strategies, using home blood pressure monitoring (HBPM), may help to improve long-term cardiovascular health.
Purpose
We evaluated whether HBPM among women with a history of preeclampsia/HELLP syndrome is feasible for early detection and management of hypertension.
Methods
The Blood Pressure after PREeclampsia by SELF monitoring (BP-PRESELF) is a multicenter randomized controlled trial. We recruited 198 women with a mean age of 45 years, approximately 12 years after the index pregnancy. Participants were randomized to intervention group with HBPM for the duration of one year or the control group with 'usual care' (Figure 1). The primary outcome was feasibility of HBPM during 1 year of follow-up, defined as protocol adherence, protocol persistence and patient acceptance. Secondary outcomes were blood pressure levels and prevalence of hypertension.
Results
The baseline characteristics did not show statistically significant differences between groups, except for years since index pregnancy. Especially blood pressure levels were very similar at time of inclusion.
Protocol adherence decreased during the first 6 months, after which it stabilized (Figure 2A). Protocol persistence remained high throughout follow-up (Figure 2B). During the study period, 33 women (34%) in the intervention group were diagnosed with hypertension versus only 10 women (11%) in the control group. At 1-year follow-up, mean systolic blood pressure (SD) was 120.4 (11.6) mm Hg in the intervention group versus 126.1 (14.3) mm Hg in the control group, P=0.003. Mean diastolic blood pressure (SD) values were 77.2 (8.0) mm Hg versus 81.7 (9.4) mm Hg, P<0.001, respectively. Adjusted systolic and diastolic differences (95% confidence interval) were −6.81 (−10.17, −3.45) and −4.93 (−7.26, −2.61) mm Hg, with 80% less hypertension at 1-year follow-up in the intervention group.
Conclusion(s)
HBPM appears to be feasible for follow-up of blood pressure in women after preeclampsia/HELLP syndrome, while it detected hypertension and reduced blood pressure levels after intervention in one-third of women in this group.
Funding Acknowledgement
Type of funding sources: Public grant(s) – EU funding. Main funding source(s): INTERREG-V-A program Germany-The Netherlands “Zorg Verbindt”, co-financed by the European Union (EU), Ministry for Economy, Innovation, Digitalization and Energy of the Federal State of Nordrhein-Westfalen (Germany), and the Province of Gelderland (The Netherlands). CONSORT flowchartProtocol adherence (A) and persistence (B)
Collapse
Affiliation(s)
- H E C Muijsers
- Radboud University Medical Center, Department of Cardiology, Nijmegen, Netherlands (The)
| | - P Wu
- Keele University, Keele Cardiovascular Research Group, Keele, United Kingdom
| | - O W H Van Der Heijden
- Radboud University Medical Center, Department of Obstetrics and Gynaecology, Nijmegen, Netherlands (The)
| | - L D E Wijnberger
- Rijnstate Hospital, Department of Obstetrics and Gynaecology, Arnhem, Netherlands (The)
| | - C Van Bijsterveldt
- Canisius - Wilhelmina Hospital (CWZ), Department of Obstetrics and Gynaecology, Nijmegen, Netherlands (The)
| | - C Buijs
- Maasziekenhuis Pantein, Department of Obstetrics and Gynaecology, Boxmeer, Netherlands (The)
| | - J Pagels
- St. Josef Hospital, Department of Obstetrics and Gynaecology, Moers, Germany
| | - P Toennies
- Bethanien Hospital, Department of Obstetrics and Gynaecology, Moers, Germany
| | - S Heiden
- St. Antonius Hospital, Department of Obstetrics and Gynaecology, Kleve, Germany
| | - N Roeleveld
- Radboud University Medical Center, Radboud Institute for Health Sciences, Department for Health Evidence, Nijmegen, Netherlands (The)
| | - A H E M Maas
- Radboud University Medical Center, Department of Cardiology, Nijmegen, Netherlands (The)
| |
Collapse
|
46
|
Ramakrishnan K, Wu P, Black C, Keeping S, Upadhyay N, Mojebi A. 880P Systematic literature review (SLR) of randomized controlled trials in patients (pts) with newly diagnosed locally advanced (LA) head and neck squamous cell carcinoma (HNSCC) ineligible for surgery. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.08.1290] [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] Open
|
47
|
Zhang X, Chen F, He M, Wu P, Zhou K, Zhang T, Chu M, Zhang G. miR-7 regulates the apoptosis of chicken primary myoblasts through the KLF4 gene. Br Poult Sci 2021; 63:39-45. [PMID: 34287083 DOI: 10.1080/00071668.2021.1958299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
1. MicroRNAs (miRNAs) play a vital role in the proliferation, differentiation, and apoptosis of myoblasts. However, the effect of miR-7 on the apoptosis of chicken primary myoblasts (CPMs) and its mechanism is still unclear.2. In this study, the expression of apoptosis marker genes (RAF1, Caspase3, Caspase9, Cytc, Fas) in CPMs was significantly increased after transfection of miR-7 mimic. The expression of the apoptosis marker genes in CPMs was significantly reduced after transfection with miR-7 inhibitor. Flow cytometry showed that the late apoptosis rate of the mimic group was significantly higher than the negative control (NC). The viable cells of the mimic group were significantly lower than the NC. In contrast, inhibition of miR-7 had the opposite effect.3. The dual-luciferase assay showed that the KLF4 was a target gene of miR-7. The rescue experiment showed that the KLF4 gene could attenuate the effect of miR-7 on the expression of apoptosis marker genes in CPMs.4. Determination of the function the KLF4 gene showed that the expression of the apoptosis marker genes in CPMs decreased significantly compared with the NC after its overexpression. Inhibition of KLF4 gene had the opposite effect. Flow cytometry showed that overexpression of the KLF4 gene inhibited early apoptosis of myoblasts (P ≤ 0.01), while interference with the KLF4 gene could promote early apoptosis of myoblasts (P ≤ 0.001).5. The results demonstrated, for the first time, that miR-7 promotes apoptosis in chicken primary myoblasts by regulating the expression of the KLF4 gene.
Collapse
Affiliation(s)
- X Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - F Chen
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - M He
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - P Wu
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - K Zhou
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - T Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - M Chu
- Institute of Animal Science, Chinese Academy of Agricultral Sciences, Beijing, China
| | - G Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| |
Collapse
|
48
|
Wu P, Tian Y, Chen G, Wang B, Gui L, Xi L, Ma X, Fang Y, Zhu T, Wang D, Meng L, Xu G, Wang S, Ma D, Zhou J. Correction: Ubiquitin B: an essential mediator of trichostatin A-induced tumor-selective killing in human cancer cells. Cell Death Differ 2021; 29:1299. [PMID: 34331026 DOI: 10.1038/s41418-021-00829-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- P Wu
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Y Tian
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - G Chen
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - B Wang
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - L Gui
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - L Xi
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - X Ma
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Y Fang
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - T Zhu
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - D Wang
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - L Meng
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - G Xu
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - S Wang
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - D Ma
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
| | - J Zhou
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
| |
Collapse
|
49
|
Wu P, Zhou K, Zhang L, Li P, He M, Zhang X, Ye H, Zhang Q, Wei Q, Zhang G. High-throughput sequencing reveals crucial miRNAs in skeletal muscle development of Bian chicken. Br Poult Sci 2021; 62:658-665. [PMID: 33874802 DOI: 10.1080/00071668.2021.1919994] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
1. Growth performance is significant for chickens. MicroRNAs (miRNAs) have been found to play important roles in the post-transcriptional regulation of skeletal muscle growth. However, the mechanism of miRNAs in this process has not been elucidated.2. This study involved collecting leg muscle from slow- and fast-growing groups of Bian chicken at 16 weeks of age for high-throughput sequencing. A total of 42 differentially expressed miRNAs (DEMs) were identified. Among them, 22 DEMs were up-regulated and 20 DEMs were down-regulated.3. Biological process terms, relating to growth, were found by GO enrichment for target genes of DEMs and KEGG pathway analysis of target genes. This revealed some significantly enriched pathways closely related to skeletal muscle development, such as the calcium signalling pathway, ECM-receptor interaction, lysine degradation, apoptosis and tight junctions. Network interaction analysis of DEMs and target genes showed that the top fifty hub genes were targeted by thirteen DEMs.4. Four important miRNAs (novel_miR_158, novel_miR_144, novel_miR_291, and miR-205a) as well as some other valuable miRNAs, such as gga-miR-214 and gga-miR-3525 were identified. The qPCR results of five DEMs were highly consistent with that of sequencing between the two groups, which proved the reliability of miRNA-seq.5. The study will help to improve the molecular mechanism of miRNAs in chickens and guide future experiments concerning miRNA function in chicken growth.
Collapse
Affiliation(s)
- P Wu
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - K Zhou
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - L Zhang
- College of Animal Science, Shanxi Agricultural University, Taiyuan, China
| | - P Li
- College of Animal Science, Shanxi Agricultural University, Taiyuan, China
| | - M He
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - X Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| | - H Ye
- College of Animal Science, Shanxi Agricultural University, Taiyuan, China
| | - Q Zhang
- College of Animal Science, Shanxi Agricultural University, Taiyuan, China
| | - Q Wei
- College of Animal Science, Shanxi Agricultural University, Taiyuan, China
| | - G Zhang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, China
| |
Collapse
|
50
|
Uneri A, Wu P, Jones CK, Ketcha MD, Vagdargi P, Han R, Helm PA, Luciano M, Anderson WS, Siewerdsen JH. Data-Driven Deformable 3D-2D Registration for Guiding Neuroelectrode Placement in Deep Brain Stimulation. Proc SPIE Int Soc Opt Eng 2021; 11598:115981B. [PMID: 35982943 PMCID: PMC9382676 DOI: 10.1117/12.2582160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PURPOSE Deep brain stimulation is a neurosurgical procedure used in treatment of a growing spectrum of movement disorders. Inaccuracies in electrode placement, however, can result in poor symptom control or adverse effects and confound variability in clinical outcomes. A deformable 3D-2D registration method is presented for high-precision 3D guidance of neuroelectrodes. METHODS The approach employs a model-based, deformable algorithm for 3D-2D image registration. Variations in lead design are captured in a parametric 3D model based on a B-spline curve. The registration is solved through iterative optimization of 16 degrees-of-freedom that maximize image similarity between the 2 acquired radiographs and simulated forward projections of the neuroelectrode model. The approach was evaluated in phantom models with respect to pertinent imaging parameters, including view selection and imaging dose. RESULTS The results demonstrate an accuracy of (0.2 ± 0.2) mm in 3D localization of individual electrodes. The solution was observed to be robust to changes in pertinent imaging parameters, which demonstrate accurate localization with ≥20° view separation and at 1/10th the dose of a standard fluoroscopy frame. CONCLUSIONS The presented approach provides the means for guiding neuroelectrode placement from 2 low-dose radiographic images in a manner that accommodates potential deformations at the target anatomical site. Future work will focus on improving runtime though learning-based initialization, application in reducing reconstruction metal artifacts for 3D verification of placement, and extensive evaluation in clinical data from an IRB study underway.
Collapse
Affiliation(s)
- A. Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD
| | - P. Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD
| | - C. K. Jones
- Department of Computer Science, Johns Hopkins University, Baltimore MD
| | - M. D. Ketcha
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD
| | - P. Vagdargi
- Department of Computer Science, Johns Hopkins University, Baltimore MD
| | - R. Han
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD
| | | | - M. Luciano
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore MD
| | - W. S. Anderson
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore MD
| | - J. H. Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD
- Department of Computer Science, Johns Hopkins University, Baltimore MD
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore MD
| |
Collapse
|