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Clinical use of current polygenic risk scores may exacerbate health disparities (vol 51, pg 584, 2019)

NATURE GENETICS 2021年第5期53卷 763-763页

作者： Martin, Alicia R. Kanai, Masahiro Kamatani, Yoichiro Okada, Yukinori Neale, Benjamin M. Daly, Mark J. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Laboratory for Statistical Analysis RIKEN Center for Integrative Medical Sciences Yokohama Japan Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Kyoto–McGill International Collaborative School in Genomic Medicine Kyoto University Graduate School of Medicine Kyoto Japan Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan

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Establishment of a protocol for rapidly expanding Epstein-Barr-virus-specific cytotoxic T cells with enhanced cytotoxicity

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BMC cancer 2024年第1期24卷 980页

作者： Chih-Hao Fang Ya Fang Cheng Shian-Ren Lin Wan-Yu Lai Li-Ren Liao Yen-Ling Chiu Jan-Mou Lee FullHope Biomedical Co. Ltd. 10F. No. 10 Ln. 609 Sec. 5 Chongxin Rd. Sanchong Dist. New Taipei City 241405 Taiwan. Department of Otolaryngology Far Eastern Memorial Hospital New Taipei City 220216 Taiwan. Division of Nephrology Department of Medicine Far Eastern Memorial Hospital New Taipei City 220216 Taiwan. yenling.chiu@***. Department of Medical Research Far Eastern Memorial Hospital No. 121 Sec. 2 Nanya S. Rd. Banqiao Dist. New Taipei City 220216 Taiwan. yenling.chiu@***. Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan 320315 Taiwan. yenling.chiu@***. Graduate Institute of Clinical Medicine National Taiwan University College of Medicine Taipei 100233 Taiwan. yenling.chiu@***. FullHope Biomedical Co. Ltd. 10F. No. 10 Ln. 609 Sec. 5 Chongxin Rd. Sanchong Dist. New Taipei City 241405 Taiwan. billlee@fhb.com.tw.

BACKGROUND:Lytic Epstein-Barr virus (EBV) infection plays a major role in the pathogenesis of nasopharyngeal carcinoma (NPC). For patients with recurrent or metastatic NPC and resistant to conventional therapies, adoptive cell therapy using EBV-specific cytotoxic T cells (EBV-CTLs) is a promising option. However, the long production period (around 3 to 4 weeks) and low EBV-CTL purity (approximately 40% of total CD8 T cells) in the cell product limits the application of EBV-CTLs in clinics. Thus, this study aimed to establish a protocol for the rapid production of EBV-CTLs. METHODS:By culturing peripheral blood mononuclear cells (PBMCs) from EBV-seropositive donors with EBV-specific peptides and interleukin (IL)-2, IL-15, and interferon α (IFN-α) for 9 days, we identified that IL-15 can enhance IL-2-mediated CTL activation and significantly increase the yield of CTLs. RESULTS:When IFN-α was used in IL-2/IL-15-mediated CTL production from days 0 to 6, the productivity of EBV-CTLs and EBV-specific cytotoxicity significantly were reinforced relative to EBV-CTLs from IL-2/IL-15 treatment. Additionally, IFN-α-induced production improvement of virus-specific CTLs was not only the case for EBV-CTLs but also for cytomegalovirus-specific CTLs. CONCLUSION:We established a novel protocol to rapidly expand highly pure EBV-CTLs from PBMCs, which can produce EBV-CTLs in 9 days and does not require feeder cells during cultivation.

关键词： Adoptive cell therapy Cytotoxic T lymphocytes Epstein Barr virus Immunotherapy Nasopharyngeal carcinoma

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GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts

arXiv

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arXiv 2023年

作者： Wu, Da Yang, Jingye Liu, Cong Hsieh, Tzung-Chien Marchi, Elaine Blair, Justin Krawitz, Peter Weng, Chunhua Chung, Wendy Lyon, Gholson J. Krantz, Ian D. Kalish, Jennifer M. Wang, Kai Raymond G. Perelman Center for Cellular and Molecular Therapeutics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Institute for Genomic Statistics and Bioinformatics University Hospital Bonn Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Department of Human Genetics New York State Institute for Basic Research in Developmental Disabilities Staten IslandNY United States Division of Human Genetics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Pediatrics Boston Children’s Hospital Harvard Medical School BostonMA United States Biology PhD Program The Graduate Center The City University of New York New York United States Department of Genetics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pediatrics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data. © 2023, CC BY.

关键词： Diseases

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Real world fracture prediction of fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OSTA) and one-minute osteoporosis risk test: An 11-year longitudinal study

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Bone Reports 2024年 20卷 101742页

作者： Sheng, Yueh-Hsuan Wu, Tai-Yin Liaw, Chen-Kun Hsiao, Sheng-Huang Kuo, Kuan-Liang Tsai, Ching-Yao Department of Family Medicine Taipei City Hospital Renai Branch No. 10 Sec. 4 Renai Rd. Daan Dist. Taipei City Taiwan Department of Family Medicine Taipei City Hospital Zhongxing Branch No. 145 Zhengzhou Rd. Datong Dist. Taipei City Taiwan Institute of Epidemiology and Preventive Medicine National Taiwan University No. 17 Xuzhou Rd. Zhongzheng Dist. Taipei City Taiwan Department of Health and Welfare University of Taipei No. 101 Sec. 2 Zhongcheng Rd. Shilin Dist Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan Department of Orthopedics Shuang Ho Hospital Taipei Medical University No. 291 Zhongzheng Rd. Zhonghe Dist. New Taipei City Taiwan Research Center of Biomedical Device College of Biomedical Engineering Graduate Institute of Biomedical Optomechatronics Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan TMU Biodesign Center Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan Department of Neurosurgery Taipei City Hospital Renai Branch No. 10 Sec. 4 Ren'ai Rd. Da'an Dist. Taipei City Taiwan Department of Psychology National Chengchi University No. 64 Sec. 2 Zhinan Rd. Wenshan Dist. Taipei City Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City Taiwan The Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen University No. 510 Zhongzheng Rd. Xinzhuang Dist. New Taipei City Taiwan Department of Ophthalmology Taipei City Hospital No. 145 Zhengzhou Rd. Datong Dist. Taipei City Taiwan Institute of Public Health National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City Taiwan Department of Business Administration Fu Jen Catholic University No. 510 Zhongzheng Rd. Xinzhuang Dist. New Taipei City Taiwan

Introduction: Fractures affect people's quality of life especially in the elders. One of the most important risk factors is osteoporosis. There are many screening tools to predict osteoporosis and fractures. We aimed to compare the predictive validity of three commonly used screening tools: fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OSTA) and one-minute osteoporosis risk test. Among them, OSTA and one-minute osteoporosis risk test were originally developed to predict osteoporosis risks and FRAX was to predict fracture risks. Methods: This is an 11-year longitudinal study. We enrolled 708 senior people from health examinees in Taiwan in 2010. A standardized questionnaire and blood tests were provided. Annual telephone interview was conducted to assess the real fracture status. We calculated risk scores of FRAX, OSTA, and one-minute osteoporosis risk test and compared with real-world fracture records. Results: The mean age of the participants were 74.9 (SD 6.4). There were 356 (50.3 %) men. From 2010 to 2020, a total of 105 (14.8 %) persons suffered from fractures. Compared to people without fractures, people with fractures had higher FRAX major osteoporotic fracture risk scores (14.0 % ± 7.6 % vs.11.3 % ± 5.7 %), higher hip fracture risk scores, and higher OSTA risk (5.9 % ± 1.4 % vs. 5.3 % ± 1.3 %). Cox regression analysis showed that hazard ratios for fracture of high FRAX risk was 1.53 (95 % confidence interval (CI) 1.05–2.21), and for high OSTA risk was 1.37 (95 % CI 1.04–1.82). Conclusions: Only OSTA and FRAX scores were satisfactory in predicting 10-year fractures. © 2024

关键词： Fracture risk assessment tool (FRAX) Fractures One-minute osteoporosis risk test Osteoporosis self-assessment tool for Asians (OSTA)

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AAV-mediated silencing of Sox4 leads to long-term amelioration of liver phenotypes in mouse models of Alagille syndrome

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Gastroenterology 2025年

作者： Duncan Fox Jun Xie Jennifer L. Burwinkel Josh M. Adams Kashish Chetal Marzieh Keivandarian Yaniv Faingelernt Sanjay Subramanian Mario F. Lopez Anna L. Peters Nathan Salomonis Neda Zarrin-Khameh Guangping Gao Stacey S. Huppert Hamed Jafar-Nejad Department of Molecular and Human Genetics Baylor College of Medicine Houston Texas Genetics and Genomics Graduate Program Baylor College of Medicine Houston Texas Department of Genetic and Cellular Medicine UMass Chan Medical School Worcester Massachusetts Horae Gene Therapy Center UMass Chan Medical School Worcester Massachusetts Department of Microbiology UMass Chan Medical School Worcester Massachusetts Viral Vector Core UMass Chan Medical School Worcester Massachusetts Division of Gastroenterology Hepatology and Nutrition Cincinnati Children’s Hospital Medical Center Cincinnati Ohio Program in Developmental Biology Baylor College of Medicine Houston Texas Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati Ohio Department of Pediatrics University of Cincinnati College of Medicine Cincinnati Ohio Department of Pathology and Immunology Baylor College of Medicine and Ben Taub Hospital Houston Texas

BACKGROUND & AIMS In patients with Alagille syndrome (ALGS), bile duct paucity often leads to severe cholestatic phenotypes for which liver transplantation remains the only definitive treatment. No FDA-approved mechanism-based strategies exist to enhance biliary development in ALGS or other diseases with bile duct paucity. We aimed to identify a therapeutic target to address this unmet need. METHODS Preclinical ALGS mouse models lacking one copy of Jag1 with or without conditional deletion of one or both copies of Sox9 were used. Sox4 levels were reduced genetically or with adeno-associated virus 8 (AAV8) vectors driving a Sox4 -silencing sequence. Liver histology, biliary tree ink injection, serum chemistry, RNAscope (on mouse and human livers), mouse liver single-cell RNA-sequencing (scRNA-Seq), and reanalysis of published human liver bulk RNA-Seq were performed. RESULTS Conditional removal of one copy of Sox4 in mouse liver significantly improved the ALGS liver phenotypes in a Sox9 -dependent manner. An increase in Sox4/SOX4 expression in early postnatal Jag1 -heterozygous mouse livers and ALGS patient livers was observed. scRNA-Seq revealed the appearance of an intermediate hepatobiliary cluster co-expressing Sox4 and Sox9 in Jag1- heterozygous livers. AAV8-mediated Sox4 knockdown, ubiquitously or driven by the hepatocyte-specific TBG promoter, led to long-term improvement of ALGS liver phenotypes upon injection at postnatal day 1 (P1). AAV8 injection at P15—after the appearance of liver necrosis—led to the incorporation of some transduced cells into bile ducts and phenotypic improvement. CONCLUSIONS Preclinical studies provide proof of principle for postnatal AAV-mediated Sox4 knockdown in TBG + cells as a therapeutic approach for ALGS liver disease.

关键词： Gene therapy JAG1 Notch signaling cholestasis

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Mixture-of-experts variational autoencoder for clustering and generating from similarity-based representations

arXiv

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arXiv 2019年

作者： Kopf, Andreas Fortuin, Vincent Somnath, Vignesh Ram Claassen, Manfred Institute of Molecular Systems Biology Department of Biology ETH Zürich Switzerland Biomedical Informatics Group Department of Computer Science ETH Zürich Switzerland Switzerland Life Science Graduate School Zurich PhD Program Systems Biology Switzerland

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering gained popularity due to the non-linearity of neural networks, which allows for flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on synthetic data, the MNIST benchmark data set, and a challenging real-world task of defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines and we show that the MoE architecture in the decoder reduces the computational cost of sampling specific data modes with high fidelity. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Percutaneous Coronary Intervention With a Drug-Eluting Stent Versus Coronary Artery Bypass Grafting in Patients Receiving Dialysis: A National Study From Taiwan

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Kidney medicine 2024年第2期6卷 100768页

作者： Szu-Yu Pan Ju-Yeh Yang Nai-Chi Teng Yun-Yi Chen Shi-Heng Wang Chien-Lin Lee Kang-Lung Chen Yen-Ling Chiu Shih-Ping Hsu Yu-Sen Peng Yung-Ming Chen Shuei-Liong Lin Likwang Chen Department of Integrated Diagnostics and Therapeutics National Taiwan University Hospital Taipei Taiwan. Division of Nephrology Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan. Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Institute of Health Policy and Management College of Public Health National Taiwan University Taipei Taiwan. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Department of Research Shin-Kong Wu Ho-Su Memorial Hospital Taipei Taiwan. Institute of Hospital and Health Care Administration National Yang Ming Chiao Tung University Taipei Taiwan. National Center for Geriatrics and Welfare Research National Health Research Institutes Yunlin Taiwan. Department of Cardiology Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Cardiovascular Surgery Far Eastern Memorial Hospital New Taipei City Taiwan. Graduate Program in Biomedical Informatics Department of Computer Science and Engineering College of Informatics Yuan Ze University Taoyuan Taiwan. Graduate Institute of Clinical Medicine National Taiwan University College of Medicine Taipei Taiwan. Graduate Institute of Physiology College of Medicine National Taiwan University Taipei Taiwan.

Rationale & Objective:We aimed to study the comparative effectiveness of percutaneous coronary intervention with drug-eluting stent and coronary artery bypass grafting in patients receiving dialysis. Study Design:This was a retrospective observational cohort study. Setting & Participants:This population-based study identified patients receiving dialysis hospitalized for coronary revascularization between January 1, 2009 and December 31, 2015, in the Taiwan National Health Insurance Research Database. Exposures:Patients received percutaneous coronary intervention with drug-eluting stent versus coronary artery bypass grafting. Outcomes:The study outcomes were all-cause mortality, in-hospital mortality, and repeat revascularization. Analytical Approach:Propensity scores were used to match patients. Cox proportional hazards models and logistic regression models were constructed to examine associations between revascularization strategies and mortality. Interval Cox models were fitted to estimate time-varying hazards during different periods. Results:A total of 1,840 propensity score-matched patients receiving dialysis were analyzed. Coronary artery bypass grafting was associated with higher in-hospital mortality (coronary artery bypass grafting vs percutaneous coronary intervention with drug-eluting stent; crude mortality rate 12.5% vs 3.3%; adjusted OR, 5.22; 95% CI, 3.42-7.97; < 0.001) and longer hospitalization duration (median [IQR], 20 [14-30] days vs 3 [2-8] days; < 0.001). After discharge, repeat revascularization, acute coronary syndrome, and repeat hospitalization all occurred more frequently in the percutaneous coronary intervention with drug-eluting stent group. Importantly, with a median follow-up of 2.8 years, coronary artery bypass grafting was significantly associated with a higher risk of all-cause overall mortality (adjusted HR, 1.19; 95% CI, 1.05-1.35; = 0.006) in the multivariable Cox proportional hazard model. Sensitivity and subgroup analyses yie

关键词： Coronary artery bypass grafting coronary revascularization dialysis drug-eluting stent end-stage kidney disease mortality percutaneous coronary intervention survival

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Author Correction: An atlas of epithelial cell states and plasticity in lung adenocarcinoma

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Nature 2024年第8006期628卷 E1页

作者： Guangchun Han Ansam Sinjab Zahraa Rahal Anne M Lynch Warapen Treekitkarnmongkol Yuejiang Liu Alejandra G Serrano Jiping Feng Ke Liang Khaja Khan Wei Lu Sharia D Hernandez Yunhe Liu Xuanye Cao Enyu Dai Guangsheng Pei Jian Hu Camille Abaya Lorena I Gomez-Bolanos Fuduan Peng Minyue Chen Edwin R Parra Tina Cascone Boris Sepesi Seyed Javad Moghaddam Paul Scheet Marcelo V Negrao John V Heymach Mingyao Li Steven M Dubinett Christopher S Stevenson Avrum E Spira Junya Fujimoto Luisa M Solis Ignacio I Wistuba Jichao Chen Linghua Wang Humam Kadara Department of Genomic Medicine The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Translational Molecular Pathology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Pulmonary Medicine The University of Texas MD Anderson Cancer Center Houston TX USA. Graduate Program in Developmental Biology Baylor College of Medicine Houston TX USA. The University of Texas Health Houston Graduate School of Biomedical Sciences Houston TX USA. Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Thoracic Head and Neck Medical Oncology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Cardiovascular and Thoracic Surgery The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Medicine The University of California Los Angeles Los Angeles CA USA. Lung Cancer Initiative at Johnson & Johnson Boston MA USA. Section of Computational Biomedicine School of Medicine Boston University Boston MA USA. Department of Pulmonary Medicine The University of Texas MD Anderson Cancer Center Houston TX USA. JChen16@***. The University of Texas Health Houston Graduate School of Biomedical Sciences Houston TX USA. JChen16@***. Department of Genomic Medicine The University of Texas MD Anderson Cancer Center Houston TX USA. lwang22@***. The University of Texas Health Houston Graduate School of Biomedical Sciences Houston TX USA. lwang22@***. Department of Translational Molecular Pathology The University of Texas MD Anderson Cancer Center Houston TX USA. hkadara@***. The University of Texas Health Houston Graduate School of Biomedical Sciences Houston TX USA. hkadara@***.

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Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy

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Computer Methods and programs in Biomedicine 2023年 233卷 107480-107480页

作者： Huang, Chih-Wei Wu, Bethany C.Y. Nguyen, Phung Anh Wang, Hsiao-Han Kao, Chih-Chung Lee, Pei-Chen Rahmanti, Annisa Ristya Hsu, Jason C. Yang, Hsuan-Chia Li, Yu-Chuan Jack Taipei Medical University Taipei Taiwan Taipei Medical University Ringgold standard institution - Center for Simulation in Medical Education Taipei 116 Taiwan National Taiwan University Children and Family Research Center Sponsored by CTBC Charity Foundation Taipei Taiwan Clinical Data Center Office of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TMU Da'an Campus 15 F No. 172-1 Kee lung Road Section 2 Da-an District Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology Wanfang Hospital Taipei Medical University Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Delta Research Center Taipei Taiwan Department of Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan

Background and objective: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. Methods: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. Results: Data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p © 2023

关键词： Emotion Recognition

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Precision probiotics supplement strategy in aging population based on gut microbiome composition

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Briefings in bioinformatics 2024年第4期25卷 bbae351页

作者： Yi-Fang Chuang Kang-Chen Fan Yin-Yuan Su Ming-Fong Wu Yen-Ling Chiu Yi-Chien Liu Chen-Ching Lin School of Medicine National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City 112304 Taiwan. Institute of Public Health National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City 112304 Taiwan. Department of Psychiatry Far Eastern Memorial Hospital No. 21 Sec. 2 Nanya S. Rd. Banqiao Dist. New Taipei City 220216 Taiwan. Institute of Biomedical Informatics National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City 112304 Taiwan. Department of Medical Research Far Eastern Memorial Hospital No. 21 Sec. 2 Nanya S. Rd. Banqiao Dist. New Taipei City 220216 Taiwan. Graduate Program in Biomedical Informatics and Graduate Institute of Medicine Yuan Ze University No. 135 Yuandong Rd. Zhongli Dist. Taoyuan City 320315 Taiwan. Graduate Institute of Clinical Medicine National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei City 106319 Taiwan. Department of Neurology Cardinal Tien Hospital No. 362 Zhongzheng Rd. Xindian Dist. New Taipei City 231009 Taiwan.

With the increasing prevalence of age-related chronic diseases burdening healthcare systems, there is a pressing need for innovative management strategies. Our study focuses on the gut microbiota, essential for metabolic, nutritional, and immune functions, which undergoes significant changes with aging. These changes can impair intestinal function, leading to altered microbial diversity and composition that potentially influence health outcomes and disease progression. Using advanced metagenomic sequencing, we explore the potential of personalized probiotic supplements in 297 older adults by analyzing their gut microbiota. We identified distinctive Lactobacillus and Bifidobacterium signatures in the gut microbiota of older adults, revealing probiotic patterns associated with various population characteristics, microbial compositions, cognitive functions, and neuroimaging results. These insights suggest that tailored probiotic supplements, designed to match individual probiotic profile, could offer an innovative method for addressing age-related diseases and functional declines. Our findings enhance the existing evidence base for probiotic use among older adults, highlighting the opportunity to create more targeted and effective probiotic strategies. However, additional research is required to validate our results and further assess the impact of precision probiotics on aging populations. Future studies should employ longitudinal designs and larger cohorts to conclusively demonstrate the benefits of tailored probiotic treatments.

关键词： consensus clustering gut microbiome metagenomics non-negative matrix factorization older adults precision probiotics

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