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Wang Y, Du Z, Zhang W, Wang X, Lin X, Liu Y, Deng Y, Zhang D, Gu J, Xu L, Hao Y. Cohort Profile: The Pearl River Cohort Study. Int J Epidemiol 2024; 53:dyae112. [PMID: 39186943 DOI: 10.1093/ije/dyae112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 08/23/2024] [Indexed: 08/28/2024] Open
Affiliation(s)
- Ying Wang
- School of Public Health, Sun Yat-sen University, Guangzhou, China
- Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Zhicheng Du
- School of Public Health, Sun Yat-sen University, Guangzhou, China
- Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Wangjian Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou, China
- Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Xiaowen Wang
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking University, Peking, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Peking, China
| | - Xiao Lin
- School of Public Health, Sun Yat-sen University, Guangzhou, China
- Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Yu Liu
- School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yu Deng
- Laboratory Animal Center, Sun Yat-sen University, Shenzhen, China
| | - Dingmei Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Jing Gu
- School of Public Health, Sun Yat-sen University, Guangzhou, China
- Research Center for Health Information & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Lin Xu
- School of Public Health, Sun Yat-sen University, Guangzhou, China
- School of Public Health, The University of Hong Kong, Hong Kong, China
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking University, Peking, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Peking, China
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2
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Su TH, Kao JH. Role of artificial intelligence in the management of chronic hepatitis B infection. Clin Liver Dis (Hoboken) 2024; 23:e0164. [PMID: 38707242 PMCID: PMC11068129 DOI: 10.1097/cld.0000000000000164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 02/20/2024] [Indexed: 05/07/2024] Open
Affiliation(s)
- Tung-Hung Su
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Hepatitis Research Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Jia-Horng Kao
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Hepatitis Research Center, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan
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Rabaan AA, Bakhrebah MA, Alotaibi J, Natto ZS, Alkhaibari RS, Alawad E, Alshammari HM, Alwarthan S, Alhajri M, Almogbel MS, Aljohani MH, Alofi FS, Alharbi N, Al-Adsani W, Alsulaiman AM, Aldali J, Ibrahim FA, Almaghrabi RS, Al-Omari A, Garout M. Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review. J Infect Public Health 2023; 16:1837-1847. [PMID: 37769584 DOI: 10.1016/j.jiph.2023.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/19/2023] [Accepted: 08/27/2023] [Indexed: 10/03/2023] Open
Abstract
Infectious diseases present a global challenge, requiring accurate diagnosis, effective treatments, and preventive measures. Artificial intelligence (AI) has emerged as a promising tool for analysing complex molecular data and improving the diagnosis, treatment, and prevention of infectious diseases. Computer-aided detection (CAD) using convolutional neural networks (CNN) has gained prominence for diagnosing tuberculosis (TB) and other infectious diseases such as COVID-19, HIV, and viral pneumonia. The review discusses the challenges and limitations associated with AI in this field and explores various machine-learning models and AI-based approaches. Artificial neural networks (ANN), recurrent neural networks (RNN), support vector machines (SVM), multilayer neural networks (MLNN), CNN, long short-term memory (LSTM), and random forests (RF) are among the models discussed. The review emphasizes the potential of AI to enhance the accuracy and efficiency of diagnosis, treatment, and prevention of infectious diseases, highlighting the need for further research and development in this area.
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Affiliation(s)
- Ali A Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran 31311, Saudi Arabia; College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; Department of Public Health and Nutrition, The University of Haripur, Haripur 22610, Pakistan.
| | - Muhammed A Bakhrebah
- Life Science and Environment Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Jawaher Alotaibi
- Infectious Diseases Unit, Department of Medicine, King Faisal Specialist Hospital and Research Center, Riyadh 11564, Saudi Arabia
| | - Zuhair S Natto
- Department of Dental Public Health, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Rahaf S Alkhaibari
- Molecular Diagnostic Laboratory, Dammam Regional Laboratory and Blood Bank, Dammam 31411, Saudi Arabia
| | - Eman Alawad
- Adult Infectious Diseases Department, Prince Mohammed Bin Abdulaziz Hospital, Riyadh 11474, Saudi Arabia
| | - Huda M Alshammari
- Clinical Pharmacy Department, College of Pharmacy, Northern Border University, Arar 9280, Saudi Arabia
| | - Sara Alwarthan
- Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
| | - Mashael Alhajri
- Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
| | - Mohammed S Almogbel
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 4030, Saudi Arabia
| | - Maha H Aljohani
- Department of Infectious Diseases, King Fahad Hospital, Madinah 42351, Saudi Arabia
| | - Fadwa S Alofi
- Department of Infectious Diseases, King Fahad Hospital, Madinah 42351, Saudi Arabia
| | - Nada Alharbi
- Department of Basic Medical Sciences, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
| | - Wasl Al-Adsani
- Department of Medicine, Infectious Diseases Hospital, Kuwait City 63537, Kuwait; Department of Infectious Diseases, Hampton Veterans Administration Medical Center, Hampton, VA 23667, USA
| | | | - Jehad Aldali
- Department of Pathology, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh 13317, Saudi Arabia
| | - Fatimah Al Ibrahim
- Infectious Disease Division, Department of Internal Medicine, Dammam Medical Complex, Dammam 32245, Saudi Arabia
| | - Reem S Almaghrabi
- Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia
| | - Awad Al-Omari
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; Research Center, Dr. Sulaiman Al Habib Medical Group, Riyadh 11372, Saudi Arabia
| | - Mohammed Garout
- Department of Community Medicine and Health Care for Pilgrims, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
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4
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Moulaei K, Sharifi H, Bahaadinbeigy K, Haghdoost AA, Nasiri N. Machine learning for prediction of viral hepatitis: A systematic review and meta-analysis. Int J Med Inform 2023; 179:105243. [PMID: 37806178 DOI: 10.1016/j.ijmedinf.2023.105243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/21/2023] [Accepted: 10/01/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND Lack of accurate and timely diagnosis of hepatitis poses obstacles to effective treatment, disease progression prevention, complication reduction, and life-saving interventions of patients. Utilizing machine learning can greatly enhance the achievement of timely and precise disease diagnosis. Therefore, we carried out this systematic review and meta-analysis to explore the performance of machine learning algorithms in predicting viral hepatitis. METHODS Using an extensive literature search in PubMed, Scopus, and Web of Science databases until June 15, 2023, English publications on hepatitis prediction using machine learning algorithms were included. Two authors independently extracted pertinent information from the selected studies. The PRISMA 2020 checklist was followed for study selection and result reporting. The risk of bias was checked using the International Journal of Medical Informatics (IJMEDI) checklist. Data were analyzed using the 'metandi' command in Stata 17. RESULTS Twenty-one original studies were included, covering 82 algorithms. Sixteen studies utilized five algorithms to predict hepatitis B. Ten studies used five algorithms for hepatitis C prediction. For hepatitis B prediction, the SVM algorithms demonstrated the highest sensitivity (90.0%; 95% confidence interval (CI): 77.0%-96.0%), specificity (94%; 95% CI: 90.0%-97.0%), and a diagnostic odds ratio (DOR) of 145 (95% CI: 37.0-559.0). In the case of hepatitis C, the KNN algorithms exhibited the highest sensitivity (80%; 95% CI:30.0%-97.0%), specificity (95%; 95% CI: 58.0%-99.0%), and DOR (72; 95% CI: 3.0-1644.0) for prediction. CONCLUSION SVM and KNN demonstrated superior performance in predicting hepatitis. The proper algorithm along with clinical practice could improve hepatitis prediction and management.
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Affiliation(s)
- Khadijeh Moulaei
- Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hamid Sharifi
- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | | | - Ali Akbar Haghdoost
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Naser Nasiri
- School of Public Health, Jiroft University of Medical Sciences, Jiroft, Kerman, Iran.
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Ajuwon BI, Awotundun ON, Richardson A, Roper K, Sheel M, Rahman N, Salako A, Lidbury BA. Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact. Int J Med Inform 2023; 179:105244. [PMID: 37820561 DOI: 10.1016/j.ijmedinf.2023.105244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 09/08/2023] [Accepted: 10/03/2023] [Indexed: 10/13/2023]
Abstract
BACKGROUND Machine learning (ML) prediction models to support clinical management of blood-borne viral infections are becoming increasingly abundant in medical literature, with a number of competing models being developed for the same outcome or target population. However, evidence on the quality of these ML prediction models are limited. OBJECTIVE This study aimed to evaluate the development and quality of reporting of ML prediction models that could facilitate timely clinical management of blood-borne viral infections. METHODS We conducted narrative evidence synthesis following the synthesis without meta-analysis guidelines. We searched PubMed and Cochrane Central Register of Controlled Trials for all studies applying ML models for predicting clinical outcomes associated with hepatitis B virus (HBV), human immunodeficiency virus (HIV), or hepatitis C virus (HCV). RESULTS We found 33 unique ML prediction models aiming to support clinical decision making. Overall, 12 (36.4%) focused on HBV, 10 (30.3%) on HCV, 10 on HIV (30.3%) and two (6.1%) on co-infection. Among these, six (18.2%) addressed the diagnosis of infection, 16 (48.5%) the prognosis of infection, eight (24.2%) the prediction of treatment response, two (6.1%) progression through a cascade of care, and one (3.03%) focused on the choice of antiretroviral therapy (ART). Nineteen prediction models (57.6%) were developed using data from high-income countries. Evaluation of prediction models was limited to measures of performance. Detailed information on software code accessibility was often missing. Independent validation on new datasets and/or in other institutions was rarely done. CONCLUSION Promising approaches for ML prediction models in blood-borne viral infections were identified, but the lack of robust validation, interpretability/explainability, and poor quality of reporting hampered their clinical relevance. Our findings highlight important considerations that can inform standard reporting guidelines for ML prediction models in the future (e.g., TRIPOD-AI), and provides critical data to inform robust evaluation of the models.
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Affiliation(s)
- Busayo I Ajuwon
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia; Department of Biosciences and Biotechnology, Faculty of Pure and Applied Sciences, Kwara State University, Malete, Nigeria.
| | - Oluwatosin N Awotundun
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Alice Richardson
- Statistical Support Network, The Australian National University, Acton, ACT, Australia
| | - Katrina Roper
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia
| | - Meru Sheel
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, Australia
| | - Nurudeen Rahman
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Abideen Salako
- Department of Clinical Sciences, Nigerian Institute of Medical Research, Yaba, Lagos State, Nigeria
| | - Brett A Lidbury
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia
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Shi H, Shen Y, Li L. Early prediction of acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit based on extreme gradient boosting. Front Med (Lausanne) 2023; 10:1221602. [PMID: 37720504 PMCID: PMC10501398 DOI: 10.3389/fmed.2023.1221602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/07/2023] [Indexed: 09/19/2023] Open
Abstract
Background Acute kidney injury (AKI) is a common and important complication in patients with gastrointestinal bleeding who are admitted to the intensive care unit. The present study proposes an artificial intelligence solution for acute kidney injury prediction in patients with gastrointestinal bleeding admitted to the intensive care unit. Methods Data were collected from the eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The prediction model was developed using the extreme gradient boosting (XGBoost) model. The area under the receiver operating characteristic curve, accuracy, precision, area under the precision-recall curve (AUC-PR), and F1 score were used to evaluate the predictive performance of each model. Results Logistic regression, XGBoost, and XGBoost with severity scores were used to predict acute kidney injury risk using all features. The XGBoost-based acute kidney injury predictive models including XGBoost and XGBoost+severity scores model showed greater accuracy, recall, precision AUC, AUC-PR, and F1 score compared to logistic regression. Conclusion The XGBoost model obtained better risk prediction for acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit than the traditional logistic regression model, suggesting that machine learning (ML) techniques have the potential to improve the development and validation of predictive models in patients with gastrointestinal bleeding admitted to the intensive care unit.
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Affiliation(s)
- Huanhuan Shi
- Department of Gastroenterology, Peking University Third Hospital, Beijing, China
| | - Yuting Shen
- Department of Gastroenterology, Peking University Third Hospital, Beijing, China
| | - Lu Li
- Department of Internal Medicine, Wuhan University of Technology Hospital, Wuhan, China
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Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models. ADVANCES IN PUBLIC HEALTH 2023. [DOI: 10.1155/2023/4168770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Generally, treatment default of diseases by patients is regarded as the biggest threat to favourable disease treatment outcomes. It is seen as the reason for the resurgence of infectious diseases including tuberculosis in some developing countries. Sadly, its occurrence in chronic disease management is associated with high morbidity and mortality rates. Many reasons have been adduced for this phenomenon. Exploration of treatment default using biographic and behavioral metrics collected from patients and healthcare providers remains a challenge. The focus on contextual nonbiomedical measurements using a supervised machine learning modeling technique is aimed at creating an understanding of the reasons why treatment default occurs, including identifying important contextual parameters that contribute to treatment default. The predicted accuracy scores of four supervised machine learning algorithms, namely, gradient boosting, logistic regression, random forest, and support vector machine were 0.87, 0.90, 0.81, and 0.77, respectively. Additionally, performance indicators such as the positive predicted value score for the four models ranged between 98.72%–98.87%, and the negative predicted values of gradient boosting, logistic regression, random forest, and support vector machine were 50%, 75%, 22.22%, and 50%, respectively. Logistic regression appears to have the highest negative-predicted value score of 75%, with the smallest error margin of 25% and the highest accuracy score of 0.90, and the random forest had the lowest negative predicted value score of 22.22%, registering the highest error margin of 77.78%. By performing a chi-square correlation statistic test of variable independence, this study suggests that age, presence of comorbidities, concern for long queuing/waiting time at treatment facilities, availability of qualified clinicians, and the patient’s nutritional state whether on a controlled diet or not are likely to affect their adherence to disease treatment and could result in an increased risk of default.
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How neighborhood environment modified the effects of power outages on multiple health outcomes in New York state? HYGIENE AND ENVIRONMENTAL HEALTH ADVANCES 2022; 4. [PMID: 36777309 PMCID: PMC9914544 DOI: 10.1016/j.heha.2022.100039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Background Although power outage (PO) is one of the most important consequences of increasing weather extremes and the health impact of POs has been reported previously, studies on the neighborhood environment underlying the population vulnerability in such situations are limited. This study aimed to identify dominant neighborhood environmental predictors which modified the impact of POs on multiple health outcomes in New York State. Methods We applied a two-stage approach. In the first stage, we used time series analysis to determine the impact of POs (versus non-PO periods) on multiple health outcomes in each power operating division in New York State, 2001-2013. In the second stage, we classified divisions as risk-elevated and non-elevated, then developed predictive models for the elevation status based on 36 neighborhood environmental factors using random forest and gradient boosted trees. Results Consistent across different outcomes, we found predictors representing greater urbanization, particularly, the proportion of residents having access to public transportation (importance ranging from 4.9-15.6%), population density (3.3-16.1%), per capita income (2.3-10.7%), and the density of public infrastructure (0.8-8.5%), were associated with a higher possibility of risk elevation following power outages. Additionally, the percent of minority (-6.3-27.9%) and those with limited English (2.2-8.1%), the percent of sandy soil (6.5-11.8%), and average soil temperature (3.0-15.7%) were also dominant predictors for multiple outcomes. Spatial hotspots of vulnerability generally were located surrounding New York City and in the northwest, the pattern of which was consistent with socioeconomic status. Conclusion Population vulnerability during power outages was dominated by neighborhood environmental factors representing greater urbanization.
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Martínez JA, Alonso-Bernáldez M, Martínez-Urbistondo D, Vargas-Nuñez JA, Ramírez de Molina A, Dávalos A, Ramos-Lopez O. Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases. World J Gastroenterol 2022; 28:6230-6248. [PMID: 36504554 PMCID: PMC9730439 DOI: 10.3748/wjg.v28.i44.6230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/07/2022] [Accepted: 11/16/2022] [Indexed: 11/25/2022] Open
Abstract
The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.
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Affiliation(s)
- J Alfredo Martínez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Marta Alonso-Bernáldez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | | | - Juan A Vargas-Nuñez
- Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro Majadahonda, Madrid 28222, Majadahonda, Spain
| | - Ana Ramírez de Molina
- Molecular Oncology and Nutritional Genomics of Cancer, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Alberto Dávalos
- Laboratory of Epigenetics of Lipid Metabolism, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico
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Shang H, Hu Y, Guo H, Lai R, Fu Y, Xu S, Zeng Y, Xun Z, Liu C, Wu W, Guo J, Ou Q, Chen T. Using machine learning models to predict HBeAg seroconversion in CHB patients receiving pegylated interferon-α monotherapy. J Clin Lab Anal 2022; 36:e24667. [PMID: 36181316 DOI: 10.1002/jcla.24667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Though there are many advantages of pegylated interferon-α (PegIFN-α) treatment to chronic hepatitis B (CHB) patients, the response rate of PegIFN-α is only 30 ~ 40%. Therefore, it is important to explore predictors at baseline and establish models to improve the response rate of PegIFN-α. METHODS We randomly divided 260 HBeAg-positive CHB patients who were not previously treated and received PegIFN-α monotherapy (180 μg/week) into a training dataset (70%) and testing dataset (30%). The intersect features were extracted from 50 routine laboratory variables using the recursive feature elimination method algorithm, Boruta algorithm, and Least Absolute Shrinkage and Selection Operator Regression algorithm in the training dataset. After that, based on the intersect features, eight machine learning models including Logistic Regression, k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Naïve Bayes were applied to evaluate HBeAg seroconversion in HBeAg-positive CHB patients receiving PegIFN-α monotherapy in the training dataset and testing dataset. RESULTS XGBoost model showed the best performance, which had largest AUROC (0.900, 95% CI: 0.85-0.95 and 0.910, 95% CI: 0.84-0.98, in training dataset and testing dataset, respectively), and the best calibration curve performance to predict HBeAg seroconversion. The importance of XGBoost model indicated that treatment time contributed greatest to HBeAg seroconversion, followed by HBV DNA(log), HBeAg, HBeAb, HBcAb, ALT, triglyceride, and ALP. CONCLUSIONS XGBoost model based on common laboratory variables had good performance in predicting HBeAg seroconversion in HBeAg-positive CHB patients receiving PegIFN-α monotherapy.
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Affiliation(s)
- Hongyan Shang
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yuhai Hu
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Hongyan Guo
- The School of Public Health, Fujian Medical University, Fuzhou, China
| | - Ruimin Lai
- Department of the Center of Liver Diseases, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Ya Fu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Siyi Xu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yongbin Zeng
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Zhen Xun
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Can Liu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Wennan Wu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Jianhui Guo
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Qishui Ou
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Tianbin Chen
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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Philips CA, Ahamed R, Abduljaleel JK, Rajesh S, Augustine P. Critical Updates on Chronic Hepatitis B Virus Infection in 2021. Cureus 2021; 13:e19152. [PMID: 34733599 PMCID: PMC8557099 DOI: 10.7759/cureus.19152] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2021] [Indexed: 02/06/2023] Open
Abstract
Chronic hepatitis B virus (HBV) infection is a global healthcare burden in the form of chronic liver disease, cirrhosis, liver failure and liver cancer. There is no definite cure for the virus and even though extensive vaccination programs have reduced the burden of liver disease in the future population, treatment options to eradicate the virus from the host are still lacking. In this review, we discuss in detail current updates on the structure and applied biology of the virus in the host, examine updates to current treatment and explore novel and state-of-the-art therapeutics in the pipeline for management of chronic HBV. Furthermore, we also specifically review clinical updates on HBV-related acute on chronic liver failure (ACLF). Current treatments for chronic HBV infection have seen important updates in the form of considerations for treating patients in the immune tolerant phase and some clarity on end points for treatment and decisions on finite therapy with nucleos(t)ide inhibitors. Ongoing cutting-edge research on HBV biology has helped us identify novel target areas in the life cycle of the virus for application of new therapeutics. Due to improvements in the area of genomics, the hope for therapeutic vaccines, vector-based treatments and focused management aimed at targeting host integration of the virus and thereby a total cure could become a reality in the near future. Newer clinical prognostic tools have improved our understanding of timing of specific treatment options for the catastrophic syndrome of ACLF secondary to reactivation of HBV. In this review, we discuss in detail pertinent updates regarding virus biology and novel therapeutic targets with special focus on the appraisal of prognostic scores and treatment options in HBV-related ACLF.
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Affiliation(s)
- Cyriac A Philips
- Clinical and Translational Hepatology, The Liver Institute, Rajagiri Hospital, Aluva, IND
| | - Rizwan Ahamed
- Gastroenterology and Advanced Gastrointestinal Endoscopy, Center of Excellence in Gastrointestinal Sciences, Rajagiri Hospital, Aluva, IND
| | - Jinsha K Abduljaleel
- Gastroenterology and Advanced Gastrointestinal Endoscopy, Center of Excellence in Gastrointestinal Sciences, Rajagiri Hospital, Aluva, IND
| | - Sasidharan Rajesh
- Diagnostic and Interventional Radiology, Center of Excellence in Gastrointestinal Sciences, Rajagiri Hospital, Aluva, IND
| | - Philip Augustine
- Gastroenterology and Advanced Gastrointestinal Endoscopy, Center of Excellence in Gastrointestinal Sciences, Rajagiri Hospital, Aluva, IND
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Waqas H, Qadir MA. Multilayer heuristics based clustering framework (MHCF) for author name disambiguation. Scientometrics 2021. [DOI: 10.1007/s11192-021-04087-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients. J Med Syst 2021; 45:61. [PMID: 33847850 DOI: 10.1007/s10916-021-01736-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 03/25/2021] [Indexed: 12/29/2022]
Abstract
In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the complex models such as deep learning achieve better performance than the simple ones due to the nonlinear hypotheses of the real world clinical data. However,complex model as a black box, which ignores why it make a certain decision, causes the model distrust from clinicians. To solve these issues, an explainable artificial intelligence (XAI) framework is proposed in this paper to give the global and local interpretation of auxiliary diagnosis of hepatitis while retaining the good prediction performance. First, a public hepatitis classification benchmark from UCI is used to test the feasibility of the framework. Then, the transparent and black-box machine learning models are both employed to forecast the hepatitis deterioration. The transparent models such as logistic regression (LR), decision tree (DT)and k-nearest neighbor (KNN) are picked. While the black-box model such as the eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), random forests (RF) are selected. Finally, the SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME) and Partial Dependence Plots (PDP) are utilized to improve the model interpretation of liver disease. The experimental results show that the complex models outperform the simple ones. The developed RF achieves the highest accuracy (91.9%) among all the models. The proposed framework combining the global and local interpretable methods improves the transparency of complex models, and gets insight into the judgments from the complex models, thereby guiding the treatment strategy and improving the prognosis of hepatitis patients. In addition, the proposed framework could also assist the clinical data scientists to design a more appropriate structure of CAD.
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A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6696041. [PMID: 33860053 PMCID: PMC8024075 DOI: 10.1155/2021/6696041] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 03/17/2021] [Indexed: 02/07/2023]
Abstract
Objective To establish a machine learning model for identifying patients coinfected with hepatitis B virus (HBV) and human immunodeficiency virus (HIV) through two sexual transmission routes in Jiangsu, China. Methods A total of 14197 HIV cases transmitted by homosexual and heterosexual routes were recruited. After data processing, 12469 cases (HIV and HBV, 1033; HIV, 11436) were left for further analysis, including 7849 cases with homosexual transmission and 4620 cases with heterosexual transmission. Univariate logistic regression was used to select variables with significant P value and odds ratio for multivariable analysis. In homosexual transmission and heterosexual transmission groups, 10 and 6 variables were selected, respectively. For identifying HIV individuals coinfected with HBV, a machine learning model was constructed with four algorithms, including Decision Tree, Random Forest, AdaBoost with decision tree (AdaBoost), and extreme gradient boosting decision tree (XGBoost). The detective value of each variable was calculated using the optimal machine learning algorithm. Results AdaBoost algorithm showed the highest efficiency in both transmission groups (homosexual transmission group: accuracy = 0.928, precision = 0.915, recall = 0.944, F − 1 = 0.930, and AUC = 0.96; heterosexual transmission group: accuracy = 0.892, precision = 0.881, recall = 0.905, F − 1 = 0.893, and AUC = 0.98). Calculated by AdaBoost algorithm, the detective value of PLA was the highest in homosexual transmission group, followed by CR, AST, HB, ALT, TBIL, leucocyte, age, marital status, and treatment condition; in the heterosexual transmission group, the detective value of PLA was the highest (consistent with the condition in the homosexual group), followed by ALT, AST, TBIL, leucocyte, and symptom severity. Conclusions The univariate logistics regression combined with the AdaBoost algorithm could accurately screen the risk factors of HBV in HIV coinfection without invasive testing. Further studies are needed to evaluate the utility and feasibility of this model in various settings.
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Caly H, Rabiei H, Coste-Mazeau P, Hantz S, Alain S, Eyraud JL, Chianea T, Caly C, Makowski D, Hadjikhani N, Lemonnier E, Ben-Ari Y. Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD. Sci Rep 2021; 11:6877. [PMID: 33767300 PMCID: PMC7994821 DOI: 10.1038/s41598-021-86320-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 03/10/2021] [Indexed: 01/31/2023] Open
Abstract
To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.
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Affiliation(s)
- Hugues Caly
- Gynecology-Obstetrics Department, Mère-Enfant Hospital, University Hospital Center, Limoges, France
| | - Hamed Rabiei
- BABiomedical, Luminy Scientific Campus, Marseille, France
- Neurochlore, Luminy Scientific Campus, Marseille, France
| | - Perrine Coste-Mazeau
- Gynecology-Obstetrics Department, Mère-Enfant Hospital, University Hospital Center, Limoges, France
| | - Sebastien Hantz
- Bacteriology-Virology-Hygiene Department, University Hospital Center, Limoges, France
- French National Reference Center for Herpes Viruses, University Hospital Center, Limoges, France
| | - Sophie Alain
- Bacteriology-Virology-Hygiene Department, University Hospital Center, Limoges, France
- French National Reference Center for Herpes Viruses, University Hospital Center, Limoges, France
| | - Jean-Luc Eyraud
- Gynecology-Obstetrics Department, Mère-Enfant Hospital, University Hospital Center, Limoges, France
| | - Thierry Chianea
- Department of Biochemistry and Molecular Genetics, Dupuytren University Hospital, Limoges, France
| | - Catherine Caly
- Gynecology-Obstetrics Department, Mère-Enfant Hospital, University Hospital Center, Limoges, France
| | - David Makowski
- INRAE, UMR MIA 518, INRA AgroParisTech Université Paris-Saclay, Paris, France
| | - Nouchine Hadjikhani
- Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA
- Gillberg Neuropsychiatry Center, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - Eric Lemonnier
- Autism Expert Center and Autism Resource Center of Limousin, University Hospital Center, Limoges, France
| | - Yehezkel Ben-Ari
- BABiomedical, Luminy Scientific Campus, Marseille, France.
- Neurochlore, Luminy Scientific Campus, Marseille, France.
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Huang Y, Li X, Zheng X, Tian X, Yu M, Sha L, Liu Y, Chong Y, Hao Y, You X. Bayesian network to predict hepatitis B surface antigen seroclearance in chronic hepatitis B patients. J Viral Hepat 2020; 27:1326-1337. [PMID: 32741055 DOI: 10.1111/jvh.13368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 07/10/2020] [Accepted: 07/19/2020] [Indexed: 12/09/2022]
Abstract
There is a need for an interpretable, accurate and interactions-considered model for predicting hepatitis B surface antigen (HBsAg) seroclearance. We aimed to construct a Bayesian network (BN) model using available medical records to predict HBsAg seroclearance in chronic hepatitis B (CHB) patients, and to evaluate the model's performance. This was a case-control study. A total of 1966 consecutive CHB patients (mean age 39.04 ± 11.23 years) between January 2006 and June 2015 were included. The demographic and clinical characteristics, laboratory data and imaging parameters were obtained and used to build a BN model to estimate the probability of HBsAg seroclearance. Baseline serum HBsAg and hepatitis Be antigen (HBeAg) levels, virological response and HBeAg seroclearance were the most significant predictors of HBsAg seroclearance. The post-test probability table showed that patients with baseline HBsAg concentrations ≤2000 IU/mL, negative baseline HBeAg, an initial virological response and without HBeAg seroclearance (i.e. no recurrence of HBeAg positivity during follow-up) were most likely to have HBsAg seroclearance. The constructed BN model had an area under the receiver operating characteristic curves of 0.896 (95% confidence interval [CI]: 0.892, 0.899), a sensitivity of 0.840 (95% CI: 0.833, 0.846), a specificity of 0.880 (95% CI: 0.876, 0.884) and an accuracy of 0.878 (95% CI: 0.874, 0.882) for predicting HBsAg seroclearance. The established BN model accurately estimated the probability of HBsAg seroclearance and is a promising tool to assist clinical decision-making.
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Affiliation(s)
- Yun Huang
- Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Xiangyong Li
- Department of Infectious Diseases, the Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaoyan Zheng
- Department of Infectious Diseases, the Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaolu Tian
- Department of Big Data, China Construction Bank Fintech Co. Ltd., Shenzhen, China
| | - Mingxue Yu
- Department of Infectious Diseases, the Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Liuping Sha
- Department of Infectious Diseases, the Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yinhui Liu
- Department of Infectious Diseases, People's Hospital of Foshan Sanshui Dictrict, Foshan, China
| | - Yutian Chong
- Department of Infectious Diseases, the Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuantao Hao
- Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Xu You
- Department of Clinical Laboratory, the Third Affiliated Hospital, Southern Medical University, Guangzhou, China
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Rybicka M, Bielawski KP. Recent Advances in Understanding, Diagnosing, and Treating Hepatitis B Virus Infection. Microorganisms 2020; 8:E1416. [PMID: 32942584 PMCID: PMC7565763 DOI: 10.3390/microorganisms8091416] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/08/2020] [Accepted: 09/11/2020] [Indexed: 02/06/2023] Open
Abstract
Chronic hepatitis B virus (HBV) infection affects 292 million people worldwide and is associated with a broad range of clinical manifestations including cirrhosis, liver failure, and hepatocellular carcinoma (HCC). Despite the availability of an effective vaccine HBV still causes nearly 900,000 deaths every year. Current treatment options keep HBV under control, but they do not offer a cure as they cannot completely clear HBV from infected hepatocytes. The recent development of reliable cell culture systems allowed for a better understanding of the host and viral mechanisms affecting HBV replication and persistence. Recent advances into the understanding of HBV biology, new potential diagnostic markers of hepatitis B infection, as well as novel antivirals targeting different steps in the HBV replication cycle are summarized in this review article.
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Affiliation(s)
- Magda Rybicka
- Department of Molecular Diagnostics, Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Abrahama 58, 80-307 Gdansk, Poland;
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Wong LP. The Guerilla Tactics of Hepatitis B Virus in Hemodialysis: Fighting a Stubborn Foe. Kidney Med 2019; 1:324-326. [PMID: 32734952 PMCID: PMC7380412 DOI: 10.1016/j.xkme.2019.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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