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Brain age estimation:premise,promise,and problems

Neural Regeneration Research 2025年第8期20卷 2313-2314页

作者： Jarrad Perron Ji Hyun Ko Graduate Program in Biomedical Engineering Price Faculty of EngineeringUniversity of ManitobaWinnipegMBCanada PrairieNeuro Research Centre Kleysen Institute for Advanced MedicineHealth Sciences CentreWinnipegMBCanada Department of Human Anatomy and Cell Science Rady Faculty of Health SciencesUniversity of ManitobaWinnipegMBCanada

Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North *** old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.

关键词： estimation providing birth

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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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Association of computed variables with anthropometric indices in north indian children exposed to Bisphenol-A and heavy metals

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Discover Epidemics 2025年第1期2卷 1-17页

作者： Malik, Vivek Singh Attri, Savita Verma Dayal, Devi Ravindra, Khaiwal Department of Community Medicine and School of Public Health Post Graduate Institute of Medical Education and Research (PGIMER) Chandigarh India Department of Pediatrics Post Graduate Institute of Medical Education and Research (PGIMER) Chandigarh India Department of Telemedicine and Biomedical Informatics All India Institute of Medical Sciences (AIIMS) Rishikesh India

Obesity and underweight-associated environmental risk factors pose significant health challenges for the public, especially among children at an early stage of life. This study evaluates the association and potential role of combined exposure to Bisphenol-A (BPA) and heavy metals with anthropometric indices among North Indian children. This study also demonstrates the possibility of reverse causality, where obesity or underweight may result from BPA and heavy metals exposure among children. A total of 301 children were enrolled, grouped into obese, non-obese, and underweight. Urinary BPA and blood heavy metal levels were measured using LC–MS/MS and ICP-MS. Both quantitative variables, BPA and metals, were combined using the computed feature of SPSS, followed by linear regression analysis to identify the association with anthropometric indices (height, weight, BMI, waist and hip circumference, waist-to-stature ratio, and waist-to-hip ratio). A positive association was found among obese children for computed variables of combined exposure to BPA*Zn (WB) and a negative association with BPA*Fe (WB) for weight, height, and BMI. Similarly, a positive association was observed among underweight children for the computed variable of BPA*U (WB) with weight, height, and BMI. Significant negative associations were also observed for the BPA*Mg (WB) and BPA*Hg (WB) with WHR among obese and BPA*Cu (WB) with weight and BPA*Hg (WB) with WHR among underweight children. This study found a positive association for BPA*Se (WB) and BPA*Cd (S) with WC and WHR, respectively, among non-obese children. Our analysis suggests that BPA, combined with metals, is associated with both obesity and underweight. Both BPA and heavy metals are endocrine disruptive pollutants that may affect the metabolic pathways by substituting vital metals and inducing oxidative stress. This extends the scope for future research to address obesity and underweight among children. However, these results should be inter

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Effects of stepping practice and functional electrical stmulation on neuromotor excitability in able bodied young adults

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Experimental Brain Research 2025年第6期243卷 1-11页

作者： Spencer, Jacob Aloba, Fisayo K. Slusarenko, Alexandra Leone, Taylor Patel, Jay Eyvazzadeh, Lily Lynch, Anna-Lee Borich, Michael R. Kesar, Trisha M. Division of Physical Therapy Department of Rehabilitation Medicine Emory University Atlanta GA United States School of Biological Sciences Georgia Institute of Technology Atlanta GA United States Neuroscience Graduate Program Graduate Division of Biological and Biomedical Sciences Emory University Atlanta GA United States Neuroscience and Behavioral Biology Undergraduate Program College of Arts and Sciences Emory University Atlanta GA United States Emory Rehabilitation Hospital 1441 Clifton Rd NE Room RG44 Atlanta 30322 GA United States

The neural processes underlying gait retraining interventions that combine stepping practice with electrical stimulation are poorly understood. The purpose of this study was to compare acute changes in corticomotor excitability measured by motor evoked potential (MEP) amplitude, monosynaptic stretch reflex excitability measured by H/M ratio, and propulsive force generation during gait measured by anterior ground reaction forces (AGRF), induced by 30-minutes of fast walking (Fast) and 30-minutes of fast walking with functional electrical stimulation of the ankle plantarflexors and dorsiflexors (FastFES) in 14 able-bodied young adults. Our results showed that FastFES, but not Fast elicited a significant acute decrease in tibialis anterior MEP amplitude (p =.01). Furthermore, the practice-induced acute decrease in tibialis anterior MEP amplitudes was significantly larger for FastFES than Fast (p =.04). FastFES also elicited a statistically significant increase in the AGRF in the tested limb (p =.01), which was significantly larger than the change induced by Fast (p =.04). Additionally, baseline soleus MEP amplitude was positively correlated with within-session change in AGRF (p =.04, r² = 0.16). Acute decrease in tibialis anterior MEP amplitude for the FastFES condition relative to the Fast condition may be caused by fatigue, while the greater increase in AGRF for the FastFES condition signify that stepping practice facilitated alterations of gait patterns. More research is needed to confirm neural mechanisms and investigate the acute as well as long-term effects of Fast and FastFES on clinical populations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Corticospinal tract Functional electrical stimulation Gait training Neuroplasticity Walking exercise

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Cytocompatible and antibacterial Fe-, Cu- and Zn-substituted calcium hydroxyapatite materials for skin applications

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Ceramics International 2025年第9期51卷 11286-11296页

作者： Lukaviciute, L. Lukowiak, A. Stankeviciute, Z. Junka, A. Mortimer, M. Zarkov, A. Yang, J.-C. Ganceviciene, R. Kareiva, A. Faculty of Chemistry and Geosciences Vilnius University Naugarduko st. 24 Vilnius03225 Lithuania Institute of Low Temperature and Structure Research Polish Academy of Sciences Okolna 2 Wroclaw50422 Poland Department of Pharmaceutical Microbiology and Parasitology Wroclaw Medical University Borowska 211A Wroclaw50556 Poland Laboratory of Environmental Toxicology National Institute of Chemical Physics and Biophysics Akadeemia tee 23 Tallinn12618 Estonia Graduate Institute of Nanomedicine and Medical Engineering International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Research Center of Biomedical Device Research Center of Digital Oral Science and Technology Taipei Medical University Taiwan Faculty of Medicine Vilnius University M.K. Ciurlionio st. 21 Vilnius03101 Lithuania

Calcium hydroxyapatite (C10(PO4)6(OH)2;CHAp) and metal-substituted samples (Fe:CHAp, Cu:CHAp, and Zn:CHAp) were synthesized using a recently developed method. This method involved the wet precipitation of amorphous calcium phosphate (ACP), followed by solid-state reaction synthesis of α-tricalcium phosphate (α-TCP), and finally hydrothermal synthesis of CHAp. The effect of Fe3+, Cu2+, and Zn2+ substitution on the phase purity and antimicrobial properties of CHAp are summarized in this study. Notably, it highlights the phase purity of CHAp and Zn:CHAp (in the presence of Fe3+ and Cu2+ ions, the α- and β-TCP phases have formed) as well as the antimicrobial activity of Zn:CHAp and Cu:CHAp against Staphylococcus aureus and Candida albicans. The synthesized nanosized CHAp, Fe:CHAp, Cu:CHAp, and Zn:CHAp samples were used as additives in the preparation of skin creams. In vitro biological studies of the resulting skin creams demonstrated their antimicrobial activity against Pseudomonas aeruginosa, and showed a reduction in osteoblasts cells viability of in the range of 20–40 %. The addition of zinc enhances antimicrobial efficacy but reduces biocompatibility, while copper maintains both high antimicrobial activity and biocompatibility, making it more suitable for the development of medical or cosmetic materials. Dermal creams containing CHAp with copper and iron ions also show significant antimicrobial activity while preserving good cell viability. These results highlight the importance of optimizing the balance between antimicrobial effectiveness and biocompatibility in these materials. © 2024

关键词： Biocompatibility

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SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for Highly Constrained Environments

SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Med...

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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024

作者： Kong, Youngbin Kim, Kwangtai Jeong, Seoi Lee, Kyu Eun Kong, Hyoun-Joong Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Seoul National University Hospital Seoul Korea Republic of Department of Surgery Seoul National University Hospital and College of Medicine Seoul Korea Republic of Department of Medicine Seoul National University College of Medicine Seoul Korea Republic of Innovative Medical Technology Research Institute Seoul National University Hospital Seoul Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University College of Medicine Seoul Korea Republic of

ISBN: (纸本)9783031818530

Medical image segmentation is a crucial step for accurate diagnosis and treatment planning, as it provides quantitative information about anatomical structures and pathological lesions in various clinical scenarios. However, the existing methodologies have limitations in terms of their generalizability and computational efficiency. In this study, we propose SwiftMedSAM, an ultra-lightweight prompt-based general model, to enable efficient medical image segmentation even in resource-constrained environments. Based on LiteMedSAM, we significantly reduced the model size and computational complexity through the hyperparameter optimization of the image encoder and mask decoder components. SwiftMedSAM showed remarkable performance across various imaging modalities enabling real-time inference in resource-limited computing environments. It achieved a validation score of 0.75 demonstrating that SwiftMedSAM outperformed the existing methodologies in terms of the trade-off between accuracy and efficiency. Owing to its unprecedented generalizability and low computational cost, SwiftMedSAM is expected to enable high-quality medical image analysis in resource-constrained settings, thereby contributing to advancements in precision medicine and telemedicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms

arXiv

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arXiv 2025年

作者： Sufriyana, Herdiantri Su, Emily Chia-Yu Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University 155 Section 2 Linong Street Beitou District Taipei112304 Taiwan

Background: Current nomogram can only be created for regression algorithm. Providing nomogram for any machine learning (ML) algorithms may accelerate model deployment in clinical settings or improve model availability. We developed an R package and web application to construct nomogram with model explainability of any ML algorithms. Methods: We formulated a function to transform an ML prediction model into a nomogram, requiring datasets with: (1) all possible combinations of predictor values;(2) the corresponding outputs of the model;and (3) the corresponding explainability values for each predictor (optional). Web application was also created. Results: Our R package could create 5 types of nomograms for categorical predictors and binary outcome without probability (1), categorical predictors and binary outcome with probability (2) or continuous outcome (3), and categorical with single numerical predictors and binary outcome with probability (4) or continuous outcome (5). Respectively, the first and remaining types optimally allowed maximum 15 and 5 predictors with maximum 3,200 combinations. Web application is provided with such limits. The explainability values were possible for types 2 to 5. Conclusions: Our R package and web application could construct nomogram with model explainability of any ML algorithms using a fair number of predictors. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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Predictive analysis of COVID-19 occurrence and vaccination impacts across the 50 US states

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Computers in Biology and medicine 2025年 185卷 109493-109493页

作者： Rayguru, Chinmayee Husnayain, Atina Chiu, Hua-Sheng Sumazin, Pavel Su, Emily Chia-Yu Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Public Health Monash University Indonesia Department of Pediatrics Baylor College of Medicine Texas Children's Hospital Cancer Center HoustonTX United States Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan

Objective: This study aimed to outline a machine learning model to assess the effectiveness of vaccination in COVID-19 confirmed cases and fatalities. The proposed model was evaluated using external validation to ensure optimal protection of vaccinated populations, distinguishing between males and females. Methods: The data from the Centers for Disease Control and Prevention (CDC) in the US, collected between 2021 and 2023, were preprocessed through merging and imputation. A deep learning long short-term memory (LSTM) model was developed to analyze the effectiveness of vaccination in predicting COVID-19 cases and fatalities. The model, which was validated internally and externally, examined the impact of vaccination according to sex. The performance was assessed against current state-of-the-art models, with the LSTM model exhibiting lower root mean square error (RMSE) values. Results: We performed intra-, inter-, and external-validation analyses. First, one- and two-dose vaccinations significantly reduced the number of COVID-19 cases and mortality in highly affected states. Second, in the inter-model analysis, the LSTM outperformed the autoregressive integrated moving average (ARIMA) model in predicting cases and deaths, yielding superior results for Texas, California, and Florida. Third, with external validation, our LSTM model effectively predicted vaccination impacts regardless of sex. Conclusions: Our study demonstrates the effectiveness of COVID-19 vaccination, showing that full vaccination significantly reduced the number of confirmed cases and deaths, influencing future public health policies. © 2024 Elsevier Ltd

关键词： COVID-19

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Deciphering the toxic effects of polystyrene nanoparticles on erythropoiesis at single-cell resolution

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Zoological Research 2025年第1期46卷 165-176页

作者： Eun Jung Kwon Hyeon Mi Sung Hansong Lee Soyul Ahn Yejin Kim Chae Rin Lee Kihun Kim Kyungjae Myung Won Kyu Kim Dokyoung Kim Sanghwa Jeong Chang-Kyu Oh Yun Hak Kim Medical Research Institute Pusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Institute for Future Earth Pusan National UniversityBusan 46241Republic of Korea Department of Convergence Medical Sciences School of MedicinePusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Department of Biomedical Informatics School of MedicinePusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Department of Anatomy School of MedicinePusan National UniversityYangsanGyeongsangnam-do 50612Republic of Korea Center for Genomic Integrity Institute for Basic ScienceUlsan 689-798Republic of Korea Department of Biomedical Engineering College of Information-Bio Convergence EngineeringUlsan National Institute of Science and TechnologyUlsan 44919Republic of Korea Natural Product Research Center Korea Institute of Science and Technology(KIST)GangneungGangwon-do 25451Republic of Korea Department of Convergence Medicine Yonsei University Wonju College of MedicineWonjuGangwon-do 26426Republic of Korea Natural Product Applied Science KIST SchoolUniversity of Science and Technology(UST)GangneungGangwon-do 25451Republic of Korea Department of Biomedical Science Graduate SchoolKyung Hee UniversitySeoul 02447Republic of Korea Department of Anatomy and Neurobiology College of MedicineKyung Hee UniversitySeoul 02447Republic of Korea Department of Precision Medicine Graduate SchoolKyung Hee UniversitySeoul 02447Republic of Korea Center for Converging Humanities Kyung Hee UniversitySeoul 02447Republic of Korea KHU-KIST Department of Converging Science and Technology Kyung Hee UniversitySeoul 02447Republic of Korea UC San Diego Materials Research Science and Engineering Center 9500 Gilman DriveLa JollaCalifornia 92093USA Medical Research Center for Bioreaction to Reactive Oxygen Species and Biomedical Science Institute Core Research Institute(CRI)Kyung Hee UniversitySeoul 02447Republic of Korea Center for Brain Technology Brain Science Institute

Polystyrene nanoparticles pose significant toxicological risks to aquatic ecosystems,yet their impact on zebrafish(Danio rerio)embryonic development,particularly erythropoiesis,remains *** study used single-cell RNA sequencing to comprehensively evaluate the effects of polystyrene nanoparticle exposure on erythropoiesis in zebrafish *** vivo validation experiments corroborated the transcriptomic findings,revealing that polystyrene nanoparticle exposure disrupted erythrocyte differentiation,as evidenced by the decrease in mature erythrocytes and concomitant increase in immature ***,impaired heme synthesis further contributed to the diminished erythrocyte *** findings underscore the toxic effects of polystyrene nanoparticles on hematopoietic processes,highlighting their potential to compromise organismal health in aquatic environments.

关键词： Polystyrene nanoparticles Zebrafish embryos Single-cell RNA sequencing Erythropoiesis

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PSYCHE: A Multi-faceted Patient Simulation Framework for Evaluation of Psychiatric Assessment Conversational Agents

arXiv

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arXiv 2025年

作者： Lee, Jingoo Lim, Kyungho Jung, Young-Chul Kim, Byung-Hoon Department of Biomedical Systems Informatics Yonsei University College of Medicine Seoul Korea Republic of Institute of Behavioral Science in Medicine Yonsei University College of Medicine Seoul Korea Republic of Department of Medicine Yonsei University Graduate School Seoul Korea Republic of Department of Psychiatry Yonsei University College of Medicine Seoul Korea Republic of

Recent advances in large language models (LLMs) have accelerated the development of conversational agents capable of generating human-like responses. Since psychiatric assessments typically involve complex conversational interactions between psychiatrists and patients, there is growing interest in developing LLM-based psychiatric assessment conversational agents (PACAs) that aim to simulate the role of psychiatrists in clinical evaluations. However, standardized methods for benchmarking the clinical appropriateness of PACAs' interaction with patients still remain underexplored. Here, we propose PSYCHE, a novel framework designed to enable the 1) clinically relevant, 2) ethically safe, 3) cost-efficient, and 4) quantitative evaluation of PACAs. This is achieved by simulating psychiatric patients based on a multi-faceted psychiatric construct that defines the simulated patients' profiles, histories, and behaviors, which PACAs are expected to assess. We validate the effectiveness of PSYCHE through a study with 10 board-certified psychiatrists, supported by an in-depth analysis of the simulated patient utterances. Copyright © 2025, The Authors. All rights reserved.

关键词： Chatbots

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