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Developmental exposure of California mice to endocrine disrupting chemicals and potential effects on the microbiome-gut-brain axis at adulthood (vol 10, 10902, 2020)

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SCIENTIFIC REPORTS 2020年第1期10卷 1-17页

作者： Kaur, Sarabjit Sarma, Saurav J. Marshall, Brittney L. Liu, Yang Kinkade, Jessica A. Bellamy, Madison M. Mao, Jiude Helferich, William G. Schenk, A. Katrin Bivens, Nathan J. Lei, Zhentian Sumner, Lloyd W. Bowden, John A. Koelmel, Jeremy P. Joshi, Trupti Rosenfeld, Cheryl S. Christopher S Bond Life Sciences Center University of Missouri Columbia USA Biomedical Sciences University of Missouri Columbia USA MU Metabolomics Center University of Missouri Columbia USA MU Institute of Data Science and Informatics University of Missouri Columbia USA Food Science and Human Nutrition University of Illinois Urbana USA Physics Randolph College Lynchburg USA DNA Core Facility University of Missouri Columbia USA Department of Biochemistry University of Missouri Columbia USA Department of Physiological Sciences College of Veterinary Medicine University of Florida Gainesville USA Center for Environmental and Human Toxicology College of Veterinary Medicine University of Florida Gainesville USA Environmental Health Sciences Yale University New Haven USA Department of Health Management and Informatics School of Medicine University of Missouri Columbia USA Thompson Center for Autism and Neurobehavioral Disorders University of Missouri Columbia USA Genetics Area Program University of Missouri Columbia USA

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

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South African Journal of Chemical Engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

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Through the Lens of Patient Partners: Challenges in Accrual of Older Adults to NCI Clinical Trials

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Journal of the National Cancer Institute - Monographs 2022年第60期2022卷 125-134页

作者： BrintzenhofeSzoc, Karlynn Canin, Beverly Casas-Silva, Esmeralda Denicoff, Andrea Braun-Inglis, Christa Okado, Izumi Bakos, Alexis Oncology Social Work Kent School of Social Work University of Louisville Louisville KY United States SCOREboard Patient Advocate Board The Cancer and Aging Research Group United States Center for Biomedical Informatics and Information Technology Informatics and Data Science Program National Institutes of Health National Cancer Institute Rockville MD United States Division of Cancer Treatment and Diagnosis Cancer Therapy and Evaluation Program National Institutes of Health National Cancer Institute Rockville MD United States Clinical Faculty UH Nancy Atmospera-Walch School of Nursing University of Hawaii Cancer Center Hawaii M U NCORP Honolulu HI United States University of HawaiiCancer Center Honolulu HI United States Division of Cancer Prevention Community Oncology and Prevention Trials Research Group National Institutes of Health National Cancer Institute Rockville MD United States

The workshop “Engaging Older Adults in Cancer Clinical Trials Conducted in the NCI Clinical Trials Network: Challenges and Opportunities” included a Patient Stakeholder Workgroup that explored the needs and concerns of older adults with cancer regarding clinical trials. To accomplish this, the workgroup conducted patient focus groups in which participants were interviewed, recorded conversations were analyzed and coded, and salient themes were identified. The focus groups identified general barriers to accrual such as complex consent forms, general communication, restrictive eligibility, nonreferrals, patient costs, cultural insensitivity, limited accessibility in community settings, and transportation issues. They also identified the influence of knowledgeable information presenters, improved care, family or caregiver support, and the desire to help others as drivers or reasons to participate in clinical trials. The workshop concluded that multi-level interventions could be used to increase the accrual of older adults to National Cancer Institute clinical trials as well as others. © The Author(s) 2022. Published by Oxford University Press. All rights reserved.

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Can OpenEHR, ISO 13606 and HL7 FHIR work together? An agnostic perspective for the selection and application of EHR standards from Spain

TechRxiv

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TechRxiv 2022年

作者： Pedrera-Jiménez, Miguel Kalra, Dipak Beale, Thomas Carrero, Adolfo Muñoz Balazote, Pablo Serrano Barrio, Noelia García Cano, David Moner Frid, Santiago Tomás, Diego Boscá Segura, Jose Alberto Maldonado Rubí, Raimundo Lozano Data Science Unit Hospital Universitario 12 de Octubre Av. de Córdoba s/n Madrid28041 Spain Data Science Unit Hospital Universitario 12 de Octubre Madrid Spain Veratech for Health Valencia Spain Unit of Medical Informatics Hospital Clinic de Barcelona Barcelona Spain Ars Semantica UK. Specifications Program OpenEHR International United Kingdom Madrid Spain

Due to the heterogeneity of Electronic Health Record (EHR) standards, the decision-making teams, who are not experts in health information, express confusion for selecting and applying these resources in their data platforms. For this reason, a group of experts has analyzed strengths and weaknesses about design, modeling capabilities, flexibility and resources implemented of three relevant standards based on Detailed Clinical Models: OpenEHR, ISO 13606 and HL7 FHIR. Thus, it was concluded that: (1) they are useful for the purposes for which they have been designed and show shortcomings in those for which they have not;(2) they are functionally compatible in health data platforms and methodologies developed in a standards-agnostic perspective;and (3) they are conceptually and technically compatible with each other, so the choice of one or the other does not have a high impact as long as one starts from the one richer in modeling capabilities and flexibility. © 2022, CC BY-NC-SA.

关键词： Decision making

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Deep learning-based computational pathology predicts origins for cancers of unknown primary

arXiv

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arXiv 2020年

作者： Lu, Ming Y. Zhao, Melissa Shady, Maha Lipkova, Jana Chen, Tiffany Y. Williamson, Drew F.K. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States

Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined1,2. This poses a significant challenge, since modern therapeutics such as chemotherapy regimen and immune checkpoint inhibitors are specific to the primary tumor3. Patients with a CUP diagnosis routinely undergo an extensive diagnostic work-up of pathology, radiology, endoscopy, laboratory tests, and clinical correlation in an attempt to determine the primary origin. Such exploration is not only time and resource consuming, but it might significantly delay administration of the suitable treatment. Despite extensive diagnostic work-ups the primary may never be determined in many cases. Recent work has focused on using genomics and transcriptomics for identification of tumor origins4–6. However, genomic testing is not conducted for every patient and lacks clinical penetration in low resource settings. Herein, to overcome these challenges, we present a deep learning-based computational pathology algorithm-TOAD-that can provide a differential diagnosis for CUP using routinely acquired histology slides. We used 17,486 gigapixel whole slide images with known primaries spread over 18 common origins to train a multi-task deep model to simultaneously identify the tumor as primary or metastatic and predict its site of origin. We tested our model on an internal test set of 4,932 cases with known primaries and achieved a top-1 accuracy of 0.84, a top-3 accuracy of 0.94 while on our external test set of 662 cases from 202 different hospitals, it achieved a top-1 and top-3 accuracy of 0.79 and 0.93 respectively. We further curated a dataset of 717 CUP cases from 151 different medical centers and identified a subset of 290 cases for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 50% of cases (κ=0.4 when adjusted for agreement by chance) and a top-3 agreement of 75%. Our proposed method can be used

关键词： Diseases

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A deep learning approach to Lung Nodule Growth Prediction using CT image combined with Demographic and image features 23

A deep learning approach to Lung Nodule Growth Prediction us...

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7th International Conference on Medical and Health informatics, ICMHI 2023

作者： Li, Ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting

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Heightened COVID-19 Mortality in People With Severe Mental Illness Persists After Vaccination: A Cohort Study of Greater Manchester Residents (vol 49, pg 275, 2023)

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SCHIZOPHRENIA BULLETIN 2023年第5期49卷 1399-1403页

作者： Hassan, Lamiece Sawyer, Chelsea Peek, Niels Lovell, Karina Carvalho, Andre F. Solmi, Marco Tilston, George Sperrin, Matthew Firth, Joseph Division of Psychology and Mental Health The University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK Centre for Health Informatics Division of Informatics Imaging and Data Sciences The University of Manchester M13 9PL UK NIHR Greater Manchester Patient Safety Translational Research Centre The University of Manchester Manchester UK Manchester Academic Health Science Centre National Institute for Health Research Manchester Biomedical Research Centre The University of Manchester Manchester M13 9PL UK Division of Nursing Midwifery and Social Work University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre School of Medicine Barwon Health Deakin University Geelong Victoria Australia Psychiatry Department University of Ottawa Ottawa ON Canada The Ottawa Hospital University of Ottawa Ottawa ON Canada Clinical Epidemiology Program Ottawa Hospital Research Institute (OHRI) University of Ottawa Ottawa ON Canada Greater Manchester Mental Health NHS Foundation Trust Manchester Academic Health Science Centre Manchester M13 9PL UK

Background and hypothesis Previous studies show that people with severe mental illness (SMI) are at higher risk of COVID-19 mortality, however limited evidence exists regarding risk post-vaccination. We investigated COVID-19 mortality among people with schizophrenia and other SMIs before, during and after the UK vaccine roll-out. Study Design Using the Greater Manchester (GM) Care Record to access routinely collected health data linked with death records, we plotted COVID-19 mortality rates over time in GM residents with schizophrenia/psychosis, bipolar disorder (BD) and/or recurrent major depressive disorder (MDD) from February 2020 to September 2021. Multivariable logistic regression was used to compare mortality risk (risk ratios;RRs) between people with SMI (N=190,188) and age-sex matched controls (N=760,752), adjusted for sociodemographic factors, pre-existing comorbidities and vaccination *** Results Mortality risks were significantly higher among people with SMI compared with matched controls, particularly among people with schizophrenia/psychosis (RR 3.14, CI 2.66-3.71) and/or BD (RR 3.17, CI 2.15-4.67). In adjusted models, the relative risk of COVID-19 mortality decreased, though remained significantly higher than matched controls for people with schizophrenia (RR 1.53, CI 1.24-1.88) and BD (RR 2.28, CI 1.49-3.49), but not recurrent MDD (RR 0.92, CI 0.78-1.09). People with SMI continued to show higher mortality rate ratios relative to controls throughout 2021, during vaccination roll-out. Conclusions People with SMI, notably schizophrenia and bipolar disorder, were at greater risk of COVID-19 mortality compared to matched controls. Despite population vaccination efforts that have prioritised people with SMI, disparities still remain in COVID-19 mortality for people with SMI.

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Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare data Representation Learning

arXiv

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arXiv 2024年

作者： Huang, Chun-Kai Hsieh, Yi-Hsien Chien, Ta-Jung Chien, Li-Cheng Sun, Shao-Hua Su, Tung-Hung Kao, Jia-Horng Lin, Che Taiwan Data Science Degree Program NTU Taiwan Department of Electrical Engineering NTU Taiwan Division of Gastroenterology and Hepatology NTU Hospital Taiwan Hepatitis Research Center NTU Hospital Taiwan Graduate Institute of Clinical Medicine College of Medicine NTU Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

Multivariate time series (MTS) data, when sampled irregularly and asynchronously, often present extensive missing values. Conventional methodologies for MTS analysis tend to rely on temporal embeddings based on timestamps that necessitate subsequent imputations, yet these imputed values frequently deviate substantially from their actual counterparts, thereby compromising prediction accuracy. Furthermore, these methods typically fail to provide robust initial embeddings for values infrequently observed or even absent within the training set, posing significant challenges to model generalizability. In response to these challenges, we propose SCAlable Numerical Embedding (SCANE), a novel framework that treats each feature value as an independent token, effectively bypassing the need for imputation. SCANE regularizes the traits of distinct feature embeddings and enhances representational learning through a scalable embedding mechanism. Coupling SCANE with the Transformer Encoder architecture, we develop the Scalable nUMerical eMbeddIng Transformer (SUMMIT), which is engineered to deliver precise predictive outputs for MTS characterized by prevalent missing entries. Our experimental validation, conducted across three disparate electronic health record (EHR) datasets marked by elevated missing value frequencies, confirms the superior performance of SUMMIT over contemporary state-of-the-art approaches addressing similar challenges. These results substantiate the efficacy of SCANE and SUMMIT, underscoring their potential applicability across a broad spectrum of MTS data analytical tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Employing texture features of chest x-ray images and machine learning in covid-19 detection and classification

Mendel

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Mendel 2021年第1期27卷 9-17页

作者： Alquran, Hiam Alsleti, Mohammad Alsharif, Roaa Qasmieh, Isam Abu Alqudah, Ali Mohammad Harun, Nor Hazlyna Binti Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid21163 Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman11855 Jordan College of applied medical Sciences Radiological Science Program King Saud University Jeddah21435 Saudi Arabia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Kedah06010 Malaysia

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient;as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival. © 2021, Brno University of Technology. All rights reserved.

关键词： COVID-19

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Machine Learning in Chemoinformatics and Medicinal Chemistry

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Biomedical data science 1000年第1期5卷 43-65页

作者： Raquel Rodríguez-Pérez Filip Miljković Jürgen Bajorath 2Current affiliation: Novartis Institutes for Biomedical Research Novartis Campus Basel Switzerland 1Department of Life Science Informatics B-IT (Bonn-Aachen International Center for Information Technology) Chemical Biology and Medicinal Chemistry Program Unit LIMES (Life and Medical Sciences Institute) Rheinische Friedrich-Wilhelms-Universität Bonn Germany email: bajorath@bit.uni-bonn.de 3Current affiliation: Data Science and AI Imaging and Data Analytics Clinical Pharmacology and Safety Sciences R&D AstraZeneca Gothenburg Sweden

In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, and reaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning and deep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains.

关键词： machine learning deep learning chemoinformatics medicinal chemistry data structures learning strategies

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