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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Wang, Po-Xiang Jiang, Ching-Fen Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung City Taiwan

ISBN: (纸本)9783031863226

Sarcopenia is characterized by a persistent reduction in skeletal muscle mass and function, which, if undetected and untreated, can lead to serious consequences such as increased fall risk and long-term immobility. Although sarcopenia has gained attention in recent years, there are currently no effective pharmacological treatments. Muscle loss can only be mitigated in the early stages through resistance training and nutritional interventions. However, precise detection methods used in hospitals are often costly and inaccessible to the general population. Traditional screening methods, like the Sarcopenia Self-Screening Questionnaire (SARC-F), proposed by the Asian Working Group for Sarcopenia, rely on subjective patient reports and lack objectivity. This study aims to address this gap by developing a rapid and convenient sarcopenia screening device that can be used at home or in community settings. Leveraging Internet of Things (IoT) technology, we developed a surface electromyography (sEMG) system to measure the muscle activity of the gastrocnemius, tibialis anterior, and quadriceps during walking and standing. The muscle signals were wirelessly transmitted using the MQTT protocol for backend analysis, enabling real-time gait and muscle strength monitoring. Twenty-eight healthy elderly individuals from the community participated in this study. To validate the effectiveness of the device, traditional sarcopenia-related physical measurements were obtained using standard methods. We first classified participants into four sarcopenia risk levels using the above data. In the meantime, we used deep image gait analysis software (GaitBEST) to classify it into four risk levels. Preliminary results show that these two classification levels had a slight discrepancy. However, the sEMG signals obtained using the developed device reflect the risk level determined by gait pattern, indicating the necessity of further analysis to explore the relation between gait and skeletal muscl

关键词： Electromyography

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Non-contact Respiratory Rate Detection Using 24 GHz Continuous-Wave Radar 6th

Non-contact Respiratory Rate Detection Using 24 GHz Continu...

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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Chen, Yun-Chien Jiang, Ching-Fen Graduate Degree Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung City Taiwan

ISBN: (纸本)9783031863226

Respiratory rate is an important vital sign that reflects health status. In hospital, traditional methods of measuring respiratory rate include manual counting and using a respiratory belt, both of which may pose a risk of contact infection. This study conducted clinical trial in medical ward of hospital, presented a non-contact method based on a 24 GHz continuous-wave radar to detect the displacement of the chest wall during breathing. By processing the radar signal, we were able to obtain the respiratory rate. However, during clinical validation, uncertainty in the daily measurement time of vital sign, which serve as the gold standard, can lead to comparison error. To overcome the interference caused by the issue, this study proposed a validation method that calculates the average of radar-detected respiratory rate over specific time interval and uses the standard deviation of these time interval as an indicator of the stability of the radar-detected respiratory rate value. Statistical results show that the radar-detected respiratory rate derived in this study is consistent with the hospital-measured respiratory rate (p = 0.17) and exhibits significant differences across different subjects (p © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Noninvasive medical procedures

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Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort

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Genomics and Informatics 2025年第1期23卷 1-11页

作者： Ndagijimana, Frank Aimee Rodrigue Park, Taesung Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Department of Statistics Seoul National University Seoul South Korea

Background: Understanding the progression and recovery process of COVID-19 is crucial for guiding public health strategies and developing targeted interventions. This longitudinal cohort study aims to elucidate the dynamics of COVID-19 severity progression and evaluate the impact of underlying health conditions on these transitions, providing critical insights for more effective disease management. Methods: Data from 4549 COVID-19 patients admitted to Seoul National University Boramae Medical Center between February 5th, 2020, and October 30th, 2021, were analyzed using a 5-state continuous-time Markov multistate model. The model estimated instantaneous transition rates between different levels of COVID-19 severity, predicted probabilities of state transitions, and determined hazard ratios associated with underlying comorbidities. Results: The analysis revealed that most patients stabilized in their initial state, with 72.2% of patients with moderate symptoms remaining moderate. Patients with hypertension had a 67.6% higher risk of progressing from moderate to severe, while those with diabetes had an 89.9% higher risk of deteriorating from severe to critical. Although transition rates to death were low early in hospitalization, these comorbidities significantly increased the likelihood of worsening conditions. Conclusion: This study highlights the utility of continuous-time Markov multistate models in assessing COVID-19 severity progression among hospitalized patients. The findings indicate that patients are more likely to recover than to experience worsening conditions. However, hypertension and diabetes significantly increase the risk of severe outcomes, underscoring the importance of managing these conditions in COVID-19 patients. © The Author(s) 2025.

关键词： Comorbidity COVID-19 progression Markov multistate model Severity state Transition intensity

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Histological demonstration of gonad development in the banana shrimp,Fenneropenaeus merguiensis

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Aquaculture and Fisheries 2025年第1期10卷 94-104页

作者： Jirakanit Chimnual Jirawat Saetan Wilaiwan Chotigeat Division of Biological Science Molecular Biotechnology and Bioinformatics ProgramFaculty of SciencePrince of Songkla UniversitySongkhla90110Thailand Division of Health and Applied Sciences Faculty of SciencePrince of Songkla UniversitySongkhla90110Thailand Center for Genomics and Bioinformatics Research Faculty of SciencePrince of Songkla UniversitySongkhla90110Thailand

The genetic diversity of the banana shrimp Fenneropenaeus merguiensis is at risk because of over-harvesting,which consequently reduces food *** endangerment is exacerbated because this species is not commonly cultivated by ***,these factors necessitate conservation of this shrimp species across its natural *** on the migration of primordial germ cells(PGCs)to form a gonad is essential for shrimp preservation techniques such as broodstock preparation,sex differentiation,and germ cell *** this study,histological analysis and in situ hybridization of vasa expression(from embryo to testis development)were used to demonstrate the movement of *** and Eosin staining and in situ hybridization with the VASA probe revealed that the PGCs migrated retrogradely along the midgut,colonizing the area between the hepatopancreas and heart,a region that becomes the genital ridge in the postlarval *** sexual organs appeared at approximately 4 months of *** real-time PCR,the expression of the vasa gene was detected early on postlarval day 7,whereas its abundant expression was detected in the ovaries and testes of adult *** study could help with the identification and monitoring of PGCs or spermatogonia in banana shrimp and facilitate the implementation of other germ cell-relevant techniques in the future.

关键词： Fenneropenaeus merguiensis Primordial germ cell Spermatogonia vasa

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Early detection of Alzheimer's disease using the MEMORIES mnemonic

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Chronic Diseases and Translational Medicine 2025年第1期11卷 22-32页

作者： Valentinus Besin Farizky M.Humardani Faculty of Medicine University of SurabayaSurabayaIndonesia Doctoral Study Program in Medical Science Faculty of Medicine Universitas BrawijayaMalangIndonesia Bioinformatics Research Center Indonesian Bioinformatics and BiomolecularMalangIndonesia

The rising incidence and death rates linked to Alzheimer's disease(AD)highlight an urgent *** screening is celebrated as a significant advancement for its early detection capabilities,pinpointing those at risk before the emergence of ***,the limited availability of these technologies highlights a critical gap in widespread *** review pivots to the potential of presymptomatic clinical assessments as a readily available,economical,and simple strategy for early ***,AD diagnosis relies on the late-stage identification of cognitive deterioration,functional impairments,and neuropsychiatric symptoms,coinciding with advanced brain ***,emerging research identifies early indicators preceding significant degeneration,manifesting years before clinical *** introduce a mnemonic,MEMORIES,to categorize these prodromal:Metabolism changes,Eye/visual impairments,March(refer to gait disturbances),Olfactory dysfunction,Rhythm(blood pressure and heart rate),Insensitivity of the tongue,Ears(hearing loss),and Stool *** these prodromal through clinical examinations provides a valuable strategy for initiating preventative actions against brain *** approach advocates for broadening the screening lens beyond genetic screening to encompass clinical evaluations,enhancing early detection and intervention opportunities for AD.

关键词： Alzheimer's clinical settings prodromal

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Deep learning health space model for ordered responses

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BMC Medical Informatics and Decision Making 2025年第1期25卷 1-10页

作者： Lee, Chanhee Park, Taesung Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Department of Statistics Seoul National University Seoul Korea

As personalized medicine becomes more prevalent, the objective measurement and visualization of an individual’s health status are becoming increasingly crucial. However, as the dimensions of data collected from each individual increase, this task becomes more challenging. The Health Space (HS) model provides a statistical framework for visualizing an individual’s health status on biologically meaningful axes. In our previous study, we developed HS models using statistical models such as logistic regression model (LRM) and the proportional odds model (POM). However, these statistical HS models are limited in their ability to accommodate complex non-linear biological relationships. In order to model complex non-linear biological relationship, we developed deep learning HS models. Specifically, we formulated five distinct deep learning HS models: four standard binary deep neural networks (DNNs) for binary outcomes and one deep ordinal neural network (DONN) that accounts for the ordinality of the dependent variable. We trained these models using 32,140 samples from the Korea National Health and Nutrition Examination Survey (KNHANES) and validated them with data from the Ewha-Boramae cohort (862 samples) and the Korea Association Resource (KARE) project (3,199 samples). The proposed deep learning HS models were compared with the existing statistical HS model based on the POM. Deep learning HS model using DONN demonstrated the best performance in discriminating health status in both the training and external datasets. We developed deep learning HS models to capture complex non-linear biological relationships in HS and compared their performance with our previously best-performing statistical HS model. The deep learning HS models show promise as effective tools for objectively and meaningfully visualizing an individual’s health status. Not applicable.

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368 Uncovering links between innate immunity, DNA repair, and cognitive health in aging populations

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Journal of Clinical and Translational Science 2025年第s1期9卷 113-114页

作者： Jamie Leiriao Hannah Lords Paola Sebastiani Tufts University Candidate Bioinformatics and Computational Biology Program Boston University Clinical and Translational Science Institute Tufts University

Objectives/Goals: Neurodegenerative diseases involve progressive neuronal loss or dysfunction, often due to accumulated damage and impaired repair mechanisms. Our research evaluates the role of innate immune recognition proteins to provide insights into age-related neurodegeneration and cognitive decline. Methods/Study Population: We will utilize transcriptomic data from the Long-Life Family Study (LLFS), a cohort rich in genetic and phenotypic data related to aging and longevity. Our approach includes assessing a set of innate immune recognition proteins, also known as pattern recognition receptors (PRRs) expression across various age groups, focusing on potential correlations with cognitive performance. By analyzing serum transcriptomic profiles, we aim to map changes in expression and DNA repair genes over time, evaluating their connection to cognitive health and neurodegeneration in aging populations. Results/Anticipated Results: We anticipate that the expression of some PRRs will increase with age and correlate with cognitive decline, suggesting a role in age-related neurodegeneration. We also expect a decrease in DNA repair pathway gene expression in older age groups, contrasting with an increase in genes involved in endogenous DNA detection. These results will reveal how PRRs may function as neuroprotective factors and how their expression changes may relate to the decline in DNA repair processes with age, providing a better understanding of innate immune recognition in cognitive health. Discussion/Significance of Impact: This study will reveal the role of PRRs in aging and neurodegeneration, potentially establishing them as a key player in neuronal protection. Findings may guide future research into therapeutic strategies targeting them for Alzheimer’s and other age-related neurodegenerative diseases.

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Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction

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Journal of Cheminformatics 2025年第1期17卷 1-16页

作者： Ha, Sumin Bang, Dongmin Kim, Sun Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea AIGENDRUG Co. Ltd. Seoul Republic of Korea Department of Computer Science and Engineering Seoul National University Seoul Republic of Korea

Toxicity is a critical hurdle in drug development, often causing the late-stage failure of promising compounds. Existing computational prediction models often focus on single-organ toxicity. However, avoiding toxicity of an organ, such as reducing gastrointestinal side effects, may inadvertently lead to toxicity in another organ, as seen in the real case of rofecoxib, which was withdrawn due to increased cardiovascular risks. Thus, simultaneous prediction of multi-organ toxicity is a desirable but challenging task. The main challenges are (1) the variability of substructures that contribute to toxicity of different organs, (2) insufficient power of molecular representations in diverse perspectives, and (3) explainability of prediction results especially in terms of substructures or potential toxicophores. To address these challenges with multiple strategies, we developed FATE-Tox, a novel multi-view deep learning framework for multi-organ toxicity prediction. For variability of substructures, we used three fragmentation methods such as BRICS, Bemis-Murcko scaffolds, and RDKit Functional Groups to formulate fragment-level graphs so that diverse substructures can be used to identify toxicity for different organs. For insufficient power of molecular representations, we used molecular representations in both 2D and 3D perspectives. For explainability, our fragment attention transformer identifies potential 3D toxicophores using attention coefficients. Scientific contribution: Our framework achieved significant improvements in prediction performance, with up to 3.01% gains over prior baseline methods on toxicity benchmark datasets from MoleculeNet (BBBP, SIDER, ClinTox) and TDC (DILI, Skin Reaction, Carcinogens, and hERG), while the multi-task learning approach further enhanced performance by up to 1.44% compared to the single-task learning framework that had already surpassed these baselines. Additionally, attention visualization aligning with literature contributes to

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Methacrylic acid/Tween 80 engineered amorphous calcium phosphate as an effective bioactive reinforcing nanofillers in dental adhesive

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Dental Materials Journal 2025年第2期44卷 190-200页

作者： Maimansomsuk, Siripatsorn Teangrom, Paremai Teanchai, Chayada Sinthuvanich, Chomdao Ruengket, Pakorn Attarataya, Jakrada Akkarachaneeyakorn, Khrongkhwan Department of Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Center of Excellence for Innovation in Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Research Center Faculty of Dentistry Mahidol University Bangkok10400 Thailand Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand Faculty of Science Kasetsart University Bangkok Thailand Genetic Engineering and Bioinformatics Program Graduate School Kasetsart University Bangkok Thailand Nakhon Ratchasima30000 Thailand

Bioactive fillers in dental adhesives are designed to release beneficial ions, such as calcium and phosphate, to help in remineralization process, and preventing secondary caries. Their incorporation also aims to improve the longevity of dental restorations. Herein, CaP nanoparticles were synthesized through a water-in-oil emulsion method using Tween80 as a surfactant and methacrylic acid (MAA). MAA officiated as a co-surfactant and reduced the size of CaP nanoparticle, including improved compatibility with the components of dental adhesive. The incorporation of 2% T80/CaP/MAA nanofillers into dental adhesive demonstrated increased efficiency, as evidenced by a higher degree of conversion (%DC) and greater micro-tensile bond strength (μTBS). More importantly, T80/CaP/ MAA nanofillers were able to induce remineralization process in the dental structure, which was confirmed by EDX mapping and IR imaging. Finally, a cytotoxicity assessment of the dental adhesive containing T80/CaP/MAA showed no significant adverse effects on human fibroblast cells. © 2025, Japanese Society for Dental Materials and Devices. All rights reserved.

关键词： Stem cells

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Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics

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PLoS Computational Biology 2025年第4期21卷 e1012962页

作者： Morin, Alexander Chu, Ching Pan Pavlidis, Paul Michael Smith Laboratories University of British Columbia VancouverBC Canada Department of Psychiatry University of British Columbia VancouverBC Canada Graduate Program in Bioinformatics University of British Columbia VancouverBC Canada

The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR’s coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use. © 2025 Morin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Mammals

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