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arXiv 2022年

作者： Mahar, Robert K. Lee, Katherine J. Chakraborty, Bibhas Salim, Agus Simpson, Julie A. Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health Faculty of Medicine Dentistry and Health Sciences University of Melbourne ParkvilleVIC Australia Clinical Epidemiology and Biostatistics Unit Murdoch Children’s Research Institute ParkvilleVIC Australia Department of Paediatrics Melbourne Medical School University of Melbourne ParkvilleVIC Australia Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Statistics Faculty of Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States School of Mathematics and Statistics Faculty of Science University of Melbourne ParkvilleVIC Australia Baker Department of Cardiometabolic Health Melbourne Medical School Faculty of Medicine Dentistry and Health Sciences University of Melbourne ParkvilleVIC Australia

The optimal prophylaxis, and treatment if the prophylaxis fails, for a disease may be best evaluated using a sequential multiple assignment randomised trial (SMART). A SMART is a multi-stage study that randomises a participant to an initial treatment, observes some response to that treatment and then, depending on their observed response, randomises the same participant to an alternative treatment. Response adaptive randomisation may, in some settings, improve the trial participants’ outcomes and expedite trial conclusions, compared to fixed randomisation. But ‘myopic’ response adaptive randomisation strategies, blind to multistage dynamics, may also result in suboptimal treatment assignments. We propose a ‘dynamic’ response adaptive randomisation strategy based on Q-learning, an approximate dynamic programming algorithm. Q-learning uses stage-wise statistical models and backward induction to incorporate late-stage ‘payoffs’ (i.e. clinical outcomes) into early-stage ‘actions’ (i.e. treatments). Our real-world example consists of a COVID-19 prophylaxis and treatment SMART with qualitatively different binary endpoints at each stage. Standard Q-learning does not work with such data because it cannot be used for sequences of binary endpoints. Sequences of qualitatively distinct endpoints may also require different weightings to ensure that the design guides participants to regimens with the highest utility. We describe how a simple decision-theoretic extension to Q-learning can be used to handle sequential binary endpoints with distinct utilities. Using simulation we show that, under a set of binary utilities, the ‘dynamic’ approach increases expected participant utility compared to the fixed approach, sometimes markedly, for all model parameters, whereas the ‘myopic’ approach can actually decrease utility. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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An adverse lipoprotein phenotype—hypertriglyceridaemic hyperapolipoprotein B—and the long-term risk of type 2 diabetes: a prospective, longitudinal, observational cohort study

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The Lancet Healthy Longevity 2022年第5期3卷 e339-e346页

作者： Pencina, Karol M Pencina, Michael J Dufresne, Line Holmes, Michael Thanassoulis, George Sniderman, Allan D Section on Men's Health Aging and Metabolism Brigham and Women's Hospital Harvard Medical School Boston MA United States Duke University School of Medicine Biostatistics and Bioinformatics Duke Clinical Research Institute Durham NC United States Mike and Valeria Rosenbloom Centre for Cardiovascular Prevention Department of Medicine McGill University Health Centre Montreal QC Canada MRC Population Health Research Unit at the University of Oxford UK Oxford United Kingdom Clinical Trial Service Unit and Epidemiological Studies Unit Nuffield Department of Population Health University of Oxford UK Oxford United Kingdom

Background: This study examines the risk of new-onset diabetes in patients with hypertriglyceridaemic hyperapolipoprotein B (high triglycerides, high apolipoprotein B [apoB], low LDL cholesterol to apoB ratio, and low HDL cholesterol). The aim was to establish whether this lipoprotein phenotype identified a substantial group at high risk of developing diabetes over the next 20 years. Methods: In this prospective, longitudinal, observational cohort study, we used data from the Framingham Offspring cohort (recruited in Framingham, MA, USA). Participants were aged 40–69 years and free of diabetes and cardiovascular disease at a baseline examination done between April, 1987, and November, 1991, and were followed up until March, 2014. Cox proportional hazards regression with hierarchical adjustment for age and sex, waist circumference, and fasting blood glucose were used to model the relationship between each lipid marker and incident diabetes, as well as the relationship between hypertriglyceridaemic hyperapoB (defined as values greater than sample medians of triglycerides and apoB, and less than medians of HDL cholesterol and LDL cholesterol to apoB ratio) and incident diabetes. Findings: Of 3446 individuals aged 40–69 years who completed baseline examination, 2515 participants were eligible and included in all analyses. During median 21·1 years (IQR 11·1–23·1) of follow-up, 402 (16·0%) individuals developed diabetes. Age (p=0·032), waist circumference (p<0·0001), fasting blood glucose (p<0·0001), and natural logarithm-transformed triglycerides (p<0·0001) were associated with new-onset diabetes, as were apoB (p=0·0016), LDL cholesterol to apoB ratio (p=0·0018), and HDL cholesterol (p=0·0016) when added to this model. The age and sex-adjusted incidence of diabetes in the hypertriglyceridaemic hyperapoB group was 32·4% (95% CI 27·8–37·7) versus 5·5% (3·5–8·6) in the optimal lipid phenotype group and 15·5% (13·5–17·7) in the mixed lipid phenotype group. The fully adjusted

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The Nature and Scope of Reported Child Maltreatment in Euro-CAN Countries: Current Evidence and Future Opportunities

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International Journal on Child Maltreatment: Research, Policy and Practice 2024年第3期7卷 387-408页

作者： Jud, Andreas Neelakantan, Lakshmi Rajter, Miroslav Græsholt-Knudsen, Troels Witt, Andreas Ntinapogias, Athanasios Quantin, Catherine Korhonen, Laura Roth, Maria Daniunaite, Ieva Bettencourt Rodrigues, Leonor Whelan, Sadhbh Włodarczyk, Joanna Otterman, Gabriel Child and Adolescent Psychiatry/Psychotherapy Ulm University Clinics Ulm Germany School of Social Work Zurich University of Applied Sciences Zurich Switzerland German Center for Mental Health (DZPG) partner site Ulm Ulm Germany Population Mental Health Unit Centre for Mental Health and Community Wellbeing School of Population and Global Health University of Melbourne Melbourne Australia University of Zagreb Zagreb Croatia Mindhood Department of Public Health Aarhus University Aarhus Denmark Department of Forensic Medicine Aarhus University Aarhus Denmark University Hospital of Child and Adolescent Psychiatry and Psychotherapy University of Bern Bern Switzerland Department of Mental Health and Social Welfare Institute of Child Health Athens Greece Biostatistics and Bioinformatics (DIM) University Hospital CHU Dijon Bourgogne INSERM Université de Bourgogne Dijon France Université Paris-Saclay Villejuif France Barnafrid Centre Department of Biomedical and Clinical Sciences Linköping University Linköping Sweden Babes-Bolyai University Cluj-Napoca-Napoca Romania Center for Psychotraumatology Institute of Psychology Vilnius University Vilnius Lithuania ProChild CoLAB Against Child Poverty and Social Exclusion (supported by FCT and NORTE-06-3559-FSE-000044) Guimaraes Braga Portugal Department of Children Equality Disability Integration and Youth Dublin Ireland We Empower Children Foundation University of Warsaw Warsaw Poland

Most European Cooperation on Science and Technology (COST) affiliated countries aim to advance the goal of protecting children from maltreatment. However, despite the increasing numbers of population-based surveys, the development of administrative data systems has lagged. In this study, we aimed to examine the current state of development of administrative data systems in a sample of countries represented in the COST Action 19106 network, Multi-Sectoral Responses to Child Abuse and Neglect in Europe: Incidence and Trends (Euro-CAN). A structured questionnaire was distributed to researchers and professionals within Euro-CAN-affiliated countries, which captured economic, legislative, systemic, and data infrastructure characteristics. Thematic trends for 13 sampled countries were presented descriptively. The implementation of legislative measures such as banning corporal punishment varied substantially, with some countries decades apart. Almost all sampled countries mandate reports of suspected child maltreatment for all or some professionals in contact with children. In most countries, public child protection, health, or law enforcement systems are decentralized, and unsubstantiated/inconclusive incidents of suspected child maltreatment are not systematically collected at the national level. Child maltreatment data is not routinely collected in health sectors in all sampled countries. Where data is collected in different sectors, such as police and child protection agencies, different descriptions are often used. Systematic data linkage remains a seldom occurrence with only a few countries offering this capability. The call for Euro-CAN countries to develop multi-sectoral data systems to capture recorded instances of child maltreatment remains relevant. © The Author(s) 2024.

关键词： Administrative data Child maltreatment Child protection Euro-CAN countries Surveillance

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An RF-CNN pipeline for predicting PM 2.5 concentration in Sri Lanka

Journal of Hazardous Materials Advances

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Journal of Hazardous Materials Advances 2025年 19卷

作者： Gimhan Attanayake Mahesh Senarathna Mike Bergin David Carlson Prakash V Bhave Gayan Bowatte Nalin Harischandra Department of Basic Sciences Faculty of Allied Health Sciences University of Peradeniya Peradeniya 20400 Sri Lanka Department of Civil and Environmental Engineering Duke University Durham NC 27708 USA Department of Biostatistics and Bioinformatics Duke University Durham NC 27708 USA Allergy and Lung Health Unit Centre for Epidemiology and Biostatistics School of Population and Global Health University of Melbourne Melbourne Victoria 3053 Australia Department of Electrical & Electronic Engineering Faculty of Engineering University of Peradeniya Peradeniya 20400 Sri Lanka

Air pollution is a considerable global public health threat, requiring efficient monitoring and forecasting to guide decision-making. This study introduces a cascaded model of enhanced Random Forest with Convolutional Neural Network (RF-CNN) that predicts spatiotemporal fluctuations in PM 2.5 concentrations throughout Sri Lanka. The K-Nearest Neighbors method is employed to impute missing data, and the model utilizes data from 24 low-cost PM 2.5 sensors that are distributed throughout the country. The Convolutional Neural Network (CNN) derives spatial features from four-band PlanetScope satellite images (3m/pixel resolution, 1km 2 spatial coverage), while the Random Forest (RF) component models the relationship between PM 2.5 levels and four meteorological parameters. These features, combined with meteorological, spatial, and temporal inputs, produce the final forecasting results. The dataset comprises 1934 satellite images that were collected between December 2022 and February 2024, with an average PM 2.5 concentration of approximately 15 μg/m 3 . The RF-CNN model exhibited robust performance metrics across a variety of climate zones, including a normalized root mean square error of approximately 32.4 %, a mean absolute percentage error of approximately 25.7 %, a normalized mean absolute error of approximately 22.8 %, a Spearman r of 0.871, and a Pearson r of 0.873. Two metrics: Input Data Quality Score (IDQS) and Testing Data Quality Score (TDQS) were implemented to evaluate the effects of imputation. Performance was minimally impacted by imputation within acceptable ranges, while exceeding limits resulted in increased uncertainty. This research emphasizes the efficacy of the RF-CNN approach, which integrates satellite imagery and low-cost sensor data, as a scalable solution for predicting spatiotemporal PM 2.5 variations. It provides valuable insights for regions that lack extensive monitoring.

关键词： Fine particular matters Spatiotemporal variations Random Forest Convolutional Neural Network K-nearest neighbor imputation

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6- YEAR RESULTS FROM A PROSPECTIVE PHASE II TRIAL OF TEN-FRACTION HYPOFRACTIONATED RADIOTHERAPY IN LOCALLY ADVANCED BREAST CANCER

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Practical radiation oncology 2025年

作者： P Pinnarò S Takanen L Marucci V Landoni A Soriani C Botti P Vici F Sperati D Giannarelli G Sanguineti Radiation Oncology Unit IRCCS Regina Elena National Cancer Institute Rome Italy. Radiation Oncology Unit IRCCS Regina Elena National Cancer Institute Rome Italy. Electronic address: silvia.takanen@ifo.it. Medical Physics Unit IRCCS Regina Elena National Cancer Institute Rome Italy. Division of Breast Surgery IRCCS Regina Elena National Cancer Institute Rome Italy. Phase IV Clinical Studies Unit IRCCS Regina Elena National Cancer Institute Rome Italy. UOSD Clinical Trial Center Biostatistics and Bioinformatics San Gallicano Dermatological Institute IRCCS Rome Italy. Biostatistics Unit Scientific Directorate Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy.

PURPOSE We report the 6-year results of a phase II study on hypofractionated radiotherapy (HFRT) targeting the primary and regional lymph nodes in ten fractions (fxs). MATERIALS AND METHODS A schedule of 34 Gy/10 fxs/2 wks to the whole breast/chest wall and to the draining lymph nodes was used. Both acute and late toxicities were collected. All pts but those who underwent mastectomy without reconstruction or with temporary expander were asked to rate their cosmetic outcome according to the Harvard scale. Toxicity was assessed weekly during RT and then at each follow-up (fup) examination. Cancer related endpoints were evaluated from the date of RT start to the diagnosis of local relapse/distant metastases or the last fup respectively. RESULTS From February 2015 to March 2019, 59 women (median age 60 yrs, IQR: 48.3-68.8 yrs) with stage II to IIIA breast cancer who underwent axillary dissection and conservative surgery (83%) or mastectomy (17%) were accrued. One patient was lost to fup immediately after the end of RT. At the median fup of 77.11 months (range: 24-102 months), the cumulative incidence of any grade loco-regional late toxicity estimated with the Kaplan-Meier method is 43.4% (95%CI) (30.0% and 46.1% for patients undergone mastectomy and lumpectomy, respectively). Peak- 2 events have been observed for fibrosis (1 pt, 1.7%), telangiectasia (1 pts, 1.7%) and lymphoedema (1 patient, 1.7%). One patient (1.7%) experienced grade 3 breast retraction at 36 month fup. The cosmetic outcome resulted to be excellent, good, fair and poor in 61.7%, 25%, 7.6% and 5.7%, respectively. At 72 months the specific-disease free survival was 96.5%; distant metastasis-free survival (DMFS) and OS rates were 88% and 94.4% respectively. CONCLUSIONS Our results support the activity of a 10-fxs Hypo-RT schedule targeting the primary site as well as the draining lymph node stations after surgery for locally advanced BC.

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Evaluation Of Sexual Function, Menopausal Symptoms And Quality Of Life In Women Undergoing Surgery For Endometrial And Cervical Cancer Vs Spontaneous Postmenopausal Controls: A Prospective Cohort Study

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International Journal of Gynecological Cancer 2025年第2期35卷

作者： Alessandra Logoteta Valentina Bruno Irene Terrenato Emanuela Mancini Cristina Vincenzoni Ermelinda Baiocco Giuseppe Cutillo Rosanna Mancari Delia Savone Cosimo Oliva Emmanuele Angelo Francesco Jannini Enrico Vizza Department of Biomedicine and Prevention Biotechnologies and Translational Medicine University of Rome Tor Vergata Rome Italy Obstetrics and Gynecology Unit San Camillo de Lellis Hospital Rieti Italy Department of Experimental Clinical Oncology Gynecologic Oncology Unit IRCCS Regina Elena National Cancer Institute Rome Italy Clinical Trial Center and Biostatistics and Bioinformatics Unit IRCCS Regina Elena National Cancer Institute Rome Italy Department of Systems Medicine Endocrinology and Medical Sexology (ENDOSEX) University of Rome Tor Vergata Rome Italy

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Learning and teaching biological data science in the Bioconductor community

arXiv

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arXiv 2024年

作者： Drnevich, Jenny Tan, Frederick J. Almeida-Silva, Fabricio Castelo, Robert Culhane, Aedin C. Davis, Sean Doyle, Maria A. Geistlinger, Ludwig Ghazi, Andrew R. Holmes, Susan Lahti, Leo Mahmoud, Alexandru Nishida, Kozo Ramos, Marcel Rue-Albrecht, Kevin Shih, David J.H. Gatto, Laurent Soneson, Charlotte Roy J. Carver Biotechnology Center University of Illinois Urbana-Champaign IL United States Johns Hopkins University Department of Biology BaltimoreMD United States Department of Plant Biotechnology and Bioinformatics Ghent University Ghent Belgium VIB Center for Plant Systems Biology Ghent Belgium Department of Medicine and Life Sciences Universitat Pompeu Fabra Barcelona Spain Limerick Digital Cancer Research Centre School of Medicine University of Limerick Ireland University of Colorado Anschutz School of Medicine DenverCO United States Core for Computational Biomedicine Department of Biomedical Informatics Harvard Medical School BostonMA United States Statistics Department Stanford University StanfordCA United States Department of Computing University of Turku Finland Channing Division of Network Medicine Harvard Medical School BostonMA United States RIKEN Center for Biosystems Dynamics Research 6-7-1 Minatojima Minamimachi Chuoku Hyogo Kobe Japan Department of Epidemiology and Biostatistics City University of New York School of Public Health New YorkNY United States MRC WIMM Centre for Computational Biology MRC Weatherall Institute of Molecular Medicine University of Oxford Oxford United Kingdom School of Biomedical Sciences Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Computational Biology and Bioinformatics Unit de Duve Institute UCLouvain Brussels Belgium Friedrich Miescher Institute for Biomedical Research Basel Switzerland SIB Swiss Institute of Bioinformatics Basel Switzerland

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the *** Codes 97K80 © 2024, CC BY.

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1912P AI-based early prediction of radiation pneumonitis in stage III NSCLC patients

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Annals of Oncology 2024年 35卷 S1114-S1114页

作者： S. Bove M.C. Comes A. Fanizzi V. Gregorc A. Catino D. Galetta M. Montrone R. Massafra Biostatistics and Bioinformatics Lab IRCCS Istituto Tumori Giovanni Paolo II Bari Italy Oncology Dept. IRCCS Ospedale San Raffaele Milan Italy Thoracic Oncology Department Istituto Tumori Giovanni Paolo II Bari Italy Medical Thoracic Oncology Unit Istituto Tumori Bari Giovanni Paolo II - IRCCS Bari Italy Medical Area Department Thoracic Oncology Unit Istituto Tumori Bari Giovanni Paolo II - IRCCS Bari Italy

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Surgeons’ opinions about Enhanced Recovery after Surgery (ERAS) for Retroperitoneal Sarcoma (RPS): a survey of the ESSO European School of Soft Tissue Sarcoma Surgery (ESSTSS)

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European Journal of Surgical Oncology 2023年第2期49卷 e56-e56页

作者： Improta, Luca Ciniselli, Chiara Maura Verderio, Paolo Pasquali, Sandro Fiore, Marco Valeri, Sergio Fondazione Policlinico Universitario Campus Bio-Medico Department of Surgery - Sarcoma Surgery Rome Italy Fondazione IRCSS Istituto Nazionale dei Tumori Department of Applied Research and Technological Development - Unit of Bioinformatics and Biostatistics Milan Italy Fondazione IRCSS Istituto Nazionale dei Tumori Department of Surgery - Sarcoma Service Milan Italy

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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