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From BERT to generative AI - Comparing encoder-only vs. large language models in a cohort of lung cancer patients for named entity recognition in unstructured medical reports

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Computers in biology and medicine 2025年 195卷

作者： Kamyar Arzideh Henning Schäfer Héctor Allende-Cid Giulia Baldini Thomas Hilser Ahmad Idrissi-Yaghir Katharina Laue Nilesh Chakraborty Niclas Doll Dario Antweiler Katrin Klug Niklas Beck Sven Giesselbach Christoph M Friedrich Felix Nensa Martin Schuler René Hosch Institute for Artificial Intelligence in Medicine University Hospital Essen Essen Germany Central IT Department Data Integration Center University Hospital Essen Essen Germany. Institute for Transfusion Medicine University Hospital Essen Essen Germany Department of Computer Science University of Applied Sciences and Arts Dortmund Dortmund Germany. Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) Sankt Augustin Germany. Institute for Artificial Intelligence in Medicine University Hospital Essen Essen Germany. West German Cancer Center Essen Department of Medical Oncology University Hospital Essen Essen Germany. Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Essen Germany. Department of Computer Science University of Applied Sciences and Arts Dortmund Dortmund Germany Institute for Medical Informatics Biometry and Epidemiology (IMIBE) University Hospital Essen Essen Germany. Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Essen Germany. Electronic address: rene.hosch@uk-essen.de.

BACKGROUND:Extracting clinical entities from unstructured medical documents is critical for improving clinical decision support and documentation workflows. This study examines the performance of various encoder and decoder models trained for Named Entity Recognition (NER) of clinical parameters in pathology and radiology reports, highlighting the applicability of Large Language Models (LLMs) for this task. METHODS:Three NER methods were evaluated: (1) flat NER using transformer-based models, (2) nested NER with a multi-task learning setup, and (3) instruction-based NER utilizing LLMs. A dataset of 2013 pathology reports and 413 radiology reports, annotated by medical students, was used for training and testing. RESULTS:The performance of encoder-based NER models (flat and nested) was superior to that of LLM-based approaches. The best-performing flat NER models achieved F1-scores of 0.87-0.88 on pathology reports and up to 0.78 on radiology reports, while nested NER models performed slightly lower. In contrast, multiple LLMs, despite achieving high precision, yielded significantly lower F1-scores (ranging from 0.18 to 0.30) due to poor recall. A contributing factor appears to be that these LLMs produce fewer but more accurate entities, suggesting they become overly conservative when generating outputs. CONCLUSION:LLMs in their current form are unsuitable for comprehensive entity extraction tasks in clinical domains, particularly when faced with a high number of entity types per document, though instructing them to return more entities in subsequent refinements may improve recall. Additionally, their computational overhead does not provide proportional performance gains. Encoder-based NER models, particularly those pre-trained on biomedical data, remain the preferred choice for extracting information from unstructured medical documents.

关键词： Large language models Machine learning Medical entity extraction Named entity recognition Natural language processing

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The warming effect of black carbon must be reassessed in light of observational constraints

Cell Reports Sustainability

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Cell Reports Sustainability 2025年

作者： Gunnar Myhre Bjørn H. Samset Camilla Weum Stjern Øivind Hodnebrog Ryan Kramer Chris Smith Timothy Andrews Olivier Boucher Greg Faluvegi Piers M. Forster Trond Iversen Alf Kirkevåg Dirk Olivié Drew Shindell Philip Stier Duncan Watson-Parris CICERO Center for International Climate Research Oslo 0318 Norway School of Earth and Environment University of Leeds Leeds UK NASA Goddard Space Flight Center Earth Sciences Division Greenbelt MD USA International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria Met Office Hadley Centre Exeter UK Institut Pierre-Simon Laplace Sorbonne Université/CNRS Paris France Center for Climate System Research Columbia University New York NY USA NASA Goddard Institute for Space Studies New York NY USA Norwegian Meteorological Institute Oslo Norway Nicholas School of the Environment Duke University Durham NC USA Department of Physics University of Oxford Atmospheric Oceanic and Planetary Physics Oxford UK Scripps Institution of Oceanography and Halicioğlu Data Science Institute University of California San Diego La Jolla CA USA

Anthropogenic emissions of black carbon (BC) aerosols are generally thought to warm the climate. However, the magnitude of this warming remains highly uncertain due to limited knowledge of BC sources; optical properties; and atmospheric processes such as transport, removal, and cloud interactions. Here, we assess and constrain estimates of the historical warming influence of BC using recent observations and emission inventories. Based on simulations from four climate models, we show that the current global mean surface temperature change from anthropogenic BC due to aerosol-radiation interaction spans a factor of three—from +0.02 ± 0.02 K to +0.06 ± 0.05 K. Rapid atmospheric adjustments reduce the instantaneous radiative forcing by nearly 50% (multi-model mean), substantially lowering the net warming. Yet, recent satellite constraints suggest a stronger effect, highlighting the need for a more comprehensive reassessment of BC’s climate influence.

关键词： black carbon climate response rapid adjustments clouds direct aerosol effect

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Optimising predictive models to prioritise viral discovery in zoonotic reservoirs (Jan, 10.1016/S2666-5247(21)00245-7, 2022)

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LANCET MICROBE 2022年第3期3卷 E170-E170页

作者： Becker, D. J. Albery, G. F. Sjodin, A. R. Department of Biology University of Oklahoma Norman OK USA Department of Biology Georgetown University Washington DC USA Department of Biological Sciences University of Idaho Moscow ID USA Université de Montréal Département de Sciences Biologiques Montréal QC Canada Institute of Biodiversity Animal Health and Comparative Medicine College of Medical Veterinary and Life Sciences University of Glasgow Glasgow UK Medical Research Centre University of Glasgow Centre for Virus Research Glasgow UK Center for Global Health Science and Security Georgetown University Medical Center Washington DC USA Icahn School of Medicine at Mount Sinai New York NY USA Department of Biological Sciences Louisiana State University Baton Rouge LA USA Department of Biology Pacific Lutheran University Tacoma WA USA Department of Microbiology Immunology and Pathology College of Veterinary Medicine and Biomedical Sciences Colorado State University Fort Collins CO USA Bat Health Foundation Fort Collins CO USA Department of Ecology & Evolutionary Biology University of Toronto Toronto ON Canada Department of Integrative Biology University of California Berkeley Berkeley CA USA Cary Institute of Ecosystem Studies Millbrook NY USA Department of Mammalogy Division of Vertebrate Zoology American Museum of Natural History New York NY USA Research Unit Knowledge-based Systems Department of Data Analysis and Mathematical Modelling Ghent University Belgium School of Biology and Environmental Science Science Centre West University College Dublin Dublin Ireland Department of Microbiology and Immunology Georgetown University Medical Center Washington DC USA

SummaryDespite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host–virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

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Mind your data: Privacy and legal matters in eHealth

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JMIR Formative Research 2021年第3期5卷 e17456页

作者： Zegers, Catharina M.L. Witteveen, Annemieke Schulte, Mieke H.J. Henrich, Julia F. Vermeij, Anouk Klever, Brigit Dekker, Andre Digital Society Health & Well-being The Hague Netherlands Institute of Data Science Maastricht University Maastricht Netherlands Department of Radiation Oncology (MAASTRO) GROW - School for Oncology and Development Biology Maastricht University Medical Centre Maastricht Netherlands Department of Biomedical Signals and Systems TechMed Centre University of Twente Enschede Netherlands Department of Clinical Neuro and Developmental Psychology Vrije Universiteit Amsterdam Netherlands Unit of Health Medical and Neuropsychology Institute of Psychology Leiden University Leiden Netherlands Department of Cognitive Neuropsychology Tilburg University Tilburg Netherlands University Medical Center University of Groningen Groningen Netherlands

The health care sector can benefit considerably from developments in digital technology. Consequently, eHealth applications are rapidly increasing in number and sophistication. For successful development and implementation of eHealth, it is paramount to guarantee the privacy and safety of patients and their collected data. At the same time, anonymized data that are collected through eHealth could be used in the development of innovative and personalized diagnostic, prognostic, and treatment tools. To address the needs of researchers, health care providers, and eHealth developers for more information and practical tools to handle privacy and legal matters in eHealth, the Dutch national Digital Society Research Programme organized the “Mind Your data: Privacy and Legal Matters in eHealth” conference. In this paper, we share the key take home messages from the conference based on the following five tradeoffs: (1) privacy versus independence, (2) informed consent versus convenience, (3) clinical research versus clinical routine data, (4) responsibility and standardization, and (5) privacy versus solidarity. © Catharina M L Zegers, Annemieke Witteveen, Mieke H J Schulte, Julia F Henrich, Anouk Vermeij, Brigit Klever, Andre Dekker.

关键词： data EHealth Privacy

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Perspectives for self-driving labs in synthetic biology

arXiv

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arXiv 2022年

作者： Martin, Hector Garcia Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean Arnold, Dan Hillson, Nathan Babnigg, Gyorgy Marti, Jose Manuel Mungall, Christopher J. Beckham, Gregg T. Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb Simmons, Blake A. Backman, Tyler Banerjee, Deepanwita Tanjore, Deepti Ramakrishnan, Lavanya Singh, Anup Lawrence Berkeley National Laboratory Biological Systems and Engineering Division BerkeleyCA United States Department of Energy Agile BioFoundry EmeryvilleCA United States Joint BioEnergy Institute EmeryvilleCA United States BCAM Basque Center for Applied Mathematics Bilbao Spain Lawrence Berkeley National Laboratory Scientific Data Division BerkeleyCA United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience BerkeleyCA United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division BerkeleyCA United States University of California Davis Department of Computer Science DavisCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Engineering Directorate Lawrence Livermore National Laboratory LivermoreCA United States Center for Bioengineering Lawrence Livermore National Laboratory LivermoreCA United States Department of Chemical Engineering Molecular Engineering & Sciences Institute Center for Synthetic Biology University of Washington SeattleWA United States Biosciences Division Argonne National Laboratory ArgonneIL United States Advanced Biofuels and Bioproducts Process Development Unit Lawrence Berkeley National Laboratory BerkeleyCA United States Biomaterials and Biomanufacturing Division Sandia National Laboratories LivermoreCA United States Earth and Biological Sciences Division Pacific Northwest National Laboratories RichlandWA United States Resources and Enabling Sciences Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Bioengineering University of California BerkeleyCA United States

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. © 2022, CC BY.

关键词： Synthetic biology

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An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

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Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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P02.18.B INTEGRATIVE OMICS ANALYSIS OF IDH1 MUTANT GLIOMA PATIENTS REVEALS ALTERATIONS IN BUTYRATE METABOLISM

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii33-ii34页

作者： Fritah, S Cano-Galiano, A Toth, R Hau, A Martinez-Garcia, E Allega, M Sumpton, D Fack, F Dieterle, M Adiamah, M Gobin, M Nicot, N Muller, A Kaoma, T Lesur, A Mittelbronn, M van Dyck, E Prasad Bekal, M Esquenazi, Y Hiller, K Sakariassen, P Miletic, H Dittmar, G Nazarov, P Tardito, S Lund-Johansen, M Bjerkvig, R Golebiewska, A Niclou, S NORLUX Neuro-Oncology Laboratory Department of Cancer Research Luxembourg Institute of Health Luxembourg Luxembourg Multiomics Data Science Department of Cancer Research Luxembourg Institute of Health Luxembourg Luxembourg Quantitative Biology Unit Luxembourg Institute of Health Luxembourg Luxembourg Cancer Research UK Beatson Institute Glasgow & Institute of Cancer Sciences University of Glasgow Glasgow United Kingdom National Center of Genetics Laboratoire National de Santé Dudelange Luxembourg Vivian L. Smith Department of Neurosurgery University of Texas Health Science Center at Houston Huston TX United States Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Belval Luxembourg Department of Biomedicine University of Bergen Bergen Norway Haukeland University Hospital Bergen Norway

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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JS08.6.A A connectivity signature for glioblastoma

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Neuro-Oncology 2022年第SUPPLEMENT_2期24卷 ii8-ii9页

作者： Hoffmann, D C Hai, L Wagener, R Mandelbaum, H Xie, R Hausmann, D Dominguez Azorín, D Weil, S Sievers, P Cebulla, G Ito, J Warnken, U Venkataramani, V Ernst, K Reibold, D Will, R Suvà, M L Herold-Mende, C Sahm, F Winkler, F Schlesner, M Wick, W Kessler, T Clinical Cooperation Unit Neurooncology German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Neurology and Neurooncology Program National Center for Tumor Diseases Heidelberg University Hospital Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Bioinformatics and Omics Data Analytics DKFZ Heidelberg Germany Department of Neuropathology Institute of Pathology University Hospital Heidelberg Heidelberg Germany Clinical Cooperation Unit Neuropathology DKTK DKFZ Heidelberg Germany Department of Neuroanatomy Institute for Anatomy and Cell Biology Heidelberg University Heidelberg Germany Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology DKTK DKFZ Heidelberg Germany Genomics and Proteomics Core Facility DKTK DKFZ Heidelberg Germany Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology and Center for Cancer Research Massachusetts General Hospital and Harvard Medical School Boston MA United States Department of Neurosurgery Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

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Association of sleep problems with suicidal behaviors and healthcare utilization in adults with chronic diseases: the role of mental illness

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Annals of general psychiatry 2025年第1期24卷 40页

作者： Chen Wen Zheng Wei Nasr Chalghaf Woldegebriel Assefa Woldegerima Jianhong Wu Nicola Luigi Bragazzi Sergio Garbarino Department of Child Psychology The Children's Hospital National Clinical Research Center for Child Health Zhejiang University School of Medicine National Children's Regional Medical Center Hangzhou Zhejiang China. Department of Psychiatry Xiamen Xianyue Hospital Xiamen China. High Institute of Sport and Physical Education of Gafsa University of Gafsa Gafsa Tunisia. Laboratory for Industrial and Applied Mathematics (LIAM) Department of Mathematics and Statistics York University Toronto Canada. Laboratory for Industrial and Applied Mathematics (LIAM) Department of Mathematics and Statistics York University Toronto Canada. robertobragazzi@***. Department of Neuroscience Rehabilitation Ophthalmology Genetics Maternal and Child Health (DINOGMI) University of Genoa Genoa Italy. robertobragazzi@***. Human Nutrition Unit (HNU) Department of Food and Drugs Medical School University of Parma Parma Italy. robertobragazzi@***. Department of Computer Science Data Science and Information Technology Sol Plaatje University Kimberly South Africa. robertobragazzi@***. Department of Neuroscience Rehabilitation Ophthalmology Genetics Maternal and Child Health (DINOGMI) University of Genoa Genoa Italy.

OBJECTIVES:This study aimed to investigate the association between sleep problems and suicidal behaviors as well as healthcare utilization in Canadian adults with chronic diseases, while also examining the mediating role of mental illness. METHODS:data were drawn from the 2015-16 cycle of the Canadian Community Health Survey, specifically from Ontario, Manitoba, and Saskatchewan - the provinces that included the optional sleep module. A total of 22,700 participants aged ≥ 18 years and diagnosed with at least one chronic disease were included in the analysis. Sleep problems were defined as extreme sleep durations (either < 5 or ≥ 10 h) and insomnia. Mental illness was classified as a self-reported mood or anxiety disorder. RESULTS:Participants with extreme sleep durations (compared to 7 to < 8 h) and those with insomnia (compared to no insomnia) showed a higher prevalence of suicidal ideation, suicidal plans, and increased healthcare utilization. After adjusting for multiple covariates, both extreme sleep durations and insomnia remained significantly associated with increased odds of suicidal ideation, suicidal plans, and healthcare utilization. Mediation analyses indicated that mental illness partially mediated these associations. CONCLUSIONS:Both extreme sleep durations and insomnia were independently associated with higher odds of suicidal behaviors and increased healthcare utilization in adults with chronic diseases, with mental illness playing a partial mediating role in these relationships.

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