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Merle DA, Sen M, Armento A, Stanton CM, Thee EF, Meester-Smoor MA, Kaiser M, Clark SJ, Klaver CCW, Keane PA, Wright AF, Ehrmann M, Ueffing M. 10q26 - The enigma in age-related macular degeneration. Prog Retin Eye Res 2023; 96:101154. [PMID: 36513584 DOI: 10.1016/j.preteyeres.2022.101154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/21/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
Despite comprehensive research efforts over the last decades, the pathomechanisms of age-related macular degeneration (AMD) remain far from being understood. Large-scale genome wide association studies (GWAS) were able to provide a defined set of genetic aberrations which contribute to disease risk, with the strongest contributors mapping to distinct regions on chromosome 1 and 10. While the chromosome 1 locus comprises factors of the complement system with well-known functions, the role of the 10q26-locus in AMD-pathophysiology remains enigmatic. 10q26 harbors a cluster of three functional genes, namely PLEKHA1, ARMS2 and HTRA1, with most of the AMD-associated genetic variants mapping to the latter two genes. High linkage disequilibrium between ARMS2 and HTRA1 has kept association studies from reliably defining the risk-causing gene for long and only very recently the genetic risk region has been narrowed to ARMS2, suggesting that this is the true AMD gene at this locus. However, genetic associations alone do not suffice to prove causality and one or more of the 14 SNPs on this haplotype may be involved in long-range control of gene expression, leaving HTRA1 and PLEKHA1 still suspects in the pathogenic pathway. Both, ARMS2 and HTRA1 have been linked to extracellular matrix homeostasis, yet their exact molecular function as well as their role in AMD pathogenesis remains to be uncovered. The transcriptional regulation of the 10q26 locus adds an additional level of complexity, given, that gene-regulatory as well as epigenetic alterations may influence expression levels from 10q26 in diseased individuals. Here, we provide a comprehensive overview on the 10q26 locus and its three gene products on various levels of biological complexity and discuss current and future research strategies to shed light on one of the remaining enigmatic spots in the AMD landscape.
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Affiliation(s)
- David A Merle
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Department for Ophthalmology, University Eye Clinic, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Department of Ophthalmology, Medical University of Graz, 8036, Graz, Austria.
| | - Merve Sen
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany
| | - Angela Armento
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany
| | - Chloe M Stanton
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Eric F Thee
- Department of Ophthalmology, Erasmus University Medical Center, 3015GD, Rotterdam, Netherlands; Department of Epidemiology, Erasmus University Medical Center, 3015CE, Rotterdam, Netherlands
| | - Magda A Meester-Smoor
- Department of Ophthalmology, Erasmus University Medical Center, 3015GD, Rotterdam, Netherlands; Department of Epidemiology, Erasmus University Medical Center, 3015CE, Rotterdam, Netherlands
| | - Markus Kaiser
- Center of Medical Biotechnology, Faculty of Biology, University Duisburg-Essen, 45117, Essen, Germany
| | - Simon J Clark
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Department for Ophthalmology, University Eye Clinic, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK
| | - Caroline C W Klaver
- Department of Ophthalmology, Erasmus University Medical Center, 3015GD, Rotterdam, Netherlands; Department of Epidemiology, Erasmus University Medical Center, 3015CE, Rotterdam, Netherlands; Department of Ophthalmology, Radboudumc, 6525EX, Nijmegen, Netherlands; Institute of Molecular and Clinical Ophthalmology Basel, CH-4031, Basel, Switzerland
| | - Pearse A Keane
- Institute for Health Research, Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, London, EC1V 2PD, UK
| | - Alan F Wright
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Michael Ehrmann
- Center of Medical Biotechnology, Faculty of Biology, University Duisburg-Essen, 45117, Essen, Germany
| | - Marius Ueffing
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Department for Ophthalmology, University Eye Clinic, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany.
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Yang J, Wu S, Dai R, Yu W, Chen Y. Publication trends of artificial intelligence in retina in 10 years: Where do we stand? Front Med (Lausanne) 2022; 9:1001673. [PMID: 36405613 PMCID: PMC9666394 DOI: 10.3389/fmed.2022.1001673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 09/20/2022] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Artificial intelligence (AI) has been applied in the field of retina. The purpose of this study was to analyze the study trends within AI in retina by reporting on publication trends, to identify journals, countries, authors, international collaborations, and keywords involved in AI in retina. MATERIALS AND METHODS A cross-sectional study. Bibliometric methods were used to evaluate global production and development trends in AI in retina since 2012 using Web of Science Core Collection. RESULTS A total of 599 publications were retrieved ultimately. We found that AI in retina is a very attractive topic in scientific and medical community. No journal was found to specialize in AI in retina. The USA, China, and India were the three most productive countries. Authors from Austria, Singapore, and England also had worldwide academic influence. China has shown the greatest rapid increase in publication numbers. International collaboration could increase influence in this field. Keywords revealed that diabetic retinopathy, optical coherence tomography on multiple diseases, algorithm were three popular topics in the field. Most of top journals and top publication on AI in retina were mainly focused on engineering and computing, rather than medicine. CONCLUSION These results helped clarify the current status and future trends in researches of AI in retina. This study may be useful for clinicians and scientists to have a general overview of this field, and better understand the main actors in this field (including authors, journals, and countries). Researches are supposed to focus on more retinal diseases, multiple modal imaging, and performance of AI models in real-world clinical application. Collaboration among countries and institutions is common in current research of AI in retina.
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Affiliation(s)
- Jingyuan Yang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shan Wu
- Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Rongping Dai
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weihong Yu
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Youxin Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,*Correspondence: Youxin Chen,
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O'Byrne C, Abbas A, Korot E, Keane PA. Automated deep learning in ophthalmology: AI that can build AI. Curr Opin Ophthalmol 2021; 32:406-412. [PMID: 34231529 DOI: 10.1097/icu.0000000000000779] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE OF REVIEW The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demonstrating the effectiveness of this technique and discuss current challenges and future directions of automated deep learning. RECENT FINDINGS There are several commercially available automated deep learning platforms. Although specific features differ between platforms, they utilise the common approach of supervised learning. Ophthalmology is an exemplar speciality in the area, with a number of recent proof-of-concept studies exploring classification of retinal fundus photographs, optical coherence tomography images and indocyanine green angiography images. Automated deep learning has also demonstrated impressive results in other specialities such as dermatology, radiology and histopathology. SUMMARY Automated deep learning allows users without coding expertise to develop deep learning algorithms. It is rapidly establishing itself as a valuable tool for those with limited technical experience. Despite residual challenges, it offers considerable potential in the future of patient management, clinical research and medical education. VIDEO ABSTRACT http://links.lww.com/COOP/A44.
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Affiliation(s)
- Ciara O'Byrne
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Trinity College School of Medicine, Dublin, Ireland
| | - Abdallah Abbas
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- University College London Medical School, London, UK
| | - Edward Korot
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Byers Eye Institute, Stanford University, Stanford, California, USA
| | - Pearse A Keane
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation, London, UK
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Nakashima T, Ogata S, Noguchi T, Tahara Y, Onozuka D, Kato S, Yamagata Y, Kojima S, Iwami T, Sakamoto T, Nagao K, Nonogi H, Yasuda S, Iihara K, Neumar R, Nishimura K. Machine learning model for predicting out-of-hospital cardiac arrests using meteorological and chronological data. Heart 2021; 107:1084-1091. [PMID: 34001636 PMCID: PMC8223656 DOI: 10.1136/heartjnl-2020-318726] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/30/2021] [Accepted: 04/05/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To evaluate a predictive model for robust estimation of daily out-of-hospital cardiac arrest (OHCA) incidence using a suite of machine learning (ML) approaches and high-resolution meteorological and chronological data. METHODS In this population-based study, we combined an OHCA nationwide registry and high-resolution meteorological and chronological datasets from Japan. We developed a model to predict daily OHCA incidence with a training dataset for 2005-2013 using the eXtreme Gradient Boosting algorithm. A dataset for 2014-2015 was used to test the predictive model. The main outcome was the accuracy of the predictive model for the number of daily OHCA events, based on mean absolute error (MAE) and mean absolute percentage error (MAPE). In general, a model with MAPE less than 10% is considered highly accurate. RESULTS Among the 1 299 784 OHCA cases, 661 052 OHCA cases of cardiac origin (525 374 cases in the training dataset on which fourfold cross-validation was performed and 135 678 cases in the testing dataset) were included in the analysis. Compared with the ML models using meteorological or chronological variables alone, the ML model with combined meteorological and chronological variables had the highest predictive accuracy in the training (MAE 1.314 and MAPE 7.007%) and testing datasets (MAE 1.547 and MAPE 7.788%). Sunday, Monday, holiday, winter, low ambient temperature and large interday or intraday temperature difference were more strongly associated with OHCA incidence than other the meteorological and chronological variables. CONCLUSIONS A ML predictive model using comprehensive daily meteorological and chronological data allows for highly precise estimates of OHCA incidence.
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Affiliation(s)
- Takahiro Nakashima
- Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Department of Preventive Medicine and Epidemiologic Informatics, National Cerebral Cardiovascular Centre, Suita, Japan
| | - Soshiro Ogata
- Department of Preventive Medicine and Epidemiologic Informatics, National Cerebral Cardiovascular Centre, Suita, Japan
| | - Teruo Noguchi
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Centre, Suita, Japan
| | - Yoshio Tahara
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Centre, Suita, Japan
| | - Daisuke Onozuka
- Department of Preventive Medicine and Epidemiologic Informatics, National Cerebral Cardiovascular Centre, Suita, Japan
| | | | | | - Sunao Kojima
- Department of General Internal Medicine 3, Kawasaki Medical School, Kurashiki, Japan
| | - Taku Iwami
- Health Service, Kyoto University, Kyoto, Japan
| | - Tetsuya Sakamoto
- Department of Emergency Medicine, Teikyo University, Itabashi-ku, Japan
| | - Ken Nagao
- Cardiovascular Centre, Nihon University Hospital, Tokyo, Japan
| | | | - Satoshi Yasuda
- Department of Cardiovascular Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Koji Iihara
- National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Robert Neumar
- Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Kunihiro Nishimura
- Department of Preventive Medicine and Epidemiologic Informatics, National Cerebral Cardiovascular Centre, Suita, Japan
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de Val J, Sohal G, Sarwar A, Ahmed H, Singh I, Coleman JJ. Investigating the challenges and opportunities for medicines management in an NHS field hospital during the COVID-19 pandemic. Eur J Hosp Pharm 2020; 28:10-15. [PMID: 33277234 PMCID: PMC7788195 DOI: 10.1136/ejhpharm-2020-002364] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/10/2020] [Accepted: 07/14/2020] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Hospital admissions from COVID-19 initially increased rapidly within the UK. National Health Service (NHS) field hospitals are part of a capacity building response built at great scale and speed to respond to the anticipated increased demand the NHS faces during this time. NHS Nightingale Hospital Birmingham (NHB) is modelled to treat mild to moderate (non-critical care) COVID-19 disease, to provide step-down capacity for patients in recovery, or for palliating patients in the dying phase of their disease in the Midlands. Opportunities and challenges presented for optimal medicines management (MM) during the development of the NHB are investigated, and a framework developed to support future NHS field hospitals of this model. METHODS A team, comprised of an associate medical director, trust chief pharmacist and senior pharmacists iteratively developed a framework to convert the large non-hospital setting into a functioning NHS field hospital with standardised MM processes adjusted appropriately to cope with operational constraints in the pandemic situation. NHB has, because of its repurposing, both challenges and advantages affecting MM that influence development of the framework. Throughout implementation, a 7-week period between announcement and opening, there was continuous evaluation, external stakeholder validation and peer review. RESULTS The PESTLE model, a mechanism of analysis to identify elements of a project environment (Political, Environmental, Social, Technological, Legal and Economic), was applied to identify influencing factors and support detailed project planning. Compliance with medicines legislation was at the forefront of all MM process development for the NHB field hospital. Internal factors were identified by the core MM team, resulting in a workforce, education & training and clinical pharmacy MM plan. DISCUSSION MM processes are extensive and integral to NHS field hospitals. The presented framework of influencing factors may support future NHS field hospital development. It is pertinent to have a broad team working approach to any large-scale project such as outlined here, and suggest the identified factors be used as a core framework for development of any future MM processes in NHS field hospitals.
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Affiliation(s)
- Jennifer de Val
- Pharmacy Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Gurjit Sohal
- Pharmacy Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Asif Sarwar
- Pharmacy Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Haseeb Ahmed
- Pharmacy Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Inderjit Singh
- Pharmacy Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Jamie J Coleman
- Pharmacy Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK .,School of Medicine, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
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Dargél AA. Which sounds better: Analog or digital psychiatry? Bipolar Disord 2020; 22:785-787. [PMID: 32991789 DOI: 10.1111/bdi.13003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/07/2020] [Accepted: 09/19/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Aroldo A Dargél
- Pasteur Institute, Perception and Memory Unit, Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche 3571, Paris, France
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Delivering personalized medicine in retinal care: from artificial intelligence algorithms to clinical application. Curr Opin Ophthalmol 2020; 31:329-336. [DOI: 10.1097/icu.0000000000000677] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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