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Time-dependent reduction of somatic variants in lichen striatus as evidence for the clearance of mutated skin cells

Journal of the American Academy of Dermatology 2025年第6期92卷 1377-1379页

作者： Lee, Byunghyuk Lim, Dong Min Shin, Kihyuk Lee, Hyun Jung Lee, Jungsoo Kim, Hoon-Soo Kim, Byung-Soo Kim, Moon-Bum Kim, Kihun Kim, Won Kyu Kang, Junho Ko, Hyun-Chang Kim, Yun Hak Department of Dermatology College of Medicine Pusan National University Yangsan South Korea Interdisciplinary Program of Genomic Data Science Pusan National University Yangsan South Korea Department of Dermatology Pusan National University Yangsan Hospital Yangsan South Korea Research Institute for Convergence of Biomedical Science and Technology Pusan National University Yangsan Hospital Yangsan South Korea Department of Pathology School of Medicine Pusan National University Yangsan South Korea Department of Biomedical Informatics School of Medicine Pusan National University Yangsan South Korea Department of Anatomy School of Medicine Pusan National University Yangsan South Korea Natural Product Research Center Korea Institute of Science and Technology Gangneung South Korea Department of Research Keimyung University Dongsan Medical Center Daegu South Korea

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Enhanced Performance and data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Respiratory syncytial virus vaccine effectiveness among US veterans, September, 2023 to March, 2024: a target trial emulation study

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The Lancet Infectious Diseases 2025年第6期25卷 625-633页

作者： Bajema, Kristina L Yan, Lei Li, Yuli Argraves, Stephanie Rajeevan, Nallakkandi Fox, Alexandra Vergun, Robert Berry, Kristin Bui, David Huang, Yuan Lin, Hung-Mo Hynes, Denise M Lucero-Obusan, Cynthia Schirmer, Patricia Cunningham, Francesca Huang, Grant D Aslan, Mihaela Ioannou, George N Veterans Affairs Portland Health Care System Portland OR United States Division of Infectious Diseases Department of Medicine Oregon Health & Science University Portland OR United States Veterans Affairs Cooperative Studies Program Clinical Epidemiology Research Center Veterans Affairs Connecticut Health Care System West Haven CT United States Department of Biostatistics Yale School of Public Health New Haven CT United States Biomedical Informatics & Data Science Yale School of Medicine New Haven CT United States Department of Internal Medicine Yale School of Medicine New Haven CT United States Seattle Epidemiologic Research and Information Center Veterans Affairs Puget Sound Health Care System Seattle WA United States Research and Development Veterans Affairs Puget Sound Health Care System Seattle WA United States Center of Innovation to Improve Veteran Involvement in Care Veterans Affairs Portland Health Care System Portland OR United States Health Management and Policy College of Health Oregon State University Corvallis OR United States Health Data and Informatics Program Center for Quantitative Life Sciences Oregon State University Corvallis OR United States Public Health National Program Office Veterans Health Administration Palo Alto CA United States Public Health National Program Office Veterans Health Administration Washington DC United States Veterans Affairs Center for Medication Safety—Pharmacy Benefit Management Services Hines IL United States Office of Research and Development Veterans Health Administration Washington DC United States Division of Gastroenterology Veterans Affairs Puget Sound Health Care System and University of Washington Seattle WA United States

Background: New respiratory syncytial virus (RSV) vaccines have been approved in the USA for the prevention of RSV-associated lower respiratory tract disease in adults aged 60 years and older. Information on the real-world effectiveness of these vaccines is needed. Methods: We used electronic health records in the Veterans Health Administration to emulate a target trial comparing a single dose of a recombinant stabilised prefusion F protein RSV vaccine versus no vaccination among veterans aged 60 years and older. We matched eligible vaccine recipients with up to four unvaccinated individuals in four monthly nested sequential trials from Sept 1 to Dec 31, 2023. Outcomes were ascertained up to March 31, 2024. The primary outcome was any positive RSV test from day 14 following the matched index date. Secondary outcomes included hospitalisation and emergency department or urgent care encounter occurring within 1 day before or after a positive RSV test. We estimated vaccine effectiveness as 100 × (1 – risk ratio). Findings: We included 146 852 vaccinated individuals matched to 582 936 unique control individuals, weighted equally to represent 146 852 individuals. Across the two groups, 276 039 (94·0%) of 293 704 veterans were male, 17 665 (6·0%) were female, and median age was 75·9 years (IQR 71·7–79·7). Over a median follow-up of 124 days (IQR 102–150), the incidence rate of documented RSV infection was 1·7 (95% CI 1·4–2·1) events per 1000 person-years (88 total events) in the vaccinated group and 7·3 (6·6–8·1) per 1000 person-years in the unvaccinated group (372 total events), and vaccine effectiveness was estimated as 78·1% (72·6–83·5). Among the secondary outcomes, vaccine effectiveness was estimated at 78·7% (72·2–84·8) against RSV-associated emergency department or urgent care encounters, and 80·3% (65·8–90·1) against RSV-associated hospitalisation. Interpretation: RSV vaccination was effective in preventing RSV-related illness, including associated health-care use,

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A multi-layered defense against adversarial attacks in brain tumor classification using ensemble adversarial training and feature squeezing

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Scientific Reports 2025年第1期15卷 1-11页

作者： Yinusa, Ahmeed Faezipour, Misa Computational and Data Science Program Middle Tennessee State University 1301 East Main Street Murfreesboro 37132 TN United States Department of Engineering Technology Middle Tennessee State University 1301 East Main Street Murfreesboro 37132 TN United States

Deep learning, particularly convolutional neural networks (CNNs), has proven valuable for brain tumor classification, aiding diagnostic and therapeutic decisions in medical imaging. Despite their accuracy, these models are vulnerable to adversarial attacks, compromising their reliability in clinical settings. In this research, we utilized a VGG16-based CNN model to classify brain tumors, achieving 96% accuracy on clean magnetic resonance imaging (MRI) data. To assess robustness, we exposed the model to Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks, which reduced accuracy to 32% and 13%, respectively. We then applied a multi-layered defense strategy, including adversarial training with FGSM and PGD examples and feature squeezing techniques such as bit-depth reduction and Gaussian blurring. This approach improved model resilience, achieving 54% accuracy on FGSM and 47% on PGD adversarial examples. Our results highlight the importance of proactive defense strategies for maintaining the reliability of AI in medical imaging under adversarial conditions. © The Author(s) 2025.

关键词： Adversarial attacks Adversarial training Artificial intelligence (AI) Brain tumor Feature squeezing Magnetic resonance imaging (MRI)

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In silico prediction of loop-mediated isothermal amplification using a generalized linear model

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Synthetic Biology 2025年第1期10卷

作者： Taguchi, Kenshiro Michiyuki, Satoru Tsuji, Takumasa Kotoku, Jun’ichi Graduate Degree Program of Health Data Science Teikyo University 2-11-1 Kaga Itabashi-Ku Tokyo173-8605 Japan Fundamental Research Laboratory Eiken Chemical Co. Ltd Tochigi329-0114 Japan Graduate School of Medical Care and Technology Teikyo University 2-11-1 Kaga Itabashi-Ku Tokyo173-8605 Japan

Loop-mediated isothermal amplification (LAMP), a DNA amplification technique under isothermal conditions, provides the important benefits of high sensitivity, specificity, rapidity, and simplicity. Maximizing LAMP features necessitates the design of a complex LAMP primer set (LPS) consisting of four primers for six regions of a given target DNA. Furthermore, the LPS of a given target DNA is designed with LPS design support software such as Primer Explorer. However, even if the design is completed, we still must do many in vitro experiments and evaluations. Consequently, designing LPS often fails to achieve high performance, including efficient amplification. For this study, we examined in silico LAMP: a generalized linear model to predict DNA amplification from LPS. Using logistic regression with elastic net regularization, we identified factors that strongly affect LPS design. These factors, combined with domain knowledge for LPS design, led to the creation of LAMP kernel variables that are highly essential for high LAMP reaction. In silico LAMP, constructed using logistic regression with LAMP kernel variables, allows classification and performance prediction of LPS with an area under the curve of 0.86. These results suggest that a high LAMP reaction can be predicted using LAMP kernel variables and generalized linear regression model. Moreover, an LPS with high performance can be constructed without in vitro experimentation. © The Author(s) 2025. Published by Oxford University Press.

关键词： Logistic regression

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Endemism shapes viral ecology and evolution in globally distributed hydrothermal vent ecosystems

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Nature Communications 2025年第1期16卷 1-14页

作者： Langwig, Marguerite V. Koester, Faith Martin, Cody Zhou, Zhichao Joye, Samantha B. Reysenbach, Anna-Louise Anantharaman, Karthik Department of Bacteriology University of Wisconsin-Madison Madison WI United States Freshwater and Marine Sciences Program University of Wisconsin-Madison Madison WI United States Microbiology Doctoral Training Program University of Wisconsin-Madison Madison WI United States Department of Marine Sciences University of Georgia Athens GA United States Department of Biology Portland State University Portland OR United States Department of Integrative Biology University of Wisconsin-Madison Madison WI United States Department of Data Science and AI Wadhwani School of Data Science and AI Indian Institute of Technology Madras TN Chennai India

Viruses are ubiquitous in deep-sea hydrothermal vents, where they influence microbial communities and biogeochemistry. Yet, viral ecology and evolution remain understudied in these environments. Here, we identify 49,962 viruses from 52 globally distributed hydrothermal vent samples (10 plume, 40 deposit, and 2 diffuse flow metagenomes), and reconstruct 5708 viral metagenome-assembled genomes, the majority of which were bacteriophages. Hydrothermal viruses were largely endemic, however, some viruses were shared between geographically separated vents, predominantly between the Lau Basin and Brothers Volcano in the Pacific Ocean. Geographically distant viruses shared proteins related to core functions such as structural proteins, and rarely, proteins of auxiliary functions involved in processes such as fermentation and cobalamin biosynthesis. Common microbial hosts of viruses included members of Campylobacterota, Alpha-, and Gammaproteobacteria in deposits, and Gammaproteobacteria in plumes. Campylobacterota- and Gammaproteobacteria-infecting viruses reflected variations in hydrothermal chemistry and functional redundancy in their predicted microbial hosts, suggesting that hydrothermal geology is a driver of viral ecology and coevolution of viruses and hosts. Our results indicate that viral ecology and evolution in globally distributed hydrothermal vents is shaped by endemism and thus may have increased susceptibility to the negative impacts of deep-sea mining and anthropogenic change in ocean ecosystems. © The Author(s) 2025.

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Genetic keys to microbial gut colonization

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Cell host & microbe 2025年第5期33卷 605-607页

作者： Karthik Anantharaman Cody Martin Department of Bacteriology University of Wisconsin-Madison Madison WI USA Department of Integrative Biology University of Wisconsin-Madison Madison WI USA Department of Data Science and AI Indian Institute of Technology Madras Chennai TN India. Electronic address: karthik@bact.wisc.edu. Microbiology Doctoral Training Program University of Wisconsin-Madison Madison WI USA.

What enables certain microbes to successfully colonize the mammalian gut? In a recent issue of Cell, Liu et al. present a microbial cross-species genomic framework that identifies conserved genetic determinants of gut residency. This study provides a roadmap for developing more effective microbiome-based therapeutics, advancing our understanding of host-microbe interactions.

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Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia

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Environmental Advances 2025年 19卷

作者： Virtriana, Riantini Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Deliar, Albertus Harto, Agung Budi Riqqi, Akhmad Sakti, Anjar Dimara Remote Sensing and GIS Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung 40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung 40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung 40132 Indonesia Geographic Information Science Universitas Pendidikan Indonesia Bandung 40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung 40132 Indonesia

Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic Information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites. © 2024

关键词： GIS Health Facility Machine Learning Remote Sensing

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Hospital Transfers for Hip Fracture Patients Are Associated With Higher Cost and Mortality

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Journal of Arthroplasty 2025年

作者： Hutchison, Catherine E.T. Rollier, Genevieve Li, Hsin-Fang Wells, Erica Workman, Kimberly L. Duwelius, Paul J. Lieberman, Elizabeth G. Oregon Health & Science University Department of Orthopaedics and Rehabilitation Portland Oregon United States Providence St. Joseph Health Surgery & Orthopedic Program Portland Oregon United States Providence St. Joseph Health Center for Cardiovascular Analytics Research & Data Science (CARDS) Portland Oregon United States Orthopedic and Fracture Specialists Portland Oregon United States

Background: Transferring hip fracture patients may increase morbidity and cost. The purpose of this study was to examine time to surgery, outcomes, and cost in patients undergoing operative management of displaced femoral neck fractures (DFNFs), comparing those admitted through the emergency department (ED) and those transferred from outside hospitals. Methods: A retrospective review of surgically managed DFNF patients (age ≥ 65 years) at a single institution between May 29, 2020, and December 29, 2022, was performed. Patients were categorized into two groups: “ED arrivals” and “transfers.” Demographics, time from presentation to surgery, hospital length of stay, discharge disposition, 1-year readmission, reoperation, and mortality, and total cost of care were collected and compared. We identified 459 patients (ED = 422, 92.0%, Transfer = 37, 8.0%). There was no difference in age, sex, body mass index, or American Society of Anesthesiologists status. Results: Median time from ED to operating room was higher in the transfer group (41.6 versus 23.2 hours, P < 0.0001). Transfer and ED patients had similar length of stay (5.9 versus 6.0 days, P = 0.29) and similar rates of discharge to skilled nursing facility (66.1 versus 62.2%, P = 0.39). There was a higher rate of 1-year mortality (27.0 versus 13.5% for ED, P = 0.03) in transfer patients but no difference in 1-year readmission or reoperation. Adjusting for demographics, transfer patients had 2.6 higher odds of 1-year mortality than ED patients (odds ratio = 2.59 (1.13 to 5.94) P = 0.03). The cost of care was higher for transfer patients ($23,521 versus $13,980, P < 0.0001). Conclusions: Transferring DFNF patients was associated with delayed time to surgery, increased 1-year mortality, and higher costs. Transferring patients is a logistical reality for many conditions;however, efforts should be made to provide efficient, safe, and cost-effective care. © 2025 Elsevier Inc.

关键词： femoral neck fracture geriatric fracture health care costs hip fracture transfer of care

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