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Álvarez-León F, Rosado-Aguilar JA, Gamboa-Angulo M, Flota-Burgos GJ, Martin J, Reyes F. Anthelmintic activity and chemical profile of native plant extracts from the Yucatan Peninsula against Toxocara canis. Acta Trop 2024; 255:107214. [PMID: 38663537 DOI: 10.1016/j.actatropica.2024.107214] [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: 12/12/2023] [Revised: 03/26/2024] [Accepted: 04/09/2024] [Indexed: 05/12/2024]
Abstract
Toxocara canis can produce the "larva migrans" syndrome in humans, and in puppies, it can cause severe digestive disorders. The most used treatments are based on anthelmintics, although there are reports of anthelmintic (AH) resistance. The Yucatan Peninsula has a great variety of plant species whose AH properties are still unknown. The objective of this study was to evaluate the in vitro AH activity of ethanolic (EE), methanolic (ME) and aqueous (AE) extracts from the leaves of five native plant species of the Yucatan Peninsula on T. canis eggs of dogs from Merida, Yucatan. As part of a screening, the EE of the plants Alseis yucatanensis, Calea jamaicensis, Cameraria latifolia, Macrocepis diademata, and Parathesis cubana were evaluated at doses of 2400 and 3600 μg/ml. The EE and AE of A. yucatanensis and M. diademata presented high percentages (≥ 91.3%) of inhibition of the larval development of T. canis after six days of exposure. The lowest LC50 and LC99 was presented by the ME from A. yucatanensis (255.5 and 629.06 µg/ml, respectively) and the ME from M. diademata (222.4 and 636.5 µg/ml, respectively), and the AE from A. yucatanenesis (LC50 of 535.9 µg/ml). Chemical profiling of the most potent AH extract (Alseis yucatanensis) was carried out by LC-UV-HRMS. Data from the ME and AE from this plant indicated the presence of the known glucosylngoumiensine, kaempferol 3,7-diglucosyde, uvaol, linoleic acid and linolenic acid together with unknown alkaloids. The EE, ME and AE from leaves of M. diademata and A. yucatanensis could be developed as natural alternatives to control T. canis.
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Affiliation(s)
- F Álvarez-León
- Faculty of Veterinary Medicine and Zootechnics, Autonomous University of Yucatan, Km 15.5, Merida- Xmatkuil highway, CP 97000 Merida, Yucatan, Mexico
| | - J A Rosado-Aguilar
- Faculty of Veterinary Medicine and Zootechnics, Autonomous University of Yucatan, Km 15.5, Merida- Xmatkuil highway, CP 97000 Merida, Yucatan, Mexico.
| | - M Gamboa-Angulo
- Biotechnology Unit, Scientific Research Center of Yucatan, Street 43 number 130 × 32 and 34, CP 97205 Merida, Yucatan, Mexico
| | - G J Flota-Burgos
- Faculty of Veterinary Medicine and Zootechnics, Autonomous University of Yucatan, Km 15.5, Merida- Xmatkuil highway, CP 97000 Merida, Yucatan, Mexico
| | - J Martin
- Fundación MEDINA, Avenida del conocimiento, 34 Parque Tecnológico de Ciencias de la Salud, Granada 18016, España
| | - F Reyes
- Fundación MEDINA, Avenida del conocimiento, 34 Parque Tecnológico de Ciencias de la Salud, Granada 18016, España
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Miller C, Ettridge K, Pettigrew S, Wittert G, Coveney J, Wakefield M, Roder D, Durkin S, Martin J, Kay E, Dono J. Warning labels for sugar-sweetened beverages and fruit juice: evaluation of 27 different labels on health effects, sugar content, energy and exercise equivalency. Public Health 2024; 230:138-148. [PMID: 38547760 DOI: 10.1016/j.puhe.2024.01.026] [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: 08/15/2023] [Revised: 12/19/2023] [Accepted: 01/26/2024] [Indexed: 04/16/2024]
Abstract
OBJECTIVES Front-of-pack warning labels may reduce consumption of sugar-sweetened beverages, potentially mitigating negative health outcomes. Comparisons between different warning label types to inform future research and policy directions are lacking. This study compared 27 warning labels across six message types for their potential to reduce sugar-sweetened beverage consumption. DESIGN AND METHODS A national sample of regular soda (n = 2578) and juice (n = 1048) consumers aged 14-60 years participated in an online survey. Participants evaluated randomly allocated labels; one from each of six warning label sets (health-graphic, sugar-pictogram, sugar-text, exercise equivalents, health-text, energy information) on four measures of perceived effectiveness (PE: overall effectiveness, discourage from drinking, emotional response, persuasive potential). Participants could also provide open comments. A general linear model compared differences in mean scores across label sets for each measure of PE. RESULTS PE ratings differed significantly between label sets. Labels clearly quantifying sugar content (sugar-teaspoons) received consistently high PE ratings, whereas 'high in sugar' labels did not. Health-graphic labels were rated highly across all PE measures except persuasive potential. Exercise labels only rated highly on persuasive potential. Health-text results were mixed, and energy labels were consistently low. CONCLUSIONS Simple, factual labels were easily interpreted and perceived as most effective. Labels quantifying sugar content were consistently high performers and should be advanced into policy to help decrease overconsumption of sugar-sweetened beverages.
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Affiliation(s)
- C Miller
- School of Public Health, The University of Adelaide, Adelaide, Australia; Health Policy Centre, South Australian Health and Medical Research Institute, Adelaide, Australia.
| | - K Ettridge
- Health Policy Centre, South Australian Health and Medical Research Institute, Adelaide, Australia; School of Psychology, The University of Adelaide, Adelaide, Australia
| | - S Pettigrew
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - G Wittert
- Freemasons Centre for Male Health and Wellbeing, South Australian Health and Medical Research Institute and Faculty of Health Sciences, University of Adelaide, Adelaide, Australia
| | - J Coveney
- College of Nursing and Health Sciences, Flinders University, Adelaide, Australia
| | - M Wakefield
- Centre for Behavioural Research in Cancer, Cancer Council Victoria, Melbourne, Australia; School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - D Roder
- Cancer Epidemiology and Population Health, University of South Australia, Australia
| | - S Durkin
- Centre for Behavioural Research in Cancer, Cancer Council Victoria, Melbourne, Australia; School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - J Martin
- Food for Health Alliance, Cancer Council Victoria, Melbourne, Australia
| | - E Kay
- Health Policy Centre, South Australian Health and Medical Research Institute, Adelaide, Australia; College of Education Psychology and Social Work, Flinders University, Adelaide, Australia
| | - J Dono
- Health Policy Centre, South Australian Health and Medical Research Institute, Adelaide, Australia; School of Psychology, The University of Adelaide, Adelaide, Australia
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Jenkins DR, Auckland C, Chadwick C, Dodgson AR, Enoch DA, Goldenberg SD, Hussain A, Martin J, Spooner E, Whalley T. A practical approach to screening for carbapenemase-producing Enterobacterales- views of a group of multidisciplinary experts from English hospitals. BMC Infect Dis 2024; 24:444. [PMID: 38671365 PMCID: PMC11046869 DOI: 10.1186/s12879-024-09307-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
INTRODUCTION Carbapenemase-producing Enterobacterales (CPE) are an important public health threat, with costly operational and economic consequences for NHS Integrated Care Systems and NHS Trusts. UK Health Security Agency guidelines recommend that Trusts use locally developed risk assessments to accurately identify high-risk individuals for screening, and implement the most appropriate method of testing, but this presents many challenges. METHODS A convenience sample of cross-specialty experts from across England met to discuss the barriers and practical solutions to implementing UK Health Security Agency framework into operational and clinical workflows. The group derived responses to six key questions that are frequently asked about screening for CPE. KEY FINDINGS Four patient groups were identified for CPE screening: high-risk unplanned admissions, high-risk elective admissions, patients in high-risk units, and known positive contacts. Rapid molecular testing is a preferred screening method for some of these settings, offering faster turnaround times and more accurate results than culture-based testing. It is important to stimulate action now, as several lessons can be learnt from screening during the COVID-19 pandemic, as well as from CPE outbreaks. CONCLUSION Further decisive and instructive information is needed to establish CPE screening protocols based on local epidemiology and risk factors. Local management should continually evaluate local epidemiology, analysing data and undertaking frequent prevalence studies to understand risks, and prepare resources- such as upscaled screening- to prevent increasing prevalence, clusters or outbreaks. Rapid molecular-based methods will be a crucial part of these considerations, as they can reduce unnecessary isolation and opportunity costs.
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Affiliation(s)
- D R Jenkins
- University Hospitals of Leicester NHS Trust, Leicester, UK.
| | - C Auckland
- Royal Devon & Exeter NHS Foundation Trust, Exeter, UK
| | - C Chadwick
- Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - A R Dodgson
- Manchester University NHS FT, Manchester, UK
| | - D A Enoch
- Cambridge University NHS Foundation Trust, Cambridge, UK
| | - S D Goldenberg
- Centre for Clinical Infection and Diagnostics Research, Guy's and Saint Thomas' Hospitals NHS Trust, London, UK
| | - A Hussain
- University Hospitals Birmingham NHS Foundation Trust, West Midlands, UK
| | - J Martin
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - E Spooner
- Royal Wolverhampton NHS Trust, Wolverhampton, UK
| | - T Whalley
- Lancashire & South Cumbria ICB, Preston, UK
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Baudouin R, Hans S, Lisan Q, Morin B, Adimi Y, Martin J, Lechien JR, Tartour E, Badoual C. Prognostic Significance of the Microenvironment in Human Papillomavirus Oropharyngeal Carcinoma: A Systematic Review. Laryngoscope 2024; 134:1507-1516. [PMID: 37642393 DOI: 10.1002/lary.31010] [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: 05/30/2023] [Revised: 08/01/2023] [Accepted: 08/15/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVE The immune microenvironment of HPV-associated (HPV+) oropharyngeal squamous cell carcinomas (OPSCCs) (HPV+OPSCCs) differs from that of HPV-independent oropharyngeal cancers (HPV-independent OPSCCs). The literature on the subject is very abundant, demanding an organized synthesis of this wealth of information to evaluate the hypothesis associating the favorable prognosis of HPV+OPSCC patients with a different immune microenvironment. A systematic review of the literature was conducted regarding the microenvironment of HPV+OPSCCs. DATA SOURCE MEDLINE/PubMed, Embase, and Cochrane Library databases. REVIEW METHODS A literature search was performed following PRISMA guidelines (Moher D. PLoS Med. 2009). The PEO (Population, Exposure, and Outcome) framework is detailed as follows: P: patients with oropharyngeal squamous cell carcinomas, E: human papillomavirus (HPV), and O: histological and immunological composition of the tumoral microenvironment (TME). No meta-analysis was performed. RESULTS From 1,202 studies that were screened, 58 studies were included (n = 6,474 patients; n = 3,581 (55%) HPV+OPSCCs and n = 2,861(45%) HPV-independent OPSCCs). The presence of tumor-infiltrating lymphocytes (TIL), CD3+ in 1,733 patients, CD4+ in 520 patients, and CD8+ (cytotoxic T lymphocytes (CTL)) in 3,104 patients, and high levels of PD-L1 expression in 1,222 patients is strongly correlated with an improved clinical outcome in HPV+OPSCCs. CONCLUSION This systematic review provides the most comprehensive information on the immune microenvironment of HPV+OPSCCs to date. Tumor-infiltrating lymphocytes and PD-L1 expression are associated with a favorable prognosis. B, CD8+ and resident memory cells densities are higher in HPV+OPSCCs. The importance of myeloid lineages is still a matter of debate and research. LEVEL OF EVIDENCE NA Laryngoscope, 134:1507-1516, 2024.
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Affiliation(s)
- R Baudouin
- Department of Otolaryngology-Head & Neck Surgery, Foch Hospital, Suresnes, France
- School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Montigny-le-Bretonneux, France
| | - S Hans
- Department of Otolaryngology-Head & Neck Surgery, Foch Hospital, Suresnes, France
- School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Montigny-le-Bretonneux, France
| | - Q Lisan
- Department of Otolaryngology-Head & Neck Surgery, Foch Hospital, Suresnes, France
- School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Montigny-le-Bretonneux, France
| | - B Morin
- Department of Pathology, Hôpital Européen Georges Pompidou, Université Paris Cité, INSERM, PARCC, Paris, France
- Department of Biological Immunology, Hôpital Européen Georges Pompidou, Université Paris Cité, INSERM, PARCC, Paris, France
| | - Y Adimi
- Department of Pathology, Hôpital Européen Georges Pompidou, Université Paris Cité, INSERM, PARCC, Paris, France
- Department of Biological Immunology, Hôpital Européen Georges Pompidou, Université Paris Cité, INSERM, PARCC, Paris, France
| | - J Martin
- Department of Pathology, Hôpital Européen Georges Pompidou, Université Paris Cité, INSERM, PARCC, Paris, France
- Department of Biological Immunology, Hôpital Européen Georges Pompidou, Université Paris Cité, INSERM, PARCC, Paris, France
| | - J R Lechien
- Department of Otolaryngology-Head & Neck Surgery, Foch Hospital, Suresnes, France
- School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Montigny-le-Bretonneux, France
| | - E Tartour
- Department of Biological Immunology, Hôpital Européen Georges Pompidou, Université Paris Cité, INSERM, PARCC, Paris, France
| | - C Badoual
- Department of Pathology, Hôpital Européen Georges Pompidou, Université Paris Cité, INSERM, PARCC, Paris, France
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Ademuyiwa AO, Bhangu A, Chakrabortee S, Glasbey J, Kamarajah SK, Ledda V, Li E, Morton D, Nepogodiev D, Picciochi M, Simoes JFF, Lapitan MC, Cheetham M, Forkman E, El-Boghdadly E, Ghosh D, Harrison EM, Hutchinson P, Lawani I, Aguilera ML, Martin J, Meara JG, Ntirenganya F, Medina ARDL, Tabiri S. Strategies to strengthen elective surgery systems during the SARS-CoV-2 pandemic: systematic review and framework development. Br J Surg 2024; 111:znad405. [PMID: 38300731 PMCID: PMC10833142 DOI: 10.1093/bjs/znad405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/12/2023] [Accepted: 10/27/2023] [Indexed: 02/03/2024]
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Harz D, Catalán Gamonal B, Matute García S, Jeremias F, Martin J, Fresno MC. Prevalence and severity of molar-incisor hypomineralization, is there an association with socioeconomic status? A cross-sectional study in Chilean schoolchildren. Eur Arch Paediatr Dent 2023; 24:577-584. [PMID: 37432610 DOI: 10.1007/s40368-023-00820-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 06/24/2023] [Indexed: 07/12/2023]
Abstract
PURPOSE Data about molar-incisor hypomineralization (MIH) prevalence and its severity remains limited for some Latin American countries. Furthermore, its association with socioeconomic status (SES) is still unclear. Thus, this study aims to determine the prevalence and severity of MIH in Santiago, Chile and explore its association with SES. METHODS A cross-sectional study with schoolchildren between 6 and 12 years was conducted. Children were evaluated using the European Academy of Paediatric Dentistry to diagnose MIH, and the Mathu-Muju and Wright criteria to determine its severity. RESULTS A total of 1,270 children were included. The MIH prevalence was 12.8% without association with gender (p = 0.609). Prevalence was higher among schoolchildren ages 8 and 9 (p = 0.002), and in lower SES (p = 0.007). MIH mild cases were the most prevalent (63%), and severity was not related to gender (p = 0.656), age (p = 0.060), or SES (p = 0.174). CONCLUSIONS The prevalence of MIH in the province of Santiago, Chile is 12.8% and was found to have a higher incidence in 8-9-year-old students and among those categorized by low SES. Furthermore, MIH prevalence was associated with low SES. IMPLICATIONS Public health policies to address MIH in Chile should start with schoolchildren aged 8 to 9, and with low SES.
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Affiliation(s)
- D Harz
- Dental School, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | | | - F Jeremias
- Graduate Program in Dental Science Araraquara School of Dentistry, UNESP Univ Estadual Paulista São Paulo, Araraquara, São Paulo, Brazil
| | - J Martin
- Department of Restorative Dentistry, Faculty of Dentistry, University of Chile, Olivos 943, Independencia, Santiago, Chile
| | - M C Fresno
- Faculty of Dentistry, University of Chile, Santiago, Chile.
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Cullington HE, Jiang D, Broomfield SJ, Chung M, Craddock LC, Driver S, Edwards D, Gallacher JM, Jones LL, Koleva T, Martin J, Meakin H, Nash R, Rocca C, Schramm DR, Willmott NS, Vanat ZH. Cochlear implant services for children, young people and adults. Quality standard. Cochlear Implants Int 2023:1-13. [PMID: 37114384 DOI: 10.1080/14670100.2023.2197344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Affiliation(s)
- H E Cullington
- University of Southampton Auditory Implant Service, SO17 1BJ, UK
| | - D Jiang
- Hearing Implant Centre, Guy's and St. Thomas NHS Foundation Trust, London, UK
- Centre for Craniofacial and Regenerative Biology, King's College London, London, UK
| | - S J Broomfield
- West of England Hearing Implant Programme, University Hospitals Bristol and Weston NHS Foundation Trust, UK
| | - M Chung
- Auditory Implant Department, Royal National ENT & Eastman Dental Hospitals, University College London Hospitals NHS Foundation Trust, UK
| | - L C Craddock
- Midlands Hearing Implant Programme (Adult service), University Hospitals Birmingham NHS Foundation Trust, UK
| | - S Driver
- Hearing Implant Centre, Guy's and St. Thomas NHS Foundation Trust, London, UK
| | - D Edwards
- Emmeline Centre for Hearing Implants, Cambridge University Hospitals NHS Trust, UK
| | - J M Gallacher
- Scottish Cochlear Implant Program, Crosshouse Hospital, Kilmarnock, UK
| | - L Ll Jones
- North Wales Auditory Implant Service, Betsi Cadwaladr University Health Board, Bodelwyddan, UK
| | - T Koleva
- Emmeline Centre for Hearing Implants, Cambridge University Hospitals NHS Trust, UK
| | - J Martin
- Cochlear Implant Programme, Great Ormond Street Hospital For Children NHS Foundation Trust, London, UK
| | - H Meakin
- Emmeline Centre for Hearing Implants, Cambridge University Hospitals NHS Trust, UK
| | - R Nash
- Cochlear Implant Programme, Great Ormond Street Hospital For Children NHS Foundation Trust, London, UK
| | - C Rocca
- Hearing Implant Centre, Guy's and St. Thomas NHS Foundation Trust, London, UK
| | - D R Schramm
- University of Ottawa Auditory Implant Centre, Ottawa, Canada
| | - N S Willmott
- Auditory Implant Centre, Belfast Health and Social Care Trust, UK
| | - Z H Vanat
- Emmeline Centre for Hearing Implants, Cambridge University Hospitals NHS Trust, UK
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Hulo S, Jacques J, Sihrener F, Wasielewski E, Jourdan L, Poslednik G, Poulet C, Turlotte A, Gey T, Douadi Y, Thiberville L, Dewolf M, Lecerf JM, Estevié I, Ricard V, Martin J, Romain AC, Locoge N, Matran R, Scherpereel A. 160P Non-invasive analysis of VOCs in exhaled air can distinguish healthy controls from lung cancer patients and may improve the effectiveness of lung cancer screening. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00414-8] [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: 04/03/2023]
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Barral E, Martin J. Abstract No. 70 Safety and Clinical Outcomes of Aspiration Thrombectomy in Patients with Massive PE: A Single-Center Retrospective Review. J Vasc Interv Radiol 2023. [DOI: 10.1016/j.jvir.2022.12.115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023] Open
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Ajrawat H, Buchholz J, Triana B, Pabon-Ramos W, Martin J, Kim C, Cline B, Ronald J. Abstract No. 583 Financial Analysis of Intravascular Ultrasound-Guided Transvenous Biopsy in an Outpatient Medicare Population. J Vasc Interv Radiol 2023. [DOI: 10.1016/j.jvir.2022.12.441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023] Open
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Gallo C, Cline B, Befera N, Ronald J, Martin J, Sag A, Pabon-Ramos W, Suhocki P, Smith T, Kim C. Abstract No. 97 Safety and Patency of Dedicated Venous Stents for Treatment of Thoracic Central Vein Stenosis Compared with Non-Venous Stents. J Vasc Interv Radiol 2023. [DOI: 10.1016/j.jvir.2022.12.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023] Open
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12
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Barral E, Martin J. Abstract No. 501 Safety and Clinical Outcomes of Aspiration Thrombectomy in Patients with Intermediate-High Risk PE: A Single-Center Retrospective Review. J Vasc Interv Radiol 2023. [DOI: 10.1016/j.jvir.2022.12.359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023] Open
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Buchholz J, Ajrawat H, Cline B, Martin J, Kim C, Ronald J. Abstract No. 223 Intravascular Ultrasound-Guided Transvenous Biopsy of Retroperitoneal Lymph Nodes: Efficacy and Safety Compared with Percutaneous CT-Guided Biopsy. J Vasc Interv Radiol 2023. [DOI: 10.1016/j.jvir.2022.12.284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023] Open
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Kasivisvanathan V, Murphy D, Link E, Lawrentschuk N, O’Brien J, Buteau J, Roberts M, Francis R, Tang C, Vela I, Thomas P, Rutherford N, Martin J, Frydenberg M, Shakher R, Wong LM, Taubman K, Lee S, Hsiao E, Nottage M, Kirkwood I, Iravani A, Williams S, Hofman M. Baseline PSMA PET-CT is prognostic for treatment failure in men with intermediate-to-high risk prostate cancer: 54 months follow-up of the proPSMA randomised trial. Eur Urol 2023. [DOI: 10.1016/s0302-2838(23)01275-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Rice W, Martin J, Hodgkin M, Carter J, Barrasa A, Sweeting K, Johnson R, Best E, Nahl J, Denton M, Hughes GJ. A protracted outbreak of difficult-to-treat resistant Pseudomonas aeruginosa in a haematology unit: a matched case-control study demonstrating increased risk with use of fluoroquinolone. J Hosp Infect 2023; 132:52-61. [PMID: 36563938 DOI: 10.1016/j.jhin.2022.11.013] [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: 09/09/2022] [Revised: 11/11/2022] [Accepted: 11/20/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Between September 2016 and November 2020, 17 cases of difficult-to-treat resistant Pseudomonas aeruginosa (DTR-PA) were reported in haematology patients at a tertiary referral hospital in the North of England. AIM A retrospective case-control study was conducted to investigate the association between DTR-PA infection and clinical interventions, patient movement, antimicrobial use and comorbidities. METHODS Cases were patients colonized or infected with the outbreak strain of DTR-PA who had been admitted to hospital prior to their positive specimen. Exposures were extracted from medical records, and cases were compared with controls using conditional logistic regression. Environmental and microbiological investigations were also conducted. FINDINGS Seventeen cases and 51 controls were included. The final model included age [>65 years, adjusted OR (aOR) 6.85, P=0.232], sex (aOR 0.60, P=0.688), admission under the transplant team (aOR 14.27, P=0.43) and use of ciprofloxacin (aOR 102.13, P=0.030). Investigations did not indicate case-to-case transmission or a point source, although a common environmental source was highly likely. CONCLUSION This study found that the use of fluoroquinolones is an independent risk factor for DTR-PA in haematology patients. Antimicrobial stewardship and review of fluoroquinolone prophylaxis should be considered as part of PA outbreak investigations in addition to standard infection control interventions.
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Affiliation(s)
- W Rice
- Field Epidemiology Training Programme, United Kingdom Heath Security Agency, London, UK; Field Service, United Kingdom Health Security Agency, Leeds, UK
| | - J Martin
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - M Hodgkin
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - J Carter
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - A Barrasa
- Field Epidemiology Training Programme, United Kingdom Heath Security Agency, London, UK
| | - K Sweeting
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - R Johnson
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - E Best
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - J Nahl
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - M Denton
- Field Service, United Kingdom Health Security Agency, Leeds, UK; Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - G J Hughes
- Field Service, United Kingdom Health Security Agency, Leeds, UK.
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nat Commun 2023; 14:436. [PMID: 36702828 PMCID: PMC9879935 DOI: 10.1038/s41467-023-36188-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Badoual C, Adimi Y, Martin J, Morin B, Baudouin R. Les cancers des voies aérodigestives supérieures induits par une infection par Papillomavirus humain : spécificités épidémiologiques, diagnostiques, pronostiques et thérapeutiques. Bulletin de l'Académie Nationale de Médecine 2023. [DOI: 10.1016/j.banm.2022.10.019] [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: 01/26/2023]
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18
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Ward DD, Martin J, Gordon EH. Is There a Sex-Frailty Paradox in Dementia? J Nutr Health Aging 2023; 27:1281-1283. [PMID: 38151880 DOI: 10.1007/s12603-023-2040-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 12/29/2023]
Affiliation(s)
- D D Ward
- David D. Ward, Centre for Health Services Research, The University of Queensland, Level 2, Building 33, Princess Alexandra Hospital, 199 Ipswich Road, Woolloongabba, QLD 4121, Australia.
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19
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Podtschaske AH, Martin J, Ulm B, Jungwirth B, Kagerbauer SM. Sex-specific issues of central and peripheral arginine-vasopressin concentrations in neurocritical care patients. BMC Neurosci 2022; 23:69. [PMID: 36434506 PMCID: PMC9700878 DOI: 10.1186/s12868-022-00757-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/17/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Arginine-Vasopressin (AVP) is a nonapeptide that exerts multiple functions within the central nervous system and in the blood circulation that might contribute to outcome in critically ill patients. Sex differences have been found for mental and physical effects of AVP. For example, stress response and response due to hemorrhage differ between males and females, at least in animal studies. Data on humans -especially on AVP within the central nervous system (CNS)-are scarce, as cerebrospinal fluid (CSF) which is said to represent central AVP activity, has to be collected by means of invasive procedures. Here we present data on 30 neurocritical care patients where we simultaneously collected blood, CSF and saliva to analyze concentrations in the central and peripheral compartments. PATIENTS AND METHODS 30 neurocritical care patients were included (13 male, 13 postmenopausal female, 4 premenopausal female) with a median age of 60 years. CSF, plasma and saliva were obtained simultaneously once in each patient and analyzed for AVP concentrations. Correlations between the central compartment represented by CSF, and the peripheral compartment represented by plasma and saliva, were identified. Relations between AVP concentrations and serum sodium and hematocrit were also determined. RESULTS In the whole patient collective, only very weak to weak correlations could be detected between AVP plasma/CSF, plasma/saliva and CSF/saliva as well as between AVP concentrations in each of the compartments and serum sodium/hematocrit. Regarding the subgroup of postmenopausal females, a significant moderate correlation could be detected for AVP in plasma and CSF and AVP CSF and serum sodium. CONCLUSION Absolute concentrations of AVP in central and peripheral compartments did not show sex differences. However, correlations between AVP plasma and CSF and AVP CSF and serum sodium in postmenopausal females indicate differences in AVP secretion and AVP response to triggers that deserve further examination.
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Affiliation(s)
- A. H. Podtschaske
- grid.6936.a0000000123222966Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - J. Martin
- grid.6936.a0000000123222966Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - B. Ulm
- grid.6936.a0000000123222966Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany ,grid.6582.90000 0004 1936 9748Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - B. Jungwirth
- grid.6582.90000 0004 1936 9748Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - S. M. Kagerbauer
- grid.6936.a0000000123222966Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany ,grid.6582.90000 0004 1936 9748Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
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Martin J, Elster C. Aleatoric Uncertainty for Errors-in-Variables Models in Deep Regression. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11066-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
AbstractA Bayesian treatment of deep learning allows for the computation of uncertainties associated with the predictions of deep neural networks. We show how the concept of Errors-in-Variables can be used in Bayesian deep regression to also account for the uncertainty associated with the input of the employed neural network. The presented approach thereby exploits a relevant, but generally overlooked, source of uncertainty and yields a decomposition of the predictive uncertainty into an aleatoric and epistemic part that is more complete and, in many cases, more consistent from a statistical perspective. We discuss the approach along various simulated and real examples and observe that using an Errors-in-Variables model leads to an increase in the uncertainty while preserving the prediction performance of models without Errors-in-Variables. For examples with known regression function we observe that this ground truth is substantially better covered by the Errors-in-Variables model, indicating that the presented approach leads to a more reliable uncertainty estimation.
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22
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Sandler H, Shore N, Dearnaley D, Freedland S, Smith M, Rosales R, Brookman-May S, Dicker A, McKenzie M, Bossi A, Widmark A, Wiegel T, Martin J, Miladinovic B, Lefresne F, Ciprotti M, McCarthy S, Mundle S, Tombal B, Feng F. Challenges and Solutions during the COVID Pandemic for Patient Retention and Physician Engagement in the Phase 3 ATLAS Study of Apalutamide Added to Androgen Deprivation Therapy (ADT) in High-Risk Localized or Locally Advanced Prostate Cancer (HRLPC). Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Martin J, Perraton L, Gupta A, Garofolini A, Malliaras P. The use of physical function capacity measures in the management of lower limb tendinopathy: A scoping review of expert recommendations. J Sci Med Sport 2022. [DOI: 10.1016/j.jsams.2022.09.150] [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: 11/17/2022]
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Gonzalez A, Anchor T, Hevia A, Posadas A, Wade J, Ansag R, Benko K, Bottoni B, Kazakova V, Alvarez M, Wong J, Martin J, Knauf R, Jantke K, Wu A. The evolution of the fAIble system to automatically compose and narrate stories for children. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2104382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Affiliation(s)
- A.J. Gonzalez
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - T. Anchor
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - A. Hevia
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - A. Posadas
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - J. Wade
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - R.A. Ansag
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - K. Benko
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - B. Bottoni
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - V. Kazakova
- Computer Science Dept, Knox College, Galesburg, IL, USA
| | - M.J. Alvarez
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - J.M Wong
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - J. Martin
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
| | - R. Knauf
- Computer Science Faculty, Technical University Ilmenau, Ilmenau, Germany
| | | | - A.S. Wu
- Intelligent Systems Laboratory, Computer Science Department, University of Central Florida, Orlando, FL, USA
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Foley P, Sheller MJ, Edwards B, Pati S, Riviera W, Sharma M, Narayana Moorthy P, Wang SH, Martin J, Mirhaji P, Shah P, Bakas S. OpenFL: the open federated learning library. Phys Med Biol 2022; 67:214001. [PMID: 36198326 PMCID: PMC9715347 DOI: 10.1088/1361-6560/ac97d9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/05/2022] [Indexed: 11/07/2022]
Abstract
Objective.Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) and deep learning (DL) projects without sharing sensitive data, such as patient records, financial data, or classified secrets.Approach.Open federated learning (OpenFL) framework is an open-source python-based tool for training ML/DL algorithms using the data-private collaborative learning paradigm of FL, irrespective of the use case. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and DL frameworks.Main results.In this manuscript, we present OpenFL and summarize its motivation and development characteristics, with the intention of facilitating its application to existing ML/DL model training in a production environment. We further provide recommendations to secure a federation using trusted execution environments to ensure explicit model security and integrity, as well as maintain data confidentiality. Finally, we describe the first real-world healthcare federations that use the OpenFL library, and highlight how it can be applied to other non-healthcare use cases.Significance.The OpenFL library is designed for real world scalability, trusted execution, and also prioritizes easy migration of centralized ML models into a federated training pipeline. Although OpenFL's initial use case was in healthcare, it is applicable beyond this domain and is now reaching wider adoption both in research and production settings. The tool is open-sourced atgithub.com/intel/openfl.
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Affiliation(s)
- Patrick Foley
- Intel Corporation, Santa Clara, CA 95052, United States of America
| | - Micah J Sheller
- Intel Corporation, Santa Clara, CA 95052, United States of America
| | - Brandon Edwards
- Intel Corporation, Santa Clara, CA 95052, United States of America
| | - Sarthak Pati
- University of Pennsylvania, 3700 Hamilton Walk, Richards Medical Research Laboratories (7th Fl), Philadelphia, PA 19104, United States of America
| | - Walter Riviera
- Intel Corporation, Santa Clara, CA 95052, United States of America
| | - Mansi Sharma
- Intel Corporation, Santa Clara, CA 95052, United States of America
| | | | - Shih-Han Wang
- Intel Corporation, Santa Clara, CA 95052, United States of America
| | - Jason Martin
- Intel Corporation, Santa Clara, CA 95052, United States of America
| | - Parsa Mirhaji
- Albert Einstein College of Medicine, 1300 Morris Park Ave, Bronx, NY 10461, United States of America
| | - Prashant Shah
- Intel Corporation, Santa Clara, CA 95052, United States of America
| | - Spyridon Bakas
- University of Pennsylvania, 3700 Hamilton Walk, Richards Medical Research Laboratories (7th Fl), Philadelphia, PA 19104, United States of America
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Pati S, Baid U, Edwards B, Sheller MJ, Foley P, Reina GA, Thakur S, Sako C, Bilello M, Davatzikos C, Martin J, Shah P, Menze B, Bakas S. The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research. Phys Med Biol 2022; 67:10.1088/1361-6560/ac9449. [PMID: 36137534 PMCID: PMC9592188 DOI: 10.1088/1361-6560/ac9449] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/22/2022] [Indexed: 11/11/2022]
Abstract
Objective.De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria.Approach.Towards this end, this manuscript describes theFederatedTumorSegmentation (FeTS) tool, in terms of software architecture and functionality.Main results.The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data.Significance.Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced athttps://github.com/FETS-AI/Front-End.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | - Siddhesh Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Otten LS, Heine RT, Chiong J, Martin J, van den Heuvel M, Piet B, Burger D. EP08.02-090 Sotorasib Drug-Drug Interactions: Essential Guidance Requested by Physicians Worldwide. J Thorac Oncol 2022. [DOI: 10.1016/j.jtho.2022.07.773] [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: 10/14/2022]
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Giraud E, Chiong J, Martin J, Burger D, Erp N, Smolders E. 1595P QTc-prolonging drug-drug interactions related to CDK4/6 inhibitors. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.1688] [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: 11/28/2022] Open
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Smeds M, Martin J, Elg M, Gremyr I. Why won’t you leave the process alone? Exploring emotional, motivational and cognitive mechanisms triggering tampering. Total Quality Management & Business Excellence 2022. [DOI: 10.1080/14783363.2022.2112514] [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: 10/15/2022]
Affiliation(s)
- Magdalena Smeds
- Department of Management and Engineering and HELIX Competence Centre, Linköping University, Linköping, Sweden
| | - Jason Martin
- Department of Management and Engineering and HELIX Competence Centre, Linköping University, Linköping, Sweden
| | - Mattias Elg
- Department of Management and Engineering and HELIX Competence Centre, Linköping University, Linköping, Sweden
| | - Ida Gremyr
- Department of Technology Management and Economics, Chalmers University of Technology, Gothenburg, Sweden
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Evans RA, Leavy OC, Richardson M, Elneima O, McAuley HJC, Shikotra A, Singapuri A, Sereno M, Saunders RM, Harris VC, Houchen-Wolloff L, Aul R, Beirne P, Bolton CE, Brown JS, Choudhury G, Diar-Bakerly N, Easom N, Echevarria C, Fuld J, Hart N, Hurst J, Jones MG, Parekh D, Pfeffer P, Rahman NM, Rowland-Jones SL, Shah AM, Wootton DG, Chalder T, Davies MJ, De Soyza A, Geddes JR, Greenhalf W, Greening NJ, Heaney LG, Heller S, Howard LS, Jacob J, Jenkins RG, Lord JM, Man WDC, McCann GP, Neubauer S, Openshaw PJM, Porter JC, Rowland MJ, Scott JT, Semple MG, Singh SJ, Thomas DC, Toshner M, Lewis KE, Thwaites RS, Briggs A, Docherty AB, Kerr S, Lone NI, Quint J, Sheikh A, Thorpe M, Zheng B, Chalmers JD, Ho LP, Horsley A, Marks M, Poinasamy K, Raman B, Harrison EM, Wain LV, Brightling CE, Abel K, Adamali H, Adeloye D, Adeyemi O, Adrego R, Aguilar Jimenez LA, Ahmad S, Ahmad Haider N, Ahmed R, Ahwireng N, Ainsworth M, Al-Sheklly B, Alamoudi A, Ali M, Aljaroof M, All AM, Allan L, Allen RJ, Allerton L, Allsop L, Almeida P, Altmann D, Alvarez Corral M, Amoils S, Anderson D, Antoniades C, Arbane G, Arias A, Armour C, Armstrong L, Armstrong N, Arnold D, Arnold H, Ashish A, Ashworth A, Ashworth M, Aslani S, Assefa-Kebede H, Atkin C, Atkin P, Aung H, Austin L, Avram C, Ayoub A, Babores M, Baggott R, Bagshaw J, Baguley D, Bailey L, Baillie JK, Bain S, Bakali M, Bakau M, Baldry E, Baldwin D, Ballard C, Banerjee A, Bang B, Barker RE, Barman L, Barratt S, Barrett F, Basire D, Basu N, Bates M, Bates A, Batterham R, Baxendale H, Bayes H, Beadsworth M, Beckett P, Beggs M, Begum M, Bell D, Bell R, Bennett K, Beranova E, Bermperi A, Berridge A, Berry C, Betts S, Bevan E, Bhui K, Bingham M, Birchall K, Bishop L, Bisnauthsing K, Blaikely J, Bloss A, Bolger A, Bonnington J, Botkai A, Bourne C, Bourne M, Bramham K, Brear L, Breen G, Breeze J, Bright E, Brill S, Brindle K, Broad L, Broadley A, Brookes C, Broome M, Brown A, Brown A, Brown J, Brown J, Brown M, Brown M, Brown V, Brugha T, Brunskill N, Buch M, Buckley P, Bularga A, Bullmore E, Burden L, Burdett T, Burn D, Burns G, Burns A, Busby J, Butcher R, Butt A, Byrne S, Cairns P, Calder PC, Calvelo E, Carborn H, Card B, Carr C, Carr L, Carson G, Carter P, Casey A, Cassar M, Cavanagh J, Chablani M, Chambers RC, Chan F, Channon KM, Chapman K, Charalambou A, Chaudhuri N, Checkley A, Chen J, Cheng Y, Chetham L, Childs C, Chilvers ER, Chinoy H, Chiribiri A, Chong-James K, Choudhury N, Chowienczyk P, Christie C, Chrystal M, Clark D, Clark C, Clarke J, Clohisey S, Coakley G, Coburn Z, Coetzee S, Cole J, Coleman C, Conneh F, Connell D, Connolly B, Connor L, Cook A, Cooper B, Cooper J, Cooper S, Copeland D, Cosier T, Coulding M, Coupland C, Cox E, Craig T, Crisp P, Cristiano D, Crooks MG, Cross A, Cruz I, Cullinan P, Cuthbertson D, Daines L, Dalton M, Daly P, Daniels A, Dark P, Dasgin J, David A, David C, Davies E, Davies F, Davies G, Davies GA, Davies K, Dawson J, Daynes E, Deakin B, Deans A, Deas C, Deery J, Defres S, Dell A, Dempsey K, Denneny E, Dennis J, Dewar A, Dharmagunawardena R, Dickens C, Dipper A, Diver S, Diwanji SN, Dixon M, Djukanovic R, Dobson H, Dobson SL, Donaldson A, Dong T, Dormand N, Dougherty A, Dowling R, Drain S, Draxlbauer K, Drury K, Dulawan P, Dunleavy A, Dunn S, Earley J, Edwards S, Edwardson C, El-Taweel H, Elliott A, Elliott K, Ellis Y, Elmer A, Evans D, Evans H, Evans J, Evans R, Evans RI, Evans T, Evenden C, Evison L, Fabbri L, Fairbairn S, Fairman A, Fallon K, Faluyi D, Favager C, Fayzan T, Featherstone J, Felton T, Finch J, Finney S, Finnigan J, Finnigan L, Fisher H, Fletcher S, Flockton R, Flynn M, Foot H, Foote D, Ford A, Forton D, Fraile E, Francis C, Francis R, Francis S, Frankel A, Fraser E, Free R, French N, Fu X, Furniss J, Garner L, Gautam N, George J, George P, Gibbons M, Gill M, Gilmour L, Gleeson F, Glossop J, Glover S, Goodman N, Goodwin C, Gooptu B, Gordon H, Gorsuch T, Greatorex M, Greenhaff PL, Greenhalgh A, Greenwood J, Gregory H, Gregory R, Grieve D, Griffin D, Griffiths L, Guerdette AM, Guillen Guio B, Gummadi M, Gupta A, Gurram S, Guthrie E, Guy Z, H Henson H, Hadley K, Haggar A, Hainey K, Hairsine B, Haldar P, Hall I, Hall L, Halling-Brown M, Hamil R, Hancock A, Hancock K, Hanley NA, Haq S, Hardwick HE, Hardy E, Hardy T, Hargadon B, Harrington K, Harris E, Harrison P, Harvey A, Harvey M, Harvie M, Haslam L, Havinden-Williams M, Hawkes J, Hawkings N, Haworth J, Hayday A, Haynes M, Hazeldine J, Hazelton T, Heeley C, Heeney JL, Heightman M, Henderson M, Hesselden L, Hewitt M, Highett V, Hillman T, Hiwot T, Hoare A, Hoare M, Hockridge J, Hogarth P, Holbourn A, Holden S, Holdsworth L, Holgate D, Holland M, Holloway L, Holmes K, Holmes M, Holroyd-Hind B, Holt L, Hormis A, Hosseini A, Hotopf M, Howard K, Howell A, Hufton E, Hughes AD, Hughes J, Hughes R, Humphries A, Huneke N, Hurditch E, Husain M, Hussell T, Hutchinson J, Ibrahim W, Ilyas F, Ingham J, Ingram L, Ionita D, Isaacs K, Ismail K, Jackson T, James WY, Jarman C, Jarrold I, Jarvis H, Jastrub R, Jayaraman B, Jezzard P, Jiwa K, Johnson C, Johnson S, Johnston D, Jolley CJ, Jones D, Jones G, Jones H, Jones H, Jones I, Jones L, Jones S, Jose S, Kabir T, Kaltsakas G, Kamwa V, Kanellakis N, Kaprowska S, Kausar Z, Keenan N, Kelly S, Kemp G, Kerslake H, Key AL, Khan F, Khunti K, Kilroy S, King B, King C, Kingham L, Kirk J, Kitterick P, Klenerman P, Knibbs L, Knight S, Knighton A, Kon O, Kon S, Kon SS, Koprowska S, Korszun A, Koychev I, Kurasz C, Kurupati P, Laing C, Lamlum H, Landers G, Langenberg C, Lasserson D, Lavelle-Langham L, Lawrie A, Lawson C, Lawson C, Layton A, Lea A, Lee D, Lee JH, Lee E, Leitch K, Lenagh R, Lewis D, Lewis J, Lewis V, Lewis-Burke N, Li X, Light T, Lightstone L, Lilaonitkul W, Lim L, Linford S, Lingford-Hughes A, Lipman M, Liyanage K, Lloyd A, Logan S, Lomas D, Loosley R, Lota H, Lovegrove W, Lucey A, Lukaschuk E, Lye A, Lynch C, MacDonald S, MacGowan G, Macharia I, Mackie J, Macliver L, Madathil S, Madzamba G, Magee N, Magtoto MM, Mairs N, Majeed N, Major E, Malein F, Malim M, Mallison G, Mandal S, Mangion K, Manisty C, Manley R, March K, Marciniak S, Marino P, Mariveles M, Marouzet E, Marsh S, Marshall B, Marshall M, Martin J, Martineau A, Martinez LM, Maskell N, Matila D, Matimba-Mupaya W, Matthews L, Mbuyisa A, McAdoo S, Weir McCall J, McAllister-Williams H, McArdle A, McArdle P, McAulay D, McCormick J, McCormick W, McCourt P, McGarvey L, McGee C, Mcgee K, McGinness J, McGlynn K, McGovern A, McGuinness H, McInnes IB, McIntosh J, McIvor E, McIvor K, McLeavey L, McMahon A, McMahon MJ, McMorrow L, Mcnally T, McNarry M, McNeill J, McQueen A, McShane H, Mears C, Megson C, Megson S, Mehta P, Meiring J, Melling L, Mencias M, Menzies D, Merida Morillas M, Michael A, Milligan L, Miller C, Mills C, Mills NL, Milner L, Misra S, Mitchell J, Mohamed A, Mohamed N, Mohammed S, Molyneaux PL, Monteiro W, Moriera S, Morley A, Morrison L, Morriss R, Morrow A, Moss AJ, Moss P, Motohashi K, Msimanga N, Mukaetova-Ladinska E, Munawar U, Murira J, Nanda U, Nassa H, Nasseri M, Neal A, Needham R, Neill P, Newell H, Newman T, Newton-Cox A, Nicholson T, Nicoll D, Nolan CM, Noonan MJ, Norman C, Novotny P, Nunag J, Nwafor L, Nwanguma U, Nyaboko J, O'Donnell K, O'Brien C, O'Brien L, O'Regan D, Odell N, Ogg G, Olaosebikan O, Oliver C, Omar Z, Orriss-Dib L, Osborne L, Osbourne R, Ostermann M, Overton C, Owen J, Oxton J, Pack J, Pacpaco E, Paddick S, Painter S, Pakzad A, Palmer S, Papineni P, Paques K, Paradowski K, Pareek M, Parfrey H, Pariante C, Parker S, Parkes M, Parmar J, Patale S, Patel B, Patel M, Patel S, Pattenadk D, Pavlides M, Payne S, Pearce L, Pearl JE, Peckham D, Pendlebury J, Peng Y, Pennington C, Peralta I, Perkins E, Peterkin Z, Peto T, Petousi N, Petrie J, Phipps J, Pimm J, Piper Hanley K, Pius R, Plant H, Plein S, Plekhanova T, Plowright M, Polgar O, Poll L, Porter J, Portukhay S, Powell N, Prabhu A, Pratt J, Price A, Price C, Price C, Price D, Price L, Price L, Prickett A, Propescu J, Pugmire S, Quaid S, Quigley J, Qureshi H, Qureshi IN, Radhakrishnan K, Ralser M, Ramos A, Ramos H, Rangeley J, Rangelov B, Ratcliffe L, Ravencroft P, Reddington A, Reddy R, Redfearn H, Redwood D, Reed A, Rees M, Rees T, Regan K, Reynolds W, Ribeiro C, Richards A, Richardson E, Rivera-Ortega P, Roberts K, Robertson E, Robinson E, Robinson L, Roche L, Roddis C, Rodger J, Ross A, Ross G, Rossdale J, Rostron A, Rowe A, Rowland A, Rowland J, Roy K, Roy M, Rudan I, Russell R, Russell E, Saalmink G, Sabit R, Sage EK, Samakomva T, Samani N, Sampson C, Samuel K, Samuel R, Sanderson A, Sapey E, Saralaya D, Sargant J, Sarginson C, Sass T, Sattar N, Saunders K, Saunders P, Saunders LC, Savill H, Saxon W, Sayer A, Schronce J, Schwaeble W, Scott K, Selby N, Sewell TA, Shah K, Shah P, Shankar-Hari M, Sharma M, Sharpe C, Sharpe M, Shashaa S, Shaw A, Shaw K, Shaw V, Shelton S, Shenton L, Shevket K, Short J, Siddique S, Siddiqui S, Sidebottom J, Sigfrid L, Simons G, Simpson J, Simpson N, Singh C, Singh S, Sissons D, Skeemer J, Slack K, Smith A, Smith D, Smith S, Smith J, Smith L, Soares M, Solano TS, Solly R, Solstice AR, Soulsby T, Southern D, Sowter D, Spears M, Spencer LG, Speranza F, Stadon L, Stanel S, Steele N, Steiner M, Stensel D, Stephens G, Stephenson L, Stern M, Stewart I, Stimpson R, Stockdale S, Stockley J, Stoker W, Stone R, Storrar W, Storrie A, Storton K, Stringer E, Strong-Sheldrake S, Stroud N, Subbe C, Sudlow CL, Suleiman Z, Summers C, Summersgill C, Sutherland D, Sykes DL, Sykes R, Talbot N, Tan AL, Tarusan L, Tavoukjian V, Taylor A, Taylor C, Taylor J, Te A, Tedd H, Tee CJ, Teixeira J, Tench H, Terry S, Thackray-Nocera S, Thaivalappil F, Thamu B, Thickett D, Thomas C, Thomas S, Thomas AK, Thomas-Woods T, Thompson T, Thompson AAR, Thornton T, Tilley J, Tinker N, Tiongson GF, Tobin M, Tomlinson J, Tong C, Touyz R, Tripp KA, Tunnicliffe E, Turnbull A, Turner E, Turner S, Turner V, Turner K, Turney S, Turtle L, Turton H, Ugoji J, Ugwuoke R, Upthegrove R, Valabhji J, Ventura M, Vere J, Vickers C, Vinson B, Wade E, Wade P, Wainwright T, Wajero LO, Walder S, Walker S, Walker S, Wall E, Wallis T, Walmsley S, Walsh JA, Walsh S, Warburton L, Ward TJC, Warwick K, Wassall H, Waterson S, Watson E, Watson L, Watson J, Welch C, Welch H, Welsh B, Wessely S, West S, Weston H, Wheeler H, White S, Whitehead V, Whitney J, Whittaker S, Whittam B, Whitworth V, Wight A, Wild J, Wilkins M, Wilkinson D, Williams N, Williams N, Williams J, Williams-Howard SA, Willicombe M, Willis G, Willoughby J, Wilson A, Wilson D, Wilson I, Window N, Witham M, Wolf-Roberts R, Wood C, Woodhead F, Woods J, Wormleighton J, Worsley J, Wraith D, Wrey Brown C, Wright C, Wright L, Wright S, Wyles J, Wynter I, Xu M, Yasmin N, Yasmin S, Yates T, Yip KP, Young B, Young S, Young A, Yousuf AJ, Zawia A, Zeidan L, Zhao B, Zongo O. Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study. Lancet Respir Med 2022; 10:761-775. [PMID: 35472304 PMCID: PMC9034855 DOI: 10.1016/s2213-2600(22)00127-8] [Citation(s) in RCA: 144] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/23/2022] [Accepted: 03/31/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND No effective pharmacological or non-pharmacological interventions exist for patients with long COVID. We aimed to describe recovery 1 year after hospital discharge for COVID-19, identify factors associated with patient-perceived recovery, and identify potential therapeutic targets by describing the underlying inflammatory profiles of the previously described recovery clusters at 5 months after hospital discharge. METHODS The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study recruiting adults (aged ≥18 years) discharged from hospital with COVID-19 across the UK. Recovery was assessed using patient-reported outcome measures, physical performance, and organ function at 5 months and 1 year after hospital discharge, and stratified by both patient-perceived recovery and recovery cluster. Hierarchical logistic regression modelling was performed for patient-perceived recovery at 1 year. Cluster analysis was done using the clustering large applications k-medoids approach using clinical outcomes at 5 months. Inflammatory protein profiling was analysed from plasma at the 5-month visit. This study is registered on the ISRCTN Registry, ISRCTN10980107, and recruitment is ongoing. FINDINGS 2320 participants discharged from hospital between March 7, 2020, and April 18, 2021, were assessed at 5 months after discharge and 807 (32·7%) participants completed both the 5-month and 1-year visits. 279 (35·6%) of these 807 patients were women and 505 (64·4%) were men, with a mean age of 58·7 (SD 12·5) years, and 224 (27·8%) had received invasive mechanical ventilation (WHO class 7-9). The proportion of patients reporting full recovery was unchanged between 5 months (501 [25·5%] of 1965) and 1 year (232 [28·9%] of 804). Factors associated with being less likely to report full recovery at 1 year were female sex (odds ratio 0·68 [95% CI 0·46-0·99]), obesity (0·50 [0·34-0·74]) and invasive mechanical ventilation (0·42 [0·23-0·76]). Cluster analysis (n=1636) corroborated the previously reported four clusters: very severe, severe, moderate with cognitive impairment, and mild, relating to the severity of physical health, mental health, and cognitive impairment at 5 months. We found increased inflammatory mediators of tissue damage and repair in both the very severe and the moderate with cognitive impairment clusters compared with the mild cluster, including IL-6 concentration, which was increased in both comparisons (n=626 participants). We found a substantial deficit in median EQ-5D-5L utility index from before COVID-19 (retrospective assessment; 0·88 [IQR 0·74-1·00]), at 5 months (0·74 [0·64-0·88]) to 1 year (0·75 [0·62-0·88]), with minimal improvements across all outcome measures at 1 year after discharge in the whole cohort and within each of the four clusters. INTERPRETATION The sequelae of a hospital admission with COVID-19 were substantial 1 year after discharge across a range of health domains, with the minority in our cohort feeling fully recovered. Patient-perceived health-related quality of life was reduced at 1 year compared with before hospital admission. Systematic inflammation and obesity are potential treatable traits that warrant further investigation in clinical trials. FUNDING UK Research and Innovation and National Institute for Health Research.
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Amanova N, Martin J, Elster C. Explainability for deep learning in mammography image quality assessment. Mach Learn : Sci Technol 2022. [DOI: 10.1088/2632-2153/ac7a03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
The application of deep learning has recently been proposed for the assessment of image quality in mammography. It was demonstrated in a proof-of-principle study that the proposed approach can be more efficient than currently applied automated conventional methods. However, in contrast to conventional methods, the deep learning approach has a black-box nature and, before it can be recommended for the routine use, it must be understood more thoroughly. For this purpose, we propose and apply a new explainability method: the oriented, modified integrated gradients (OMIG) method. The design of this method is inspired by the integrated gradientsmethod but adapted considerably to the use case at hand. To further enhance this method, an upsampling technique is developed that produces high-resolution explainability maps for the downsampled data used by the deep learning approach. Comparison with established explainability methods demonstrates that the proposed approach yields substantially more expressive and informative results for our specific use case. Application of the proposed explainability approach generally confirms the validity of the considered deep learning-based mammography image quality assessment (IQA) method. Specifically, it is demonstrated that the predicted image quality is based on a meaningful mapping that makes successful use of certain geometric structures of the images. In addition, the novel explainability method helps us to identify the parts of the employed phantom that have the largest impact on the predicted image quality, and to shed some light on cases in which the trained neural networks fail to work as expected. While tailored to assess a specific approach from deep learning for mammography IQA, the proposed explainability method could also become relevant in other, similar deep learning applications based on high-dimensional images.
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M, D'Afflitto M, Deshpande A, Duque Golding J, Frisira E, Germani Batacchi M, Gomaa A, Hay D, Hutchison R, Iakovou A, Iakovou D, Ismail E, Jefferson S, Jones L, Khouli Y, Knowles C, Mason J, McCaughan R, Moffatt J, Morawala A, Nadir H, Neyroud F, Nikookam Y, Parmar A, Pinto L, Ramamoorthy R, Richards E, Thomson S, Trainer C, Valetopoulou A, Vassiliou A, Wantman A, Wilde S, Dickinson M, Rockall T, Senn D, Wcislo K, Zalmay P, Adelekan K, Allen K, Bajaj M, Gatumbu P, Hang S, Hashmi Y, Kaur T, Kawesha A, Kisiel A, Woodmass M, Adelowo T, Ahari D, Alhwaishel K, Atherton R, Clayton B, Cockroft A, Curtis Lopez C, Hilton M, Ismail N, Kouadria M, Lee L, MacConnachie A, Monks F, Mungroo S, Nikoletopoulou C, Pearce L, Sara X, Shahid A, Suresh G, Wilcha R, Atiyah A, Davies E, Dermanis A, Gibbons H, Hyde A, Lawson A, Lee C, Leung-Tack M, Li Saw Hee J, Mostafa O, Nair D, Pattani N, Plumbley-Jones J, Pufal K, Ramesh P, Sanghera J, Saram S, Scadding S, See S, Stringer H, Torrance A, Vardon H, Wyn-Griffiths F, Brew A, Kaur G, Soni D, Tickle A, Akbar Z, Appleyard T, Figg K, Jayawardena P, Johnson A, Kamran Siddiqui Z, Lacy-Colson J, Oatham R, Rowlands B, Sludden E, Turnbull C, Allin D, Ansar Z, Azeez Z, Dale VH, Garg J, Horner A, Jones S, Knight S, McGregor C, McKenna J, McLelland T, Packham-Smith A, Rowsell K, Spector-Hill I, Adeniken E, Baker J, Bartlett M, Chikomba L, Connell B, Deekonda P, Dhar M, Elmansouri A, Gamage K, Goodhew R, Hanna P, Knight J, Luca A, Maasoumi N, Mahamoud F, Manji S, Marwaha PK, Mason F, Oluboyede A, Pigott L, Razaq AM, Richardson M, Saddaoui I, Wijeyendram P, Yau S, Atkins W, Liang K, Miles N, Praveen B, Ashai S, Braganza J, Common J, Cundy A, Davies R, Guthrie J, Handa I, Iqbal M, Ismail R, Jones C, Jones I, Lee KS, Levene A, Okocha M, Olivier J, Smith A, Subramaniam E, Tandle S, Wang A, Watson A, Wilson C, Chan XHF, Khoo E, Montgomery C, Norris M, Pugalenthi PP, Common T, Cook E, Mistry H, Shinmar HS, Agarwal G, Bandyopadhyay S, Brazier B, Carroll L, Goede A, Harbourne A, Lakhani A, Lami M, Larwood J, Martin J, Merchant J, Pattenden S, Pradhan A, Raafat N, Rothwell E, Shammoon Y, Sudarshan R, Vickers E, Wingfield L, Ashworth I, Azizi S, Bhate R, Chowdhury T, Christou A, Davies L, Dwaraknath M, Farah Y, Garner J, Gureviciute E, Hart E, Jain A, Javid S, Kankam HK, Kaur Toor P, Kaz R, Kermali M, Khan I, Mattson A, McManus A, Murphy M, Nair K, Ngemoh D, Norton E, Olabiran A, Parry L, Payne T, Pillai K, Price S, Punjabi K, Raghunathan A, Ramwell A, Raza M, Ritehnia J, Simpson G, Smith W, Sodeinde S, Studd L, Subramaniam M, Thomas J, Towey S, Tsang E, Tuteja D, Vasani J, Vio M, Badran A, Adams J, Anthony Wilkinson J, Asvandi S, Austin T, Bald A, Bix E, Carrick M, Chander B, Chowdhury S, Cooper Drake B, Crosbie S, D Portela S, Francis D, Gallagher C, Gillespie R, Gravett H, Gupta P, Ilyas C, James G, Johny J, Jones A, Kinder F, MacLeod C, Macrow C, Maqsood-Shah A, Mather J, McCann L, McMahon R, Mitham E, Mohamed M, Munton E, Nightingale K, O'Neill K, Onyemuchara I, Senior R, Shanahan A, Sherlock J, Spyridoulias A, Stavrou C, Stokes D, Tamang R, Taylor E, Trafford C, Uden C, Waddington C, Yassin D, Zaman M, Bangi S, Cheng T, Chew D, Hussain N, Imani-Masouleh S, Mahasivam G, McKnight G, Ng HL, Ota HC, Pasha T, Ravindran W, Shah K, Vishnu K S, Zaman S, Carr W, Cope S, Eagles EJ, Howarth-Maddison M, Li CY, Reed J, Ridge A, Stubbs T, Teasdaled D, Umar R, Worthington J, Dhebri A, Kalenderov R, Alattas A, Arain Z, Bhudia R, Chia D, Daniel S, Dar T, Garland H, Girish M, Hampson A, Kyriacou H, Lehovsky K, Mullins W, Omorphos N, Vasdev N, Venkatesh A, Waldock W, Bhandari A, Brown G, Choa G, Eichenauer CE, Ezennia K, Kidwai Z, Lloyd-Thomas A, Macaskill Stewart A, Massardi C, Sinclair E, Skajaa N, Smith M, Tan I, Afsheen N, Anuar A, Azam Z, Bhatia P, Davies-kelly N, Dickinson S, Elkawafi M, Ganapathy M, Gupta S, Khoury EG, Licudi D, Mehta V, Neequaye S, Nita G, Tay VL, Zhao S, Botsa E, Cuthbert H, Elliott J, Furlepa M, Lehmann J, Mangtani A, Narayan A, Nazarian S, Parmar C, Shah D, Shaw C, Zhao Z, Beck C, Caldwell S, Clements JM, French B, Kenny R, Kirk S, Lindsay J, McClung A, McLaughlin N, Watson S, Whiteside E, Alyacoubi S, Arumugam V, Beg R, Dawas K, Garg S, Lloyd ER, Mahfouz Y, Manobharath N, Moonesinghe R, Morka N, Patel K, Prashar J, Yip S, Adeeko ES, Ajekigbe F, Bhat A, Evans C, Farrugia A, Gurung C, Long T, Malik B, Manirajan S, Newport D, Rayer J, Ridha A, Ross E, Saran T, Sinker A, Waruingi D, Allen R, Al Sadek Y, Alves do Canto Brum H, Asharaf H, Ashman M, Balakumar V, Barrington J, Baskaran R, Berry A, Bhachoo H, Bilal A, Boaden L, Chia WL, Covell G, Crook D, Dadnam F, Davis L, De Berker H, Doyle C, Fox C, Gruffydd-Davies M, Hafouda Y, Hill A, Hubbard E, Hunter A, Inpadhas V, Jamshaid M, Jandu G, Jeyanthi M, Jones T, Kantor C, Kwak SY, Malik N, Matt R, McNulty P, Miles C, Mohomed A, Myat P, Niharika J, Nixon A, O'Reilly D, Parmar K, Pengelly S, Price L, Ramsden M, Turnor R, Wales E, Waring H, Wu M, Yang T, Ye TTS, Zander A, Zeicu C, Bellam S, Francombe J, Kawamoto N, Rahman MR, Sathyanarayana A, Tang HT, Cheung J, Hollingshead J, Page V, Sugarman J, Wong E, Chiong J, Fung E, Kan SY, Kiang J, Kok J, Krahelski O, Liew MY, Lyell B, Sharif Z, Speake D, Alim L, Amakye NY, Chandrasekaran J, Chandratreya N, Drake J, Owoso T, Thu YM, Abou El Ela Bourquin B, Alberts J, Chapman D, Rehnnuma N, Ainsworth K, Carpenter H, Emmanuel T, Fisher T, Gabrel M, Guan Z, Hollows S, Hotouras A, Ip Fung Chun N, Jaffer S, Kallikas G, Kennedy N, Lewinsohn B, Liu FY, Mohammed S, Rutherfurd A, Situ T, Stammer A, Taylor F, Thin N, Urgesi E, Zhang N, Ahmad MA, Bishop A, Bowes A, Dixit A, Glasson R, Hatta S, Hatt K, Larcombe S, Preece J, Riordan E, Fegredo D, Haq MZ, Li C, McCann G, Stewart D, Baraza W, Bhullar D, Burt G, Coyle J, Deans J, Devine A, Hird R, Ikotun O, Manchip G, Ross C, Storey L, Tan WWL, Tse C, Warner C, Whitehead M, Wu F, Court EL, Crisp E, Huttman M, Mayes F, Robertson H, Rosen H, Sandberg C, Smith H, Al Bakry M, Ashwell W, Bajaj S, Bandyopadhyay D, Browlee O, Burway S, Chand CP, Elsayeh K, Elsharkawi A, Evans E, Ferrin S, Fort-Schaale A, Iacob M, I K, Impelliziere Licastro G, Mankoo AS, Olaniyan T, Otun J, Pereira R, Reddy R, Saeed D, Simmonds O, Singhal G, Tron K, Wickstone C, Williams R, Bradshaw E, De Kock Jewell V, Houlden C, Knight C, Metezai H, Mirza-Davies A, Seymour Z, Spink D, Wischhusen S. Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study. Lancet Digit Health 2022; 4:e520-e531. [PMID: 35750401 DOI: 10.1016/s2589-7500(22)00069-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 01/07/2022] [Accepted: 04/06/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. METHODS We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). FINDINGS In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]). INTERPRETATION In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. FUNDING British Journal of Surgery Society.
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Miller C, Wright K, Dono J, Pettigrew S, Wakefield M, Coveney J, Wittert G, Roder D, Durkin S, Martin J, Ettridge K. "You can't just eat 16 teaspoons of sugar so why would you drink 16 teaspoons' worth of sugar?": a qualitative study of young adults' reactions to sugary drink warning labels. BMC Public Health 2022; 22:1241. [PMID: 35733102 PMCID: PMC9219237 DOI: 10.1186/s12889-022-13648-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 06/06/2022] [Indexed: 11/10/2022] Open
Abstract
Background Several jurisdictions have introduced nutrient warning front of pack (FoP) labels in an effort to curb consumption of ultra-processed foods and beverages high in free sugars (sugars added to foods and beverages, and sugars naturally present in honey, syrups, fruit juices and fruit juice concentrates). This study aimed to explore consumer understanding and perceptions of FoP warning labels that convey different nutritional and health information messages regarding the consumption of sugary drinks. Methods Sixteen focus groups were held with 4–8 young adults per group (aged 18–24; n = 105 participants in total) stratified by education level, location (rural centres, large cities) and gender (males, females) to ensure diversity. Labels shown to participants during group discussions included text warning labels of health effects, exercise equivalents, calorie/kilojoule information and sugar content as a “high in” label and as teaspoons (text and pictograms). Thematic analysis was undertaken. Results Four themes were identified related to participants’ perceived effectiveness of labels: the extent to which labels were perceived to be useful, relevant and credible; the extent to which a label elicited shock or disgust (perceived aversiveness); the extent to which the label message was resistant to self-exemption; and participants’ perceived potential of the label to reduce purchasing and consumption behaviour. Across all four themes, labels communicating the number of teaspoons of sugar in a sugary drink (whether by text or pictogram) were perceived as the most impactful, resistant to self-exemption and to have the greatest potential to reduce consumption, with enhanced reactions to the pictogram label. Labels depicting health effects, exercise equivalents, calorie/kilojoule information or a general ‘high in sugar’ warning were perceived by consumers to be less effective in one or more themes. Conclusions Labels conveying the amount of sugar in a beverage in teaspoons were perceived as highly factual, relatable and interpretable, and as having the greatest potential to impact consumption attitudes and intentions. Further quantitative studies are required to compare the potential effectiveness of the teaspoons of sugar labels in reducing purchasing and consumption behaviour than other alternative warning labels, such as health effects or “high in” sugar labels. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13648-1.
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Affiliation(s)
- C Miller
- The University of Adelaide's School of Public Health, Adelaide, Australia. .,Health Policy Centre, South Australian Health and Medical Research Institute, North Terrace, Adelaide, South Australia, 5000, Australia.
| | - K Wright
- Health Policy Centre, South Australian Health and Medical Research Institute, North Terrace, Adelaide, South Australia, 5000, Australia.,The University of Adelaide's School of Psychology, Adelaide, Australia
| | - J Dono
- Health Policy Centre, South Australian Health and Medical Research Institute, North Terrace, Adelaide, South Australia, 5000, Australia.,The University of Adelaide's School of Psychology, Adelaide, Australia
| | - S Pettigrew
- Food Policy, The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - M Wakefield
- Centre for Behavioural Research in Cancer, Cancer Council Victoria, Melbourne, Australia.,School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - J Coveney
- College of Nursing and Health Sciences, Flinders University, Adelaide, Australia
| | - G Wittert
- Freemasons Foundation Centre for Men's Health, Faculty of Health Sciences, University of Adelaide, Adelaide, Australia.,Centre for Nutrition and GI Diseases, South Australian Health and Medical Research Institute, Adelaide, Australia
| | - D Roder
- Cancer Epidemiology and Population Health, University of South Australia, Adelaide, Australia
| | - S Durkin
- Centre for Behavioural Research in Cancer, Cancer Council Victoria, Melbourne, Australia.,School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - J Martin
- Obesity Policy Coalition and Alcohol and Obesity Policy, Cancer Council Victoria, Melbourne, Australia
| | - K Ettridge
- Health Policy Centre, South Australian Health and Medical Research Institute, North Terrace, Adelaide, South Australia, 5000, Australia.,The University of Adelaide's School of Psychology, Adelaide, Australia
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Martin J. Martin Library Leadership survey: development, reliability and validation. LM 2022. [DOI: 10.1108/lm-11-2021-0100] [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: 11/17/2022]
Abstract
Purpose An effective measurement of library leadership is crucial to understanding the current state of library leadership and to developing library leaders. This study sought to validate and measure the reliability of the Martin Library Leadership survey.Design/methodology/approach This survey is based on the Martin Library Leadership Definition, an evidence-based definition of library leadership. The first version of the survey consisted of 28 questions plus questions on respondent and library leader demographics. Each question measured one of the three components of the definition. This version of the survey was distributed to multiple ALA listservs and after analysis 16 items were removed. The resulting 12 question version of the survey was sent to the same ALA listservs and completed by 291 librarians and library staff from various library types and library work areas. The responses were analyzed using SPSS.Findings Exploratory factor analysis found three factors that align with the three components of the Martin Library Leadership Definition, and questions loaded in their expected factors at least 0.7. Cronbach's alpha was used to determine internal consistency. The alpha for the entire survey was 0.956. The Martin Library Leadership survey was validated and found to be reliable.Originality/value The results of this study provide strong and consistent evidence the Martin Library Leadership survey is valid and can be used in further library leadership research and professional development.
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Duffy FJR, Papadopolou C, Barrie J, Hendry A, Andrew M, Martin J. 799 FRAILTY MATTERS PROJECT. Age Ageing 2022. [DOI: 10.1093/ageing/afac036.799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Introduction
Preventing and managing frailty is a new area for many community practitioners yet frailty specific education remains limited. We aimed to understand and strengthen the capability of District Nurses (DNs) in leading personalised care for older people with frailty.
Methods
We conducted a participatory action research (PAR) study with DNs in one Scottish NHS Board area. Phase 1 involved three focus groups (n = 17); one one-to-one interviews; and collection of baseline team dynamics questionnaires (n = 10). Evidence from phase 1 informed co-design of an educational framework, delivered in Phase 2 as a combined coaching and educational programme through small group learning, web based coaching and bite sized online education. Interactive sessions were supported by a person-centred coach and 2 older citizen ‘co-coaches’ to bring the experience of people affected by frailty. In Phase 3 we analysed participant feedback and assessed transferability to other disciplines and to health and care settings across the UK.
Results
At baseline, DNs did not perceive frailty as a long term condition. They identified a need for help to understand the concept of frailty and to build skills and confidence in delivering community interventions. Participants embraced the coaching and educational intervention and valued the opportunity for dialogue with peers and citizen co-coaches about what really matters to patients, families and professionals. Our survey of other disciplines and teams highlighted this educational programme is both relevant and transferable.
Conclusion
Through co-design we developed a contextually sensitive programme that makes sense of frailty in the reality of both community professionals and people living with frailty. Combining technical knowledge and relational skills-building with peer support and coaching helps prepare DNs to lead interprofessional teams caring for people living with frailty. The educational framework and combined coaching and educational package are highly applicable to interdisciplinary teams in other community settings.
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Affiliation(s)
| | | | - J Barrie
- University of the West of Scotland
| | | | - M Andrew
- Health and Social Care Alliance Scotland
| | - J Martin
- Health and Social Care Alliance Scotland
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van de Kuit A, Krishnan RJ, Mallee WH, Goedhart LM, Lambert B, Doornberg JN, Vervest TMJS, Martin J. Surgical site infection after wound closure with staples versus sutures in elective knee and hip arthroplasty: a systematic review and meta-analysis. Arthroplasty 2022; 4:12. [PMID: 35241172 PMCID: PMC8896293 DOI: 10.1186/s42836-021-00110-7] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 12/23/2021] [Indexed: 11/10/2022] Open
Abstract
Purpose This systematic review and meta-analysis aimed to study surgical site infection of wound closure using staples versus sutures in elective knee and hip arthroplasties. Methods A systematic literature review was performed to search for randomized controlled trials that compared surgical site infection after wound closure using staples versus sutures in elective knee and hip arthroplasties. The primary outcome was surgical site infection. The risk of bias was assessed with the Cochrane risk of bias assessment tool. The relative risk and 95% confidence interval with a random-effects model were assessed. Results Eight studies were included in this study, including 2 studies with a low risk of bias, 4 studies having ‘some concerns’, and 2 studies with high risk of bias. Significant difference was not found in the risk of SSI for patients with staples (n = 557) versus sutures (n = 573) (RR: 1.70, 95% CI: 0.94–3.08, I2 = 16%). The results were similar after excluding the studies with a high risk of bias (RR: 1.67, 95% CI: 0.91–3.07, I2 = 32%). Analysis of studies with low risk of bias revealed a significantly higher risk of surgical site infection in patients with staples (n = 331) compared to sutures (n = 331) (RR: 2.56, 95% CI: 1.20–5.44, I2 = 0%). There was no difference between continuous and interrupted sutures (P > 0.05). In hip arthroplasty, stapling carried a significantly higher risk of surgical site infection than suturing (RR: 2.51, 95% CI: 1.15–5.50, I2 = 0%), but there was no significant difference in knee arthroplasty (RR: 0.87, 95% CI: 0.33–2.25, I2 = 22%; P > 0.05). Conclusions Stapling might carry a higher risk of surgical site infection than suturing in elective knee and hip arthroplasties, especially in hip arthroplasty. Supplementary Information The online version contains supplementary material available at 10.1186/s42836-021-00110-7.
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Affiliation(s)
- A van de Kuit
- University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - R J Krishnan
- Department of Anesthesia & Perioperative Medicine and Department of Epidemiology & Biostatistics, MEDICI Centre, University of Western Ontario, London, Canada
| | - W H Mallee
- Department of Orthopaedics, Joint Research, Onze Lieve Vrouwe Gasthuis, Amsterdam, the Netherlands
| | - L M Goedhart
- Department of Orthopaedics, University Medical Center Groningen, Postbus 30.001, 9700 RB, Groningen, The Netherlands
| | - B Lambert
- Department of Orthopaedics, University Medical Center Groningen, Postbus 30.001, 9700 RB, Groningen, The Netherlands
| | - J N Doornberg
- Department of Orthopaedics, University Medical Center Groningen, Postbus 30.001, 9700 RB, Groningen, The Netherlands.
| | - T M J S Vervest
- Department of Orthopaedics, Tergooi Hospital, Hilversum, The Netherlands
| | - J Martin
- Department of Anesthesia & Perioperative Medicine and Department of Epidemiology & Biostatistics, MEDICI Centre, University of Western Ontario, London, Canada
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Yap C, Solovyeva O, Yin Z, Martin J, Manickavasagar T, Weir C, Lee S, Dimairo M, Liu R, Kightley A, de Bono J. 53P Assessing the reporting quality of early phase dose-finding trials. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.01.018] [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
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TALBOT B, Martin J, Burman J, Kaur N, Garvey V, Knight J. POS-713 PROOF OF CONCEPT FOR A POINT OF CARE AFFORDABLE DIALYSIS SYSTEM. Kidney Int Rep 2022. [DOI: 10.1016/j.ekir.2022.01.747] [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: 11/17/2022] Open
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Bender Ignacio RA, Shapiro AE, Nance RM, Whitney BM, Delaney J, Bamford L, Wooten D, Karris M, Mathews WC, Kim HN, Van Rompaey SE, Keruly JC, Burkholder G, Napravnik S, Mayer KH, Jacobson J, Saag MS, Moore RD, Eron JJ, Willig AL, Christopoulos KA, Martin J, Hunt PW, Crane HM, Kitahata MM, Cachay E. Racial and ethnic disparities in COVID-19 disease incidence independent of comorbidities, among people with HIV in the US. medRxiv 2021:2021.12.07.21267296. [PMID: 34909782 PMCID: PMC8669849 DOI: 10.1101/2021.12.07.21267296] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
OBJECTIVES To define the incidence of clinically-detected COVID-19 in people with HIV (PWH) in the US and evaluate how racial and ethnic disparities, comorbidities, and HIV-related factors contribute to risk of COVID-19. DESIGN Observational study within the CFAR Network of Integrated Clinical Systems cohort in 7 cities during 2020. METHODS We calculated cumulative incidence rates of COVID-19 diagnosis among PWH in routine care by key characteristics including race/ethnicity, current and lowest CD4 count, and geographic area. We evaluated risk factors for COVID-19 among PWH using relative risk regression models adjusted with disease risk scores. RESULTS Among 16,056 PWH in care, of whom 44.5% were Black, 12.5% were Hispanic, with a median age of 52 years (IQR 40-59), 18% had a current CD4 count < 350, including 7% < 200; 95.5% were on antiretroviral therapy, and 85.6% were virologically suppressed. Overall in 2020, 649 PWH were diagnosed with COVID-19 for a rate of 4.94 cases per 100 person-years. The cumulative incidence of COVID-19 was 2.4-fold and 1.7-fold higher in Hispanic and Black PWH respectively, than non-Hispanic White PWH. In adjusted analyses, factors associated with COVID-19 included female sex, Hispanic or Black identity, lowest historical CD4 count <350 (proxy for CD4 nadir), current low CD4/CD8 ratio, diabetes, and obesity. CONCLUSIONS Our results suggest that the presence of structural racial inequities above and beyond medical comorbidities increased the risk of COVID-19 among PWHPWH with immune exhaustion as evidenced by lowest historical CD4 or current low CD4:CD8 ratio had greater risk of COVID-19.
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Affiliation(s)
- R A Bender Ignacio
- University of Washington, Seattle, WA, USA
- Fred Hutchinson Cancer Research Center
| | - A E Shapiro
- University of Washington, Seattle, WA, USA
- Fred Hutchinson Cancer Research Center
| | - R M Nance
- University of Washington, Seattle, WA, USA
| | | | | | - L Bamford
- University of California San Diego, San Diego, CA, USA
| | - D Wooten
- University of California San Diego, San Diego, CA, USA
| | - M Karris
- University of California San Diego, San Diego, CA, USA
| | - W C Mathews
- University of California San Diego, San Diego, CA, USA
| | - H N Kim
- University of Washington, Seattle, WA, USA
| | | | - J C Keruly
- Johns Hopkins School of Medicine, Baltimore, MD
| | - G Burkholder
- University of Alabama at Birmingham, Birmingham, AL, USA
| | - S Napravnik
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - K H Mayer
- Fenway Health and Harvard Medical School, Boston, MA, USA
| | - J Jacobson
- Case Western Reserve University, Cleveland, OH, USA
| | - M S Saag
- University of Alabama at Birmingham, Birmingham, AL, USA
| | - R D Moore
- Johns Hopkins School of Medicine, Baltimore, MD
| | - J J Eron
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - A L Willig
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - J Martin
- University of California, San Francisco, San Francisco, CA, USA
| | - P W Hunt
- University of California, San Francisco, San Francisco, CA, USA
| | - H M Crane
- University of Washington, Seattle, WA, USA
| | | | - E Cachay
- University of California San Diego, San Diego, CA, USA
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Baid U, Pati S, Thakur S, Edwards B, Sheller M, Martin J, Bakas S. NIMG-32. THE FEDERATED TUMOR SEGMENTATION (FETS) INITIATIVE: THE FIRST REAL-WORLD LARGE-SCALE DATA-PRIVATE COLLABORATION FOCUSING ON NEURO-ONCOLOGY. Neuro Oncol 2021. [DOI: 10.1093/neuonc/noab196.532] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
PURPOSE
Robustness and generalizability of artificial intelligent (AI) methods is reliant on the training data size and diversity, which are currently hindered in multi-institutional healthcare collaborations by data ownership and legal concerns. To address these, we introduce the Federated Tumor Segmentation (FeTS) Initiative, as an international consortium using federated learning (FL) for data-private multi-institutional collaborations, where AI models leverage data at participating institutions, without sharing data between them. The initial FeTS use-case focused on detecting brain tumor boundaries in MRI.
METHODS
The FeTS tool incorporates: 1) MRI pre-processing, including image registration and brain extraction; 2) automatic delineation of tumor sub-regions, by label fusion of pretrained top-performing BraTS methods; 3) tools for manual delineation refinements; 4) model training. 55 international institutions identified local retrospective cohorts of glioblastoma patients. Ground truth was generated using the first 3 FeTS functionality modes as mentioned earlier. Finally, the FL training mode comprises of i) an AI model trained on local data, ii) local model updates shared with an aggregator, which iii) combines updates from all collaborators to generate a consensus model, and iv) circulates the consensus model back to all collaborators for iterative performance improvements.
RESULTS
The first FeTS consensus model, from 23 institutions with data of 2,200 patients, showed an average improvement of 11.1% in the performance of the model on each collaborator’s validation data, when compared to a model trained on the publicly available BraTS data (n=231).
CONCLUSION
Our findings support that data increase alone would lead to AI performance improvements without any algorithmic development, hence indicating that the model performance would improve further when trained with all 55 collaborating institutions. FL enables AI model training with knowledge from data of geographically-distinct collaborators, without ever having to share any data, hence overcoming hurdles relating to legal, ownership, and technical concerns of data sharing.
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Affiliation(s)
- Ujjwal Baid
- University of Pennsylvania, Philadelphia, PA, USA
| | - Sarthak Pati
- University of Pennsylvania, Philadelphia, PA, USA
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Brillant G, Martin J. Void fraction in a co-current two-phase flow through a prototypical PWR spent fuel assembly. Nuclear Engineering and Design 2021. [DOI: 10.1016/j.nucengdes.2021.111401] [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: 11/30/2022]
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Hemming K, Martin J, Gallos I, Coomarasamy A, Middleton L. Interim data monitoring in cluster randomised trials: Practical issues and a case study. Clin Trials 2021; 18:552-561. [PMID: 34154426 PMCID: PMC8479148 DOI: 10.1177/17407745211024751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND There is an abundance of guidance for the interim monitoring of individually randomised trials. While methodological literature exists on how to extend these methods to cluster randomised trials, there is little guidance on practical implementation. Cluster trials have many features which make their monitoring needs different. We outline the methodological and practical challenges of interim monitoring of cluster trials; and apply these considerations to a case study. CASE STUDY The E-MOTIVE study is an 80-cluster randomised trial of a bundle of interventions to treat postpartum haemorrhage. The proposed data monitoring plan includes (1) monitor sample size assumptions, (2) monitor for evidence of selection bias, and (3) an interim assessment of the primary outcome, as well as monitoring data completeness. The timing of the sample size monitoring is chosen with both consideration of statistical precision and to allow time to recruit more clusters. Monitoring for selection bias involves comparing individual-level characteristics and numbers recruited between study arms to identify any post-randomisation participant identification bias. An interim analysis of outcomes presented with 99.9% confidence intervals using the Haybittle-Peto approach should mitigate any concern regarding the inflation of type-I error. The pragmatic nature of the trial means monitoring for adherence is not relevant, as it is built into a process evaluation. CONCLUSIONS The interim analyses of cluster trials have a number of important differences to monitoring individually randomised trials. In cluster trials, there will often be a greater need to monitor nuisance parameters, yet there will often be considerable uncertainty in their estimation. This means the utility of sample size re-estimation can be questionable particularly when there are practical or funding difficulties associated with making any changes to planned sample sizes. Perhaps most importantly interim monitoring has the potential to identify selection bias, particularly in trials with post-randomisation identification or recruitment. Finally, the pragmatic nature of cluster trials might mean that the utility of methods to allow for interim monitoring of outcomes based on statistical testing, or monitoring for adherence to study interventions, are less relevant. Our intention is to facilitate the planning of future cluster randomised trials and to promote discussion and debate to improve monitoring of these studies.
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Affiliation(s)
- K Hemming
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - J Martin
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - I Gallos
- University of Birmingham, Birmingham, UK
| | - A Coomarasamy
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - L Middleton
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
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Peacock S, Briggs D, Barnardo M, Battle R, Brookes P, Callaghan C, Clark B, Collins C, Day S, Diaz Burlinson N, Dunn P, Fernando R, Fuggle S, Harmer A, Kallon D, Keegan D, Key T, Lawson E, Lloyd S, Martin J, McCaughan J, Middleton D, Partheniou F, Poles A, Rees T, Sage D, Santos-Nunez E, Shaw O, Willicombe M, Worthington J. BSHI/BTS guidance on crossmatching before deceased donor kidney transplantation. Int J Immunogenet 2021; 49:22-29. [PMID: 34555264 PMCID: PMC9292213 DOI: 10.1111/iji.12558] [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] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 12/12/2022]
Abstract
All UK H&I laboratories and transplant units operate under a single national kidney offering policy, but there have been variations in approach regarding when to undertake the pre‐transplant crossmatch test. In order to minimize cold ischaemia times for deceased donor kidney transplantation we sought to find ways to be able to report a crossmatch result as early as possible in the donation process. A panel of experts in transplant surgery, nephrology, specialist nursing in organ donation and H&I (all relevant UK laboratories represented) assessed evidence and opinion concerning five factors that relate to the effectiveness of the crossmatch process, as follows: when the result should be ready for reporting; what level of donor HLA typing is needed; crossmatch sample type and availability; fairness and equity; risks and patient safety. Guidelines aimed at improving practice based on these issues are presented, and we expect that following these will allow H&I laboratories to contribute to reducing CIT in deceased donor kidney transplantation.
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Affiliation(s)
- S Peacock
- Tissue Typing Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Briggs
- H&I Laboratory, NHSBT Birmingham Vincent Drive, Birmingham, UK
| | - M Barnardo
- Clinical Transplant Immunology, Churchill Hospital, Oxford, UK
| | - R Battle
- H&I Laboratory, SNBTS, Edinburgh, UK
| | - P Brookes
- H&I Laboratory, Harefield Hospital, Harefield, UK
| | - C Callaghan
- Department of Nephrology and Transplantation, Guy's Hospital, London, UK
| | - B Clark
- H&I Laboratory, Leeds Teaching Hospitals NHS Trust, UK
| | - C Collins
- H&I Laboratory, NHSBT Birmingham Vincent Drive, Birmingham, UK
| | - S Day
- H&I Laboratory, Southmead Hospital, Bristol, UK
| | - N Diaz Burlinson
- Transplantation Laboratory, Manchester Royal Infirmary, Manchester, UK
| | - P Dunn
- Transplant Laboratory, Leicester General Hospital, Leicester, UK
| | - R Fernando
- H&I Laboratory, The Anthony Nolan Laboratories, Royal Free Hospital, UK
| | - S Fuggle
- Organ Donation & Transplantation, NHSBT, Stoke Gifford, Bristol, UK
| | - A Harmer
- H&I Laboratory, NHSBT Barnsley Centre, Barnsley, UK
| | - D Kallon
- H & I Laboratory, Royal London Hospital, London, UK
| | - D Keegan
- Department of H&I, Beaumont Hospital, Dublin, UK
| | - T Key
- H&I Laboratory, NHSBT Barnsley Centre, Barnsley, UK
| | - E Lawson
- Organ Donation and Transplantation, NHSBT, Birmingham, UK
| | - S Lloyd
- Welsh Transplantation & Immunogenetics Laboratory, Cardiff, UK
| | - J Martin
- H&I Laboratory, Belfast Health and Social Care Trust, Belfast, UK
| | - J McCaughan
- H&I Laboratory, Belfast Health and Social Care Trust, Belfast, UK
| | - D Middleton
- H&I Laboratory, Liverpool Foundation Trust, Liverpool, UK
| | - F Partheniou
- H&I Laboratory, Liverpool Foundation Trust, Liverpool, UK
| | - A Poles
- H&I Laboratory, University Hospitals Plymouth, Plymouth, UK.,H&I Laboratory, NHSBT Filton, Bristol, UK
| | - T Rees
- Welsh Transplantation & Immunogenetics Laboratory, Cardiff, UK
| | - D Sage
- H&I Laboratory, NHSBT Tooting Centre, London, UK
| | - E Santos-Nunez
- H&I Laboratory, Imperial College Healthcare NHS Trust, London, UK
| | - O Shaw
- H&I Laboratory, Viapath, Guys & St Thomas, London, UK
| | - M Willicombe
- Department of Immunology and Inflammation, Imperial College London, UK
| | - J Worthington
- Transplantation Laboratory, Manchester Royal Infirmary, Manchester, UK
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Hadaschik B, Fanti S, Ost P, Tunariu N, de Nunzio C, Antoni L, Lukac M, Martin J, Pissart G, Wapenaar R, Mottet N. 649TiP PRIMORDIUM: A randomized, international, trial-in-progress of adding apalutamide to radiotherapy and an LHRH agonist in high-risk patients with PSMA-PET-positive hormone-sensitive prostate cancer. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.08.1162] [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: 11/29/2022] Open
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Abstract
Purpose
Over one million organisations have a quality management system (QMS) certified to the ISO 9001 standard; however, the system requires a lot of resources and its value has been questioned. This critique also leads to a questioning of the strategic relevance of quality management. The purpose of this paper is to explore how different types of uses of QMS correlate with management perceptions of quality management in terms of respect, cost and strategic importance.
Design/methodology/approach
The paper is based on a mixed method data collection strategy, quantitative data being collected from a survey in 8 organisations (n = 108) and qualitative data being collected from 12 interviews with quality managers in 12 different organisations.
Findings
The paper shows that a compliance-oriented QMS usage will more likely lead to a view of quality management as costly and of little respect, than a business or improvement-oriented QMS usage. Moreover, it nuances the view on compliance-oriented usage, showing that it is mainly documentation that negatively influences how management views quality management, whereas standardisation that is part of the compliance-oriented use is perceived as more value-adding.
Originality/value
This paper suggests three types of QMS use, namely, business management, improvement, and compliance-oriented use, and that a wise selection of how to use the QMS will affect the respect, strategic importance and cost that management associates with quality management.
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Ceci A, Muñoz-Ballester C, Tegge AN, Brown KL, Umans RA, Michel FM, Patel D, Tewari B, Martin J, Alcoreza O, Maynard T, Martinez-Martinez D, Bordwine P, Bissell N, Friedlander MJ, Sontheimer H, Finkielstein CV. Development and implementation of a scalable and versatile test for COVID-19 diagnostics in rural communities. Nat Commun 2021; 12:4400. [PMID: 34285229 PMCID: PMC8292415 DOI: 10.1038/s41467-021-24552-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 06/24/2021] [Indexed: 12/23/2022] Open
Abstract
Rapid and widespread testing of severe acute respiratory coronavirus 2 (SARS-CoV-2) is essential for an effective public health response aimed at containing and mitigating the coronavirus disease 2019 (COVID-19) pandemic. Successful health policy implementation relies on early identification of infected individuals and extensive contact tracing. However, rural communities, where resources for testing are sparse or simply absent, face distinctive challenges to achieving this success. Accordingly, we report the development of an academic, public land grant University laboratory-based detection assay for the identification of SARS-CoV-2 in samples from various clinical specimens that can be readily deployed in areas where access to testing is limited. The test, which is a quantitative reverse transcription polymerase chain reaction (RT-qPCR)-based procedure, was validated on samples provided by the state laboratory and submitted for FDA Emergency Use Authorization. Our test exhibits comparable sensitivity and exceeds specificity and inclusivity values compared to other molecular assays. Additionally, this test can be re-configured to meet supply chain shortages, modified for scale up demands, and is amenable to several clinical specimens. Test development also involved 3D engineering critical supplies and formulating a stable collection media that allowed samples to be transported for hours over a dispersed rural region without the need for a cold-chain. These two elements that were critical when shortages impacted testing and when personnel needed to reach areas that were geographically isolated from the testing center. Overall, using a robust, easy-to-adapt methodology, we show that an academic laboratory can supplement COVID-19 testing needs and help local health departments assess and manage outbreaks. This additional testing capacity is particularly germane for smaller cities and rural regions that would otherwise be unable to meet the testing demand.
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Affiliation(s)
- A Ceci
- Molecular Diagnostics Laboratory, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, USA
| | - C Muñoz-Ballester
- Center for Glial Biology in Health, Disease, and Cancer, Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA
| | - A N Tegge
- Department of Statistics, Virginia Tech, Blacksburg, VA, USA
| | - K L Brown
- Virginia Tech Carilion School of Medicine, Roanoke, VA, USA
| | - R A Umans
- Center for Glial Biology in Health, Disease, and Cancer, Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA
| | - F M Michel
- Department of Geosciences, Virginia Tech, Blacksburg, VA, USA
| | - D Patel
- Center for Glial Biology in Health, Disease, and Cancer, Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA
| | - B Tewari
- Center for Glial Biology in Health, Disease, and Cancer, Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA
| | - J Martin
- Center for Glial Biology in Health, Disease, and Cancer, Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA
- Translational Biology, Medicine, and Health Graduate Program, Virginia Tech, Roanoke, VA, USA
| | - O Alcoreza
- Center for Glial Biology in Health, Disease, and Cancer, Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA
- Virginia Tech Carilion School of Medicine, Roanoke, VA, USA
- Translational Biology, Medicine, and Health Graduate Program, Virginia Tech, Roanoke, VA, USA
| | - T Maynard
- Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA
| | - D Martinez-Martinez
- MRC London Institute of Medical Sciences, London, UK
- Institute of Clinical Sciences, Imperial College London, London, UK
| | - P Bordwine
- Division of Surveillance and Investigation, Office of Epidemiology, Virginia Department of Health, Christiansburg, USA
| | - N Bissell
- New River Valley Health District, Virginia Department of Health, Christiansburg, USA
| | - M J Friedlander
- Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA
| | - H Sontheimer
- Center for Glial Biology in Health, Disease, and Cancer, Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA
| | - C V Finkielstein
- Molecular Diagnostics Laboratory, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, USA.
- Integrated Cellular Responses Laboratory, Fralin Biomedical Research Institute at VTC, Roanoke, VA, USA.
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA.
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Fuller L, Martin J, Ma Y, King S, Sen S. Control of Texture and Morphology of Zinc Films through Pulsed Methods from Additive‐Free Electrolytes. ChemistrySelect 2021. [DOI: 10.1002/slct.202101193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Lee Fuller
- Department of Chemistry & Biochemistry University of Wisconsin-La Crosse La Crosse WI 54601 USA
| | - Jason Martin
- Department of Chemistry & Biochemistry University of Wisconsin-La Crosse La Crosse WI 54601 USA
| | - Yuanman Ma
- Department of Chemistry & Biochemistry University of Wisconsin-La Crosse La Crosse WI 54601 USA
| | - Seth King
- Department of Physics University of Wisconsin-La Crosse La Crosse, WI 54601 USA
| | - Sujat Sen
- Department of Chemistry & Biochemistry University of Wisconsin-La Crosse La Crosse WI 54601 USA
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Martin C, Burnet E, Ronayette-Preira A, de Carli P, Martin J, Delmas L, Prieur B, Burgel PR. Patient perspectives following initiation of elexacaftor-tezacaftor-ivacaftor in people with cystic fibrosis and advanced lung disease. Respir Med Res 2021; 80:100829. [PMID: 34091202 DOI: 10.1016/j.resmer.2021.100829] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 04/28/2021] [Accepted: 05/07/2021] [Indexed: 11/19/2022]
Abstract
BACKGOUND Elexacaftor-tezacaftor-ivacaftor partially restores cystic fibrosis transmembrane conductance regulator function, and has been shown to induce significant clinical improvement in patients with at least one Phe508del allele. Yet little data exist on patient perspectives following elexacaftor-tezacaftor-ivacaftor initiation. METHODS A mixed methods study was conducted using an online 13-item questionnaire (including 9 closed questions and 4 open questions), submitted from July 10th to August 21th 2020 to French patients aged 12 years and older with advanced CF who were treated with elexacaftor-tezacaftor-ivacaftor. Their responses were summarized as numbers (%), and free-text items were analysed using a grounded theory approach. RESULTS Of 245 patients who started elexacaftor-tezacaftor-ivacaftor in France, 101 (41%) participated. Median [IQR] age was 35 [28-41] years and duration of elexacaftor-tezacaftor-ivacaftor treatment was 4.3 [3.0-5.6] months. Patients generally reported a rapid impact on respiratory symptoms, sleep quality, general well-being and physical self-esteem, and a reduction in overall treatment burden. The majority of patients contrasted treatment burden, symptom severity, depression and a closed future marked by death or transplantation before elexacaftor-tezacaftor-ivacaftor, to renewed and unexpected physical strength, leading to greater self-confidence, autonomy and long-term planning, after treatment initiation. A small number of patients expressed concerns, mainly regarding changes in body representation and/or the fear of becoming dependent on the treatment. CONCLUSION After initiation of elexacaftor-tezacaftor-ivacaftor, CF patients with advanced disease reported rapid and positive physical, psychological and social effects, which translated into improved quality of life and the formulation of new life goals.
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Affiliation(s)
- C Martin
- Université de Paris, Institut Cochin, Inserm U1016, Paris, France; Respiratory Medicine and National Reference Cystic Fibrosis Reference Center, Cochin Hospital, Assistance Publique Hôpitaux de Paris (AP-HP), Paris, France; ERN-Lung CF network
| | - E Burnet
- Respiratory Medicine and National Reference Cystic Fibrosis Reference Center, Cochin Hospital, Assistance Publique Hôpitaux de Paris (AP-HP), Paris, France; ERN-Lung CF network
| | | | - P de Carli
- Vaincre la Mucoviscidose, 75013 Paris, France
| | | | | | - B Prieur
- Respiratory Medicine and National Reference Cystic Fibrosis Reference Center, Cochin Hospital, Assistance Publique Hôpitaux de Paris (AP-HP), Paris, France; ERN-Lung CF network
| | - P-R Burgel
- Université de Paris, Institut Cochin, Inserm U1016, Paris, France; Respiratory Medicine and National Reference Cystic Fibrosis Reference Center, Cochin Hospital, Assistance Publique Hôpitaux de Paris (AP-HP), Paris, France; ERN-Lung CF network.
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Crocker-Buque T, Williams S, Brentnall AR, Gabe R, Duffy S, Prowle JR, Orkin C, Kunst H, Cutino-Moguel T, Zenner D, Bloom B, Melzer M, de Freitas S, Darmalingam M, McCafferty K, Kapil V, Pfeffer P, Martin J, Gourtsoyannis Y, Chandran S, Dhariwal A, Rachman R, Milligan I, Mabayoje D, Adobah E, Falconer J, Nugent H, Yaqoob M, Collier D, Pearse R, Caulfield M, Tiberi S. The Barts Health NHS Trust COVID-19 cohort: characteristics, outcomes and risk scoring of patients in East London. Int J Tuberc Lung Dis 2021; 25:358-366. [PMID: 33977903 DOI: 10.5588/ijtld.20.0926] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND: Barts Health National Health Service Trust (BHNHST) serves a diverse population of 2.5 million people in London, UK. We undertook a health services assessment of factors used to evaluate the risk of severe acute respiratory coronavirus 2 (SARS-CoV-2) infection.METHODS: Patients with confirmed polymerase chain reaction (PCR) test results admitted between 1 March and 1 August 2020 were included, alongwith clinician-diagnosed suspected cases. Prognostic factors from the 4C Mortality score and 4C Deterioration scores were extracted from electronic health records and logistic regression was used to quantify the strength of association with 28-day mortality and clinical deterioration using national death registry linkage.RESULTS: Of 2783 patients, 1621 had a confirmed diagnosis, of whom 61% were male and 54% were from Black and Minority Ethnic groups; 26% died within 28 days of admission. Mortality was strongly associated with older age. The 4C mortality score had good stratification of risk with a calibration slope of 1.14 (95% CI 1.01-1.27). It may have under-estimated mortality risk in those with a high respiratory rate or requiring oxygen.CONCLUSION: Patients in this diverse patient cohort had similar mortality associated with prognostic factors to the 4C score derivation sample, but survival might be poorer in those with respiratory failure.
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Affiliation(s)
- T Crocker-Buque
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - S Williams
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - A R Brentnall
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, Mile End Road, London, UK
| | - R Gabe
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, Mile End Road, London, UK, Barts Clinical Trials Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Mile End Road, London, UK
| | - S Duffy
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, Mile End Road, London, UK
| | - J R Prowle
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, The William Harvey Research Institute, Queen Mary University of London Charterhouse Square, London, UK
| | - C Orkin
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK
| | - H Kunst
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK
| | - T Cutino-Moguel
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - D Zenner
- Centre for Global Public Health, Queen Mary University of London, Mile End Road, London, UK
| | - B Bloom
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK
| | - M Melzer
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, Whipps Cross University Hospital, Barts Health NHS Trust, Leytonstone, London, UK
| | - S de Freitas
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - M Darmalingam
- Whipps Cross University Hospital, Barts Health NHS Trust, Leytonstone, London, UK
| | - K McCafferty
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - V Kapil
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, The William Harvey Research Institute, Queen Mary University of London Charterhouse Square, London, UK, St Bartholomew´s Hospital, Barts Health NHS Trust, West Smithfield, London, UK
| | - P Pfeffer
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, The William Harvey Research Institute, Queen Mary University of London Charterhouse Square, London, UK
| | - J Martin
- Newham University Hospital, Barts Health NHS Trust, London, UK
| | - Y Gourtsoyannis
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - S Chandran
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - A Dhariwal
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - R Rachman
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - I Milligan
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - D Mabayoje
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - E Adobah
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - J Falconer
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - H Nugent
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK
| | - M Yaqoob
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK
| | - D Collier
- Barts Clinical Trials Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Mile End Road, London, UK, The William Harvey Research Institute, Queen Mary University of London Charterhouse Square, London, UK
| | - R Pearse
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, The William Harvey Research Institute, Queen Mary University of London Charterhouse Square, London, UK
| | - M Caulfield
- The William Harvey Research Institute, Queen Mary University of London Charterhouse Square, London, UK
| | - S Tiberi
- The Royal London Hospital, Barts Health NHS Trust, Whitechapel, London, UK, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK, Newham University Hospital, Barts Health NHS Trust, London, UK
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50
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Hamed M, Logan A, Gruszczyk AV, Beach TE, James AM, Dare AJ, Barlow A, Martin J, Georgakopoulos N, Gane AM, Crick K, Fouto D, Fear C, Thiru S, Dolezalova N, Ferdinand JR, Clatworthy MR, Hosgood SA, Nicholson ML, Murphy MP, Saeb-Parsy K. Mitochondria-targeted antioxidant MitoQ ameliorates ischaemia-reperfusion injury in kidney transplantation models. Br J Surg 2021; 108:1072-1081. [PMID: 33963377 DOI: 10.1093/bjs/znab108] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 02/28/2021] [Indexed: 11/12/2022]
Abstract
BACKGROUND Ischaemia-reperfusion (IR) injury makes a major contribution to graft damage during kidney transplantation. Oxidative damage to mitochondria is an early event in IR injury. Therefore, the uptake, safety, and efficacy of the mitochondria-targeted antioxidant MitoQ were investigated in models of transplant IR injury. METHODS MitoQ uptake by warm and cooled pairs of pig and declined human kidneys was measured when preserved in cold static storage or by hypothermic machine perfusion. Pairs of pigs' kidneys were exposed to defined periods of warm and cold ischaemia, flushed and stored at 4°C with or without MitoQ (50 nmol/l to 250 µmol/l), followed by reperfusion with oxygenated autologous blood in an ex vivo normothermic perfusion (EVNP). Pairs of declined human kidneys were flushed and stored with or without MitoQ (5-100 µmol/l) at 4°C for 6 h and underwent EVNP with ABO group-matched blood. RESULTS Stable and concentration-dependent uptake of MitoQ was demonstrated for up to 24 h in pig and human kidneys. Total blood flow and urine output were significantly greater in pig kidneys treated with 50 µmol/l MitoQ compared with controls (P = 0.006 and P = 0.007 respectively). In proof-of-concept experiments, blood flow after 1 h of EVNP was significantly greater in human kidneys treated with 50 µmol/l MitoQ than in controls (P ≤ 0.001). Total urine output was numerically higher in the 50-µmol/l MitoQ group compared with the control, but the difference did not reach statistical significance (P = 0.054). CONCLUSION Mitochondria-targeted antioxidant MitoQ can be administered to ischaemic kidneys simply and effectively during cold storage, and may improve outcomes after transplantation.
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Affiliation(s)
- M Hamed
- Department of Surgery, University of Cambridge, Cambridge, UK.,MRC Mitochondrial Biology Unit, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - A Logan
- MRC Mitochondrial Biology Unit, Cambridge, UK
| | - A V Gruszczyk
- Department of Surgery, University of Cambridge, Cambridge, UK.,MRC Mitochondrial Biology Unit, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - T E Beach
- Department of Surgery, University of Cambridge, Cambridge, UK.,MRC Mitochondrial Biology Unit, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - A M James
- MRC Mitochondrial Biology Unit, Cambridge, UK
| | - A J Dare
- Department of Surgery, University of Cambridge, Cambridge, UK.,MRC Mitochondrial Biology Unit, Cambridge, UK
| | - A Barlow
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - J Martin
- Department of Surgery, University of Cambridge, Cambridge, UK.,MRC Mitochondrial Biology Unit, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - N Georgakopoulos
- Department of Surgery, University of Cambridge, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - A M Gane
- Department of Surgery, University of Cambridge, Cambridge, UK.,MRC Mitochondrial Biology Unit, Cambridge, UK
| | - K Crick
- Department of Surgery, University of Cambridge, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - D Fouto
- Department of Surgery, University of Cambridge, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - C Fear
- Department of Surgery, University of Cambridge, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - S Thiru
- Department of Pathology, Cambridge University Hospitals NHS Trust, Addenbrooke's Hospital, Cambridge, UK
| | - N Dolezalova
- Department of Surgery, University of Cambridge, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - J R Ferdinand
- Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK.,Department of Medicine, University of Cambridge, Cambridge, UK
| | - M R Clatworthy
- Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK.,Department of Medicine, University of Cambridge, Cambridge, UK
| | - S A Hosgood
- Department of Surgery, University of Cambridge, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - M L Nicholson
- Department of Surgery, University of Cambridge, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
| | - M P Murphy
- MRC Mitochondrial Biology Unit, Cambridge, UK.,Department of Medicine, University of Cambridge, Cambridge, UK
| | - K Saeb-Parsy
- Department of Surgery, University of Cambridge, Cambridge, UK.,Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre and NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, Cambridge, UK
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