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Xenoestrogen effects on the gut microbiome

Current Opinion in Endocrine and Metabolic Research 2021年 19卷 41-45页

作者： Rosenfeld, Cheryl S. Christopher S Bond Life Sciences Center University of Missouri Columbia 65211 MO United States Biomedical Sciences University of Missouri Columbia 65211 MO United States MU Institute for Data Science and Informatics (MU IDSI) University of Missouri Columbia 65211 MO United States Thompson Center for Autism and Neurobehavioral Disorders University of Missouri Columbia 65211 MO United States Genetics Area Program University of Missouri Columbia 65211 MO United States

Endocrine disrupting chemicals (EDCs) that act as xenoestrogens are natural and synthetic chemicals widely present in food products, industrial products, and the environment. Such compounds can activate or inhibit normal hormonal pathways by binding to steroid and non-steroid receptors. It is becomingly apparent that resident bacteria in the gut and elsewhere in the body can dramatically influence host responses. As such, increasing number of studies have examined how EDCs affect the gut microbiome in a range of animal species. This review article will examine what is known about how various xenoestrogens, including bisphenol A (BPA), phthalates, and phytoestrogens, affect the gut microbiome in vertebrate species, any known secondary host effects, such as through alteration of gut metabolites, and future directions in the field. © 2021 Elsevier Ltd

关键词： Bisphenol A Genistein Phytoestrogens Rodent models Xenoestrogens

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data-Driven Discriminable Factors Analytics of Teaching Performance Ratings for College Teachers

Data-Driven Discriminable Factors Analytics of Teaching Perf...

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International Conference on Educational and Information Technology (ICEIT)

作者： Junxiao Liu Tianyu Gao Shu Liang Kecheng Chen Junwei Zeng Fengshi Jing Jiandong Zhou Centre for Faculty Development Jinan University Guangzhou China Young Teachers’ Development Program School of PE Jinan University Guangzhou China School of Data Science City University of Hong Kong Hong Kong SAR China School of Physical Education Guangdong University of Technology Guangzhou China UNC Project-China UNC Global University of North Carolina Chapel Hill NC United States Nuffield Department of Medicine University of Oxford Oxford Untied Kindom

Professional development for college teachers is essential and improving teaching performance is a key aspect of this. To evaluate teaching performance in colleges and universities, we use student ratings for each lecturer and course. Investigating the characteristics of college teachers associated with teaching performance ratings can be valuable for students, teachers, universities, and higher education administrators. Previous studies have identified only some of the characteristics associated with teaching performance, and the data on ratings used in these studies was often collected from online rating/comment websites, which may not be reliable. This data-driven study aims to identify discriminative factors of teaching performance ratings for college teachers at a university in Guangdong, China. We collected comprehensive data on college teachers' characteristics and used data on teaching performance ratings obtained from the university's official channel. Our findings can inform policies and practices related to teaching evaluation and professional development in higher education.

关键词： Education Reliability Information technology

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Molecular-driven Foundation Model for Oncologic Pathology

arXiv

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arXiv 2025年

作者： Vaidya, Anurag Zhang, Andrew Jaume, Guillaume Song, Andrew H. Ding, Tong Wagner, Sophia J. Lu, Ming Y. Doucet, Paul Robertson, Harry Almagro-Pérez, Cristina Chen, Richard J. ElHarouni, Dina Ayoub, Georges Bossi, Connor Ligon, Keith L. Gerber, Georg Le, Long Phi Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich – German Research Center for Environment and Health Munich Germany School of Computation Information and Technology TUM Munich Germany CambridgeMA United States Sydney Precision Data Science Center The University of Sydney CamperdownNSW Australia Department of Oncologic Pathology Dana-Farber Cancer Institute BostonMA02215 United States Department of Pathology Boston Children’s Hospital BostonMA02115 United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. Here, we introduce THREADS, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. THREADS was pretrained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles—the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables THREADS to capture the tissue’s underlying molecular composition, yielding powerful representations applicable to a wide array of downstream tasks. In extensive benchmarking across 54 oncology tasks, including clinical subtyping, grading, mutation prediction, immunohistochemistry status determination, treatment response prediction and survival prediction THREADS outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well-suited for predicting rare events, further emphasizing its clinical utility. We intend to make the model publicly available for the broader community. © 2025, CC BY-NC-ND.

关键词： Transfer learning

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Characterizing Emergency Department Care for Patients With Histories of Incarceration

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JACEP Open 2025年第1期6卷 100022-100022页

作者： Huang, Thomas Socrates, Vimig Ovchinnikova, Polina Faustino, Isaac Kumar, Anusha Safranek, Conrad Chi, Ling Wang, Emily A. Puglisi, Lisa Wong, Ambrose H. Wang, Karen H. Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Department for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States SEICHE Center for Health and Justice Yale School of Medicine New Haven CT United States Equity Research and Innovation Center Yale School of Medicine Yale University New Haven CT United States

Objectives: Patients with a history of incarceration experience bias from health care team members, barriers to privacy, and a multitude of health care disparities. We aimed to assess care processes delivered in emergency departments (EDs) for people with histories of incarceration. Methods: We utilized a fine-tuned large language model to identify patient incarceration status from 480,374 notes from the ED setting. We compared socio-demographic characteristics, comorbidities, and care processes, including disposition, restraint use, and sedation, between individuals with and without a history of incarceration. We then conducted multivariable logistic regression to assess the independent correlation of incarceration history and management in the ED while adjusting for demographic characteristics, health behaviors, presentation, and past medical history. Results: We found 1734 unique patient encounters with a history of incarceration from a total of 177,987 encounters. Patients with history of incarceration were more likely to be male, Black, Hispanic, or other race/ethnicity, currently unemployed or disabled, and had smoking and substance use histories, compared with those without. This cohort demonstrated higher odds of elopement (OR: 3.59 [95% CI: 2.41–5.12]), leaving against medical advice (OR: 2.39 [95% CI: 1.46–3.67]), and being subjected to sedation (OR: 3.89 [95% CI: 3.19–4.70]) and restraint use (OR: 3.76 [95% CI: 3.06–4.57]). After adjusting for covariates, the association between incarceration and elopement remained significant (adjusted odds ratio: 1.65 [95% CI: 1.08–2.43]), while associations with other dispositions, restraint use, and sedation did not persist. Conclusion: This study identified differences in patient characteristics and care processes in the ED for patients with histories of incarceration and demonstrated the potential of using natural language processing in measuring care processes in populations that are largely hidden, but highly prev

关键词： artificial intelligence emergency medicine incarceration large language models quality of health care

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SubTuner leverages physics-based modeling to complement AI in enzyme engineering toward non-native substrates

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Chem Catalysis 2025年第6期5卷

作者： Shao, Qianzhen Hollenbeak, Asher C. Jiang, Yaoyukun Ran, Xinchun Bachmann, Brian O. Yang, Zhongyue J. Department of Chemistry Vanderbilt University Nashville 37235 TN United States Center for Structural Biology Vanderbilt University Nashville 37235 TN United States The Vanderbilt Institute of Chemical Biology Vanderbilt University Nashville 37235 TN United States Data Science Institute Vanderbilt University Nashville 37235 TN United States Department of Chemical and Biomolecular Engineering Vanderbilt University Nashville 37235 TN United States The Chemical and Physical Biology Program Vanderbilt University Nashville 37232 TN United States

We developed SubTuner, a physics-based computational tool that tackles the challenge of identifying enzyme mutants with enhanced activity for specified non-native substrates. To test the performance of SubTuner, we designed three tasks, all aiming to identify beneficial anion methyltransferase mutants for synthesis of non-native S-adenosyl-L-methionine analogs: first, in the conversion of ethyl iodide from a pool of 190 Arabidopsis thaliana Harmless to Ozone Layer 1 (AtHOL1) single-point mutants for an initial test of accuracy and speed;second, of ethyl, n-propyl, cyclopropylmethyl, and phenethyl iodides from a pool of 600 Aspergillus clavatus methyltransferase multi-point mutants for a test of generalizability;and, eventually, of bulkier substrates for AtHOL1 combined with experimental characterization for a test of a priori predictivity. All tests demonstrated SubTuner's ability to accelerate enzyme engineering for non-native substrates, superior to existing bioinformatics and machine-learning-based tools. SubTuner, with its physical hypothesis, quantitative accuracy, and mechanism-informing ability, holds significant potential to aid enzyme engineering for substrate scope expansion. © 2025 Elsevier Inc.

关键词： anion methyltransferase artificial intelligence biocatalysis computational enzyme engineering EnzyHTP enzyme high-throughput enzyme modeling physics-guided modeling SDG9: Industry, innovation, and infrastructure

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Fractal geometry predicts dynamic differences in structural and functional connectomes

arXiv

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arXiv 2025年

作者： Rădulescu, Anca Kaslik, Eva Fikl, Alexandru Nakuci, Johan Muldoon, Sarah Anderson, Michael Department of Mathematics SUNY New Paltz United States Department of Computer Science West University of Timișoara Romania Institute for Advanced Environmental Research West University of Timișoara Romania U.S. ARMY DEVCOM Army Research Laboratory United States Department of Mathematics Institute for Artificial Intelligence and Data Science Neuroscience Program University of Buffalo SUNY United States Department of Data Analytics Georgia Tech United States

Understanding the intricate architecture of brain networks and its connection to brain function is essential for deciphering the underlying principles of cognition and disease. While, traditional graph-theoretical measures have been widely used to characterize these networks, they often fail to fully capture the emergent properties of large-scale neural dynamics. Here, we introduce an alternative approach to quantify brain networks that is rooted in complex dynamics, fractal geometry, and asymptotic analysis. We apply these concepts to brain connectomes and demonstrate how quadratic iterations and geometric properties of Mandelbrot-like sets can provide novel insights into structural and functional network dynamics. Our findings reveal fundamental distinctions between structural (positive) and functional (signed) connectomes, such as the shift of cusp orientation and the variability in equi-M set geometry. Notably, structural connectomes exhibit more robust, predictable features, while functional connectomes show increased variability for non-trivial tasks. We further demonstrate that traditional graph-theoretical measures, when applied separately to the positive and negative sub-networks of functional connectomes, fail to fully capture their dynamic complexity. Instead, size and shape-based invariants of the equi-M set effectively differentiate between rest and emotional task states, which highlights their potential as superior markers of emergent network dynamics. These results suggest that incorporating fractal-based methods into network neuroscience provides a powerful tool for understanding how information flows in natural systems beyond static connectivity measures, while maintaining their simplicity. © 2025, CC BY.

关键词： Fractal dimension

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Is Parent Diet Quality Related to Child Diet Quality? A Cross-Sectional Pilot Study Among Low-Income Families from Indiana

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Journal of Nutrition Education and Behavior 2024年第8期56卷 S44-S45页

作者： Analí Morales-Juárez Mwiza A. Uwashimimana Regan Bailey Bruce A. Craig Janet Tooze Blake Connolly Wanda Stevens Heather A. Eicher-Miller Department of Nutrition Science Purdue University Institute for Advancing Health Through Agriculture Texas A&M University System Department of Statistics Purdue University Department of Biostatistics and Data Science Wake Forest University School of Medicine Purdue Extension Nutrition Education Program (NEP) in the College of Health and Human Sciences Purdue University

Background Evidence has shown inconclusive relationships between parent and child diets, including diet quality, despite an expected link. Objective The objective of this pilot study was to determine whether parent diet quality predicts child diet quality among low-income families in Indiana, United States. Study Design, Settings, Participants Baseline data collected from April 2023 to January 2024 from 49 paired parents 26-75 years and their children 5-17 years (one parent and one child) from low-income households throughout Indiana was utilized for this secondary analysis. Measurable Outcome/Analysis The parent completed the U.S. Household Food Security Survey Module and parent and child each completed one to four 24-hour dietary recalls. The Healthy Eating Index (HEI-2015) for both parent and child were derived using the simple method for individuals. Their associations were assessed via Pearson correlation (unadjusted) as well as multiple linear regression models with child HEI as the dependent variable, adjusted for food security status; child's sex, ethnicity, and age; and parent's age and smoking status. Results The HEI-2015 total scores indicated poor dietary quality for parents and children (50.9±11.6 and 51.5±12.1, respectively). The parent-child correlations for HEI-2015 that were statistically significant (p < 0.05) were weak to moderate (r=0.4 for total HEI, total fruits, greens and beans, and saturated fats; r=0.3 for whole fruits, total vegetables, and fatty acids; and r=0.5 for whole grains). When controlling for covariates, parent diet quality was not significantly related to child diet quality for the total HEI-2015 score but was significantly related for components such that an increase in one HEI point in the parent score, on average, was related to a small increase of 0.4 points for greens and beans (p=0.01), whole grains (p=0.004), and saturated fats (p=0.01); and 0.3 points for fatty acids (p=0.02) in the child scores. Conclusions A weak-moder

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Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

arXiv

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arXiv 2025年

作者： Huang, Yepeng Su, Xiaorui Ullanat, Varun Liang, Ivy Clegg, Lindsay Olabode, Damilola Ho, Nicholas John, Bino Gibbs, Megan Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Program in Biological and Biomedical Sciences Harvard Medical School BostonMA United States Harvard College CambridgeMA United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca GaithersburgMD United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Program in Computational Biology Carnegie Mellon University PittsburghPA United States Imaging and Data Analytics Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations, reducing late-stage clinical failures, and accelerating the development of precision therapies. Current models rely on structural or target-based features to identify high-efficacy, low-toxicity drug combinations. However, these approaches fail to incorporate the multimodal data necessary for accurate, clinically-relevant predictions. Here, we introduce MADRIGAL, a multimodal AI model that learns from structural, pathway, cell viability, and transcriptomic data to predict drug combination effects across 953 clinical outcomes and 21,842 compounds, including combinations of approved drugs and novel compounds in development. MADRIGAL uses a transformer bottleneck module to unify preclinical drug data modalities while handling missing data during training and inference—a major challenge in multimodal learning. It outperforms single-modality methods and state-of-the-art models in predicting adverse drug interactions. MADRIGAL performs virtual screening of anticancer drug combinations and supports polypharmacy management for type II diabetes and metabolic dysfunction-associated steatohepatitis (MASH). It identifies transporter-mediated drug interactions. MADRIGAL predicts resmetirom, the first and only FDA-approved drug for MASH, among therapies with the most favorable safety profile. It supports personalized cancer therapy by integrating genomic profiles from cancer patients. Using primary acute myeloid leukemia samples and patient-derived xenograft models, it predicts the efficacy of personalized drug combinations. Integrating MADRIGAL with a large language model allows users to describe clinical outcomes in natural language, improving safety assessment by identifying potential adverse interactions and toxicity risks. MADRIGAL provides a multimodal approach for designing combination therapies with improved predictive accuracy and clinical relevance. Copy

关键词： Diseases

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Hybrid Intelligent-Annotation Organ Segmentation on Medical datasets

Hybrid Intelligent-Annotation Organ Segmentation on Medical ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tao Peng Jing Zhao Yidong Gu Gongye Di Lei Zhang Jing Cai School of Future Science and Engineering Soochow University Suzhou China Department of Radiation Oncology UT Southwestern Medical Center Dallas TX USA Department of Ultrasound Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital Beijing China Department of Medical Ultrasound Suzhou Municipal Hospital The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Jiangsu China Department of Ultrasonic The Affiliated Taizhou People's Hospital of Nanjing Medical University Taizhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan Jiangsu China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China

Ultrasound image segmentation is crucial for early disease detection and treatment planning but remains a challenging task due to the low contrast of organ boundaries and varying image quality. Current methods often require manual intervention or have limited accuracy. In this paper, we propose a novel hybrid framework that combines an automatic option polygon segment (AOPS) algorithm and a distributed- and memory-based evolution (DME) algorithm for precise ultrasound organ segmentation. Our pipeline consists of two cascaded stages: (1) a coarse segmentation step using the AOPS algorithm, which determines the number of vertices/clusters without human intervention, and (2) a refinement step using the DME algorithm to hunt for the optimal neural network, which is then used to represent a smooth, explainable mathematical expression of the organ boundary. We employ the fractional backpropagation learning network with L2 regularization (FBLN) for training and use the scaled exponential linear unit (SELU) activation function to address the vanishing gradient problem. This is a new attempt such a hybrid framework is applied to ultrasound organ segmentation tasks, and it demonstrates significant contributions in terms of accuracy, smoothness, and computational efficiency.

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Correction to: A machine learning approach to leveraging electronic health records for enhanced omics analysis (Nature Machine Intelligence, (2025), 7, 2, (293-306), 10.1038/s42256-024-00974-9)

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Nature Machine Intelligence 2025年

作者： Mataraso, Samson J. Espinosa, Camilo A. Seong, David Reincke, S. Momsen Berson, Eloise Reiss, Jonathan D. Kim, Yeasul Ghanem, Marc Shu, Chi-Hung James, Tomin Tan, Yuqi Shome, Sayane Stelzer, Ina A. Feyaerts, Dorien Wong, Ronald J. Shaw, Gary M. Angst, Martin S. Gaudilliere, Brice Stevenson, David K. Aghaeepour, Nima Department of Anesthesiology Perioperative and Pain Medicine Stanford University School of Medicine StanfordCA United States Department of Pediatrics Stanford University School of Medicine StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Immunology Program Stanford University School of Medicine StanfordCA United States Medical Scientist Training Program Stanford University School of Medicine StanfordCA United States Department of Pathology Stanford University School of Medicine StanfordCA United States Department of Microbiology and Immunology Stanford University School of Medicine StanfordCA United States Department of Pathology University of California San Diego La Jolla CA United States

Correction to: Nature Machine Intelligencehttps://***/10.1038/s42256-024-00974-9, published online 16 January 2025. In the version of the article initially published, in the "COMET deep learning architecture" section of the Methods, the equations for MSE and BSE were switched and have now been corrected as shown: (Formula presented.) and (Formula presented.) Additionally, the grey background circles in Fig. 4d were incorrectly offset from the clusters and have now been corrected. These corrections have been made to the HTML and PDF versions of the article. © The Author(s) 2025.

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