Pub Date : 2026-01-19DOI: 10.1016/j.compbiomed.2026.111469
S. Jayasrilakshmi, Ansuman Mahapatra
Generative AI, an artificial intelligence, significantly transforms the healthcare sector. Recent breakthroughs in Generative AI include the use of language models and leveraging modern pre-trained Transformer models such as ChatGPT, Bard, LLaMA, DALL-E, and Bing. In medical applications, the advent of Large Language Models (LLMs) is a significant tool for predicting diseases, identifying risk factors, and enhancing diagnostic accuracy by analyzing a massive volume of unevenly distributed medical resources. This study provides a comprehensive review of existing literature on the use of LLMs in healthcare. It elucidates the ‘status quo’ of language models for general readers, healthcare professionals, and researchers. Specifically, this study investigates the capabilities of LLMs, including the transformation of healthcare consultation, enhancement of patient management and treatment, evolution of medical education, optimal resource utilization, and advancement of clinical research. The article organizes the literature based on human organs that will help readers quickly find relevant LLM applications for specific medical fields. The outcome of this survey will help medical professionals, researchers, and the healthcare industry understand the benefits, challenges, observed limitations, future challenges and applications of LLMs in healthcare.
{"title":"Generative AI in medicine: A thorough examination of applications, challenges, and future perspectives","authors":"S. Jayasrilakshmi, Ansuman Mahapatra","doi":"10.1016/j.compbiomed.2026.111469","DOIUrl":"10.1016/j.compbiomed.2026.111469","url":null,"abstract":"<div><div>Generative AI, an artificial intelligence, significantly transforms the healthcare sector. Recent breakthroughs in Generative AI include the use of language models and leveraging modern pre-trained Transformer models such as ChatGPT, Bard, LLaMA, DALL-E, and Bing. In medical applications, the advent of Large Language Models (LLMs) is a significant tool for predicting diseases, identifying risk factors, and enhancing diagnostic accuracy by analyzing a massive volume of unevenly distributed medical resources. This study provides a comprehensive review of existing literature on the use of LLMs in healthcare. It elucidates the ‘status quo’ of language models for general readers, healthcare professionals, and researchers. Specifically, this study investigates the capabilities of LLMs, including the transformation of healthcare consultation, enhancement of patient management and treatment, evolution of medical education, optimal resource utilization, and advancement of clinical research. The article organizes the literature based on human organs that will help readers quickly find relevant LLM applications for specific medical fields. The outcome of this survey will help medical professionals, researchers, and the healthcare industry understand the benefits, challenges, observed limitations, future challenges and applications of LLMs in healthcare.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111469"},"PeriodicalIF":6.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146008895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1016/j.compbiomed.2026.111466
Kwang Ho Park , Younghee Lee , Wei Ding , Kwang Sun Ryu , Keun Ho Ryu
Accurate classification of hematopoietic cancer subtypes remains challenging due to the multipotent nature of hematopoietic cells and the absence of definitive genetic markers. To address this, we propose a Transformer-based Autoencoder that captures compact and biologically informative embeddings from gene expression data. Specifically, our method employs multi-head self-attention in the encoder to learn complex nonlinear interactions among genes, with a reconstruction decoder that enforces biological feature retention. We benchmarked our approach against four widely-used feature extraction methods—Principal Component Analysis, Non-negative Matrix Factorization, Autoencoder, and Variational Autoencoder—using transcriptomic data from five hematopoietic cancer subtypes in The Cancer Genome Atlas, totaling 2452 samples. Data were split 60:20:20 into training, validation, and test sets with stratification, and feature-extractor hyperparameters were chosen on the validation set. Each method produced 100-dimensional feature vectors, subsequently evaluated using eight multi-class classifiers: Light Gradient Boosting Machine, Extreme Gradient Boosting, Logistic Regression, Random Forest, Decision Tree, Support Vector Machine, and Neural Networks. On the independent test set, the Transformer-based Autoencoder embeddings combined with Light Gradient Boosting Machine achieved F1-score: 0.969, accuracy: 0.986, precision: 0.975, recall: 0.964, specificity: 0.996, G-mean: 0.980, and balanced accuracy: 0.954. For context, we additionally included a supervised tabular Transformer (FT-Transformer) as a reference; while strong, it is not directly comparable to our unsupervised feature extractor. To enhance interpretability and clinical relevance, we applied Shapley Additive exPlanations to identify the twenty most influential genes contributing to subtype discrimination. This analysis revealed key biomarkers related to endoplasmic reticulum function, antigen processing, and ribonucleic acid regulation. These findings demonstrate that transformer-based unsupervised feature extraction substantially improves predictive accuracy and yields valuable biological insights for complex hematologic malignancies. Overall, the study supports attention-driven representation learning for tabular biomedical data and motivates future work in generative/self-supervised representations for gene expression.
{"title":"Transformer-based feature extraction approach for hematopoietic cancer subtype classification","authors":"Kwang Ho Park , Younghee Lee , Wei Ding , Kwang Sun Ryu , Keun Ho Ryu","doi":"10.1016/j.compbiomed.2026.111466","DOIUrl":"10.1016/j.compbiomed.2026.111466","url":null,"abstract":"<div><div>Accurate classification of hematopoietic cancer subtypes remains challenging due to the multipotent nature of hematopoietic cells and the absence of definitive genetic markers. To address this, we propose a Transformer-based Autoencoder that captures compact and biologically informative embeddings from gene expression data. Specifically, our method employs multi-head self-attention in the encoder to learn complex nonlinear interactions among genes, with a reconstruction decoder that enforces biological feature retention. We benchmarked our approach against four widely-used feature extraction methods—Principal Component Analysis, Non-negative Matrix Factorization, Autoencoder, and Variational Autoencoder—using transcriptomic data from five hematopoietic cancer subtypes in The Cancer Genome Atlas, totaling 2452 samples. Data were split 60:20:20 into training, validation, and test sets with stratification, and feature-extractor hyperparameters were chosen on the validation set<strong>.</strong> Each method produced 100-dimensional feature vectors, subsequently evaluated using eight multi-class classifiers: Light Gradient Boosting Machine, Extreme Gradient Boosting, Logistic Regression, Random Forest, Decision Tree, Support Vector Machine, and Neural Networks. On the independent test set, the Transformer-based Autoencoder embeddings combined with Light Gradient Boosting Machine achieved F1-score: 0.969, accuracy: 0.986, precision: 0.975, recall: 0.964, specificity: 0.996, G-mean: 0.980, and balanced accuracy: 0.954. For context, we additionally included a supervised tabular Transformer (FT-Transformer) as a reference; while strong, it is not directly comparable to our unsupervised feature extractor. To enhance interpretability and clinical relevance, we applied Shapley Additive exPlanations to identify the twenty most influential genes contributing to subtype discrimination. This analysis revealed key biomarkers related to endoplasmic reticulum function, antigen processing, and ribonucleic acid regulation. These findings demonstrate that transformer-based unsupervised feature extraction substantially improves predictive accuracy and yields valuable biological insights for complex hematologic malignancies. Overall, the study supports attention-driven representation learning for tabular biomedical data and motivates future work in generative/self-supervised representations for gene expression.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111466"},"PeriodicalIF":6.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1016/j.compbiomed.2026.111475
Shirley Ferraz Crispilho , Paulo Cesar Duarte Junior , Martin Poulsen Kessler , Rudolf Huebner , Altibano Ortenzi
Accurate blending of oxygen and air in pediatric and neonatal respiratory support depends on compact connectors that promote efficient mixing without generating excessive pressure drop or dead volume. In current clinical practice, commercially available T-shaped connectors are often used as passive mixers, but their internal geometry was not originally optimized for this purpose. In this work, the original commercial connector (Geometry A), an in-house modified multi-part connector incorporating a static insert (Geometry B), and a new monolithic helical static-mixer insert (Geometry C) were evaluated under identical flow conditions. Three-dimensional Reynolds-averaged Navier–Stokes computational fluid dynamics simulations were performed to represent oxygen–nitrogen mixing in high-flow nasal cannula circuits, considering realistic flow rates and boundary conditions. For each geometry, mixture quality at the outlet was assessed from the spatial distribution of species mass fraction, hydraulic performance was quantified by the device pressure drop, and residence-time behavior for the helical insert was obtained from a transient scalar-pulse simulation. Geometry B improved outlet homogeneity relative to the commercial connector but required several assembled parts, which complicates handling and sterilization. Geometry C, designed as a monolithic helical static mixer, produced more uniform gas mixing than both previous configurations while maintaining pressure drops within ranges compatible with pediatric and neonatal use. These results indicate that the proposed helical insert has the potential to replace the current multi-part in-house adaptation and to offer a more effective alternative to standard commercial connectors when implemented as a monolithic medical-grade component.
{"title":"Helical static-mixer insert for pediatric and neonatal gas blending: RANS-CFD comparison of commercial and in-house monolithic designs","authors":"Shirley Ferraz Crispilho , Paulo Cesar Duarte Junior , Martin Poulsen Kessler , Rudolf Huebner , Altibano Ortenzi","doi":"10.1016/j.compbiomed.2026.111475","DOIUrl":"10.1016/j.compbiomed.2026.111475","url":null,"abstract":"<div><div>Accurate blending of oxygen and air in pediatric and neonatal respiratory support depends on compact connectors that promote efficient mixing without generating excessive pressure drop or dead volume. In current clinical practice, commercially available T-shaped connectors are often used as passive mixers, but their internal geometry was not originally optimized for this purpose. In this work, the original commercial connector (Geometry A), an in-house modified multi-part connector incorporating a static insert (Geometry B), and a new monolithic helical static-mixer insert (Geometry C) were evaluated under identical flow conditions. Three-dimensional Reynolds-averaged Navier–Stokes computational fluid dynamics simulations were performed to represent oxygen–nitrogen mixing in high-flow nasal cannula circuits, considering realistic flow rates and boundary conditions. For each geometry, mixture quality at the outlet was assessed from the spatial distribution of species mass fraction, hydraulic performance was quantified by the device pressure drop, and residence-time behavior for the helical insert was obtained from a transient scalar-pulse simulation. Geometry B improved outlet homogeneity relative to the commercial connector but required several assembled parts, which complicates handling and sterilization. Geometry C, designed as a monolithic helical static mixer, produced more uniform gas mixing than both previous configurations while maintaining pressure drops within ranges compatible with pediatric and neonatal use. These results indicate that the proposed helical insert has the potential to replace the current multi-part in-house adaptation and to offer a more effective alternative to standard commercial connectors when implemented as a monolithic medical-grade component.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111475"},"PeriodicalIF":6.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146008969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1016/j.compbiomed.2026.111468
Eugenio Peixoto Junior , Felipe Cordeiro de Sousa , Junxin Chen , David Camacho , Stephen Rathinaraj Benjamin , Victor Hugo C. de Albuquerque
Parkinson’s disease (PD) remains one of the most prevalent neurodegenerative disorders, where delays in diagnosis compromise therapeutic outcomes and increase healthcare costs. Conventional unimodal approaches, based on voice, sensors, or imaging, face critical limitations, including small datasets, lack of reproducibility, and high infrastructure demands. To address these challenges, the proposed multimodal agent-based architecture integrates medical language models, audio signals, and neuroimaging, and is supported by data–machine learning pipelines and an edge–cloud infrastructure. The system leverages ensemble learning, large and vision language models, and Retrieval-Augmented Generation (RAG) to enhance clinical decision support. The transparency of the model was supported by explainability techniques (SHapley Additive exPlanations, permutation importance, partial dependence, and individual conditional expectation), which highlighted the main audio and sensor variables responsible for the predictions. Experimental evaluation confirmed the effectiveness of multimodal fusion. When integrated, the architecture achieved robust performance, with an accuracy of 0.86, an F1-score above 0.88, ROC-AUC greater than 0.93, and both sensitivity and specificity above 0.89. Calibration and hypothesis tests were validated by a low Brier score of 0.205 and an Expected Calibration Error of 0.151, while Decision Curve Analysis confirmed clinical relevance by minimizing false negatives, critical for early screening, and reducing redundant interventions. Multimodal fusion produced accurate, well-calibrated, and interpretable risk estimates for PD screening; larger prospective studies and cost-effectiveness analyses are needed to consolidate clinical applicability.
{"title":"Multimodal diagnosis of Parkinson’s disease with an internet-based collaborative agent architecture of medical language models","authors":"Eugenio Peixoto Junior , Felipe Cordeiro de Sousa , Junxin Chen , David Camacho , Stephen Rathinaraj Benjamin , Victor Hugo C. de Albuquerque","doi":"10.1016/j.compbiomed.2026.111468","DOIUrl":"10.1016/j.compbiomed.2026.111468","url":null,"abstract":"<div><div>Parkinson’s disease (PD) remains one of the most prevalent neurodegenerative disorders, where delays in diagnosis compromise therapeutic outcomes and increase healthcare costs. Conventional unimodal approaches, based on voice, sensors, or imaging, face critical limitations, including small datasets, lack of reproducibility, and high infrastructure demands. To address these challenges, the proposed multimodal agent-based architecture integrates medical language models, audio signals, and neuroimaging, and is supported by data–machine learning pipelines and an edge–cloud infrastructure. The system leverages ensemble learning, large and vision language models, and Retrieval-Augmented Generation (RAG) to enhance clinical decision support. The transparency of the model was supported by explainability techniques (SHapley Additive exPlanations, permutation importance, partial dependence, and individual conditional expectation), which highlighted the main audio and sensor variables responsible for the predictions. Experimental evaluation confirmed the effectiveness of multimodal fusion. When integrated, the architecture achieved robust performance, with an accuracy of 0.86, an F1-score above 0.88, ROC-AUC greater than 0.93, and both sensitivity and specificity above 0.89. Calibration and hypothesis tests were validated by a low Brier score of 0.205 and an Expected Calibration Error of 0.151, while Decision Curve Analysis confirmed clinical relevance by minimizing false negatives, critical for early screening, and reducing redundant interventions. Multimodal fusion produced accurate, well-calibrated, and interpretable risk estimates for PD screening; larger prospective studies and cost-effectiveness analyses are needed to consolidate clinical applicability.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111468"},"PeriodicalIF":6.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1016/j.compbiomed.2026.111482
Farhana Islam , Mostafa Kamal , Shoeb Ahmad , Masum Shahriar , Fariya Islam Rodru , Md. Nazmul Hasan , Md. Nazmul Hasan Zilani , Md. Ataur Rahman , Shahad Saif Khandker , Saquiba Yesmine
Chronic kidney disease (CKD) is a progressive, irreversible disorder associated with renal dysfunction, inflammation, and oxidative stress. Given the limitations of current therapies, this study assessed the renal curative effects of Nypa fruticans ethyl acetate leaf extract (EaNFL) in a gentamicin-induced nephrotoxicity rat model. GC‒MS and HPLC analyses identified 23 bioactive compounds in EaNFL, including rosmarinic acid, quercetin, and (−)-epicatechin, which were selected based on ADMET profiling, Lipinski's rule, and DFT analysis. These compounds were further investigated through computational studies against two renal targets: the AT1 receptor (PDB ID: 4YAY) and SGLT2 (PDB ID: 7VSI). Treatment with EaNFL, particularly at 400 mg/kg body weight and in combination therapy, significantly improved renal function and normalized biochemical and hematological parameters, likely due to its potent antioxidant and anti-inflammatory properties. Histopathological data supported these findings, showing reduced tubular necrosis, glomerular damage, and inflammation, especially in the high-dose groups. DFT analysis revealed that rosmarinic acid had the highest HOMO–LUMO energy gap (ΔE = 0.1314 eV), suggesting high chemical reactivity and potential biological compatibility. Molecular docking identified quercetin, rosmarinic acid, and (−)-epicatechin as the top binders, with rosmarinic acid showing the strongest affinity and forming a stable complex, as confirmed by 100 ns MDS. Taken together, the in vivo and in silico results indicate that EaNFL offers renoprotective benefits by targeting the RAAS and glucose transport pathways while also mitigating oxidative stress and inflammation. These findings demonstrate its therapeutic potential and warrant further investigation into its bioactive constituents and potential clinical use in renal treatment.
{"title":"Unveiling the renal therapeutic potential of Nypa fruticans leaves: An integrated experimental and in silico approach","authors":"Farhana Islam , Mostafa Kamal , Shoeb Ahmad , Masum Shahriar , Fariya Islam Rodru , Md. Nazmul Hasan , Md. Nazmul Hasan Zilani , Md. Ataur Rahman , Shahad Saif Khandker , Saquiba Yesmine","doi":"10.1016/j.compbiomed.2026.111482","DOIUrl":"10.1016/j.compbiomed.2026.111482","url":null,"abstract":"<div><div>Chronic kidney disease (CKD) is a progressive, irreversible disorder associated with renal dysfunction, inflammation, and oxidative stress. Given the limitations of current therapies, this study assessed the renal curative effects of <em>Nypa fruticans</em> ethyl acetate leaf extract (EaNFL) in a gentamicin-induced nephrotoxicity rat model. GC‒MS and HPLC analyses identified 23 bioactive compounds in EaNFL, including rosmarinic acid, quercetin, and (−)-epicatechin, which were selected based on ADMET profiling, Lipinski's rule, and DFT analysis. These compounds were further investigated through computational studies against two renal targets: the AT1 receptor (PDB ID: 4YAY) and SGLT2 (PDB ID: 7VSI). Treatment with EaNFL, particularly at 400 mg/kg body weight and in combination therapy, significantly improved renal function and normalized biochemical and hematological parameters, likely due to its potent antioxidant and anti-inflammatory properties. Histopathological data supported these findings, showing reduced tubular necrosis, glomerular damage, and inflammation, especially in the high-dose groups. DFT analysis revealed that rosmarinic acid had the highest HOMO–LUMO energy gap (ΔE = 0.1314 eV), suggesting high chemical reactivity and potential biological compatibility. Molecular docking identified quercetin, rosmarinic acid, and (−)-epicatechin as the top binders, with rosmarinic acid showing the strongest affinity and forming a stable complex, as confirmed by 100 ns MDS. Taken together, the <em>in vivo</em> and <em>in silico</em> results indicate that EaNFL offers renoprotective benefits by targeting the RAAS and glucose transport pathways while also mitigating oxidative stress and inflammation. These findings demonstrate its therapeutic potential and warrant further investigation into its bioactive constituents and potential clinical use in renal treatment.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111482"},"PeriodicalIF":6.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-17DOI: 10.1016/j.compbiomed.2026.111461
Truong Tien Vo , Quy Phuong Le , Trong Nhan Nguyen , Jaeyeop Choi , Sudip Mondal , Byeongil Lee , Junghwan Oh
The study introduces an innovative approach for efficient vital signs monitoring in acupuncture by combining multi-channel ballistocardiogram (BCG) signals and multi-task learning, taking advantage of the polyvinylidene fluoride (PVDF) film sensor and deep neural networks. The proposed system utilizes non-contact under-mattress BCG signals and deep learning for heart rate (HR), respiration rate (RR) estimation and lying posture detection. A custom-designed data-logger captures the signal from a BCG sensor located under the patient’s back for data acquisition, and integrates Gated Recurrent Unit (GRU) and Multi-head Self-Attention (MHSA) deep learning mechanisms for efficient HR, RR estimation and posture classification. In experiments with 25 participants, the proposed method achieved 98.7% accuracy for activity recognition and 97.6% for lying posture classification. In HR and RR estimation, the best case of mean absolute error (MAE) for HR achieves 0.77 beats per minute (bpm) in the right lateral posture, while the best value of MAE for RR is 0.43 breaths per minute (brpm) in the seated posture, compared to an FDA-approved device. The results demonstrate the high performance of multi-task learning for vital signs estimation and posture classification with our BCG-based system. This work establishes an innovative and practical pathway for medical assistance tools in non-contact monitoring and management.
{"title":"Multi-task non-contact ballistocardiogram-based vital signs monitoring in acupuncture","authors":"Truong Tien Vo , Quy Phuong Le , Trong Nhan Nguyen , Jaeyeop Choi , Sudip Mondal , Byeongil Lee , Junghwan Oh","doi":"10.1016/j.compbiomed.2026.111461","DOIUrl":"10.1016/j.compbiomed.2026.111461","url":null,"abstract":"<div><div>The study introduces an innovative approach for efficient vital signs monitoring in acupuncture by combining multi-channel ballistocardiogram (BCG) signals and multi-task learning, taking advantage of the polyvinylidene fluoride (PVDF) film sensor and deep neural networks. The proposed system utilizes non-contact under-mattress BCG signals and deep learning for heart rate (HR), respiration rate (RR) estimation and lying posture detection. A custom-designed data-logger captures the signal from a BCG sensor located under the patient’s back for data acquisition, and integrates Gated Recurrent Unit (GRU) and Multi-head Self-Attention (MHSA) deep learning mechanisms for efficient HR, RR estimation and posture classification. In experiments with 25 participants, the proposed method achieved 98.7% accuracy for activity recognition and 97.6% for lying posture classification. In HR and RR estimation, the best case of mean absolute error (MAE) for HR achieves 0.77 beats per minute (bpm) in the right lateral posture, while the best value of MAE for RR is 0.43 breaths per minute (brpm) in the seated posture, compared to an FDA-approved device. The results demonstrate the high performance of multi-task learning for vital signs estimation and posture classification with our BCG-based system. This work establishes an innovative and practical pathway for medical assistance tools in non-contact monitoring and management.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111461"},"PeriodicalIF":6.3,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145997559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rheumatoid arthritis (RA) is a systemic autoimmune disease that predominantly affects synovial joints, especially those of the hands, elbows, wrists, knees, and shoulders. RA frequently co-occurs with major depressive disorder (MDD), amplifying disease burden and complicating clinical outcomes. This study employed a multi-step integrative bioinformatics and structural biology framework to identify candidate molecular biomarkers for RA and MDD. Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on the epitranscriptomic dataset. These analyses identified immune-regulatory gene modules that were significantly associated with both phenotypes. Least absolute shrinkage and selection operator (LASSO) regression was applied to select strong, statistically significant biomarkers. The methylated biomarker EEF1A1 was identified, and its structure predicted via AlphaFold, was subjected to in silico structure-based virtual screening (SBVS) against the Comprehensive Marine Natural Product Database (CMNPD). Four marine natural products (CMNPD17984, CMNPD27318, CMNPD26200, and CMNPD26011) showed significant binding affinity for EEF1A1. Furthermore, EEF1A1-MNP complexes were simulated for 150 ns using GROMACS, and PCA-based free energy landscape (FEL) analyses were performed to characterize the dynamic behavior and identify energy minima. This integrated computational approach provides a comprehensive platform for biomarker discovery and validation in RA and MDD, with potential applications in early diagnosis, therapeutic targeting, and precision medicine.
{"title":"Machine learning guided structural dynamics identifies translation elongation factor 1 (EEF1A1) as an immunological biomarker and marine natural products as therapeutic leads for rheumatoid arthritis with major depressive disorder","authors":"Santhiya Panchalingam , Govindaraju Kasivelu , Manikandan Jayaraman , Jeyakanthan Jeyaraman","doi":"10.1016/j.compbiomed.2026.111480","DOIUrl":"10.1016/j.compbiomed.2026.111480","url":null,"abstract":"<div><div>Rheumatoid arthritis (RA) is a systemic autoimmune disease that predominantly affects synovial joints, especially those of the hands, elbows, wrists, knees, and shoulders. RA frequently co-occurs with major depressive disorder (MDD), amplifying disease burden and complicating clinical outcomes. This study employed a multi-step integrative bioinformatics and structural biology framework to identify candidate molecular biomarkers for RA and MDD. Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on the epitranscriptomic dataset. These analyses identified immune-regulatory gene modules that were significantly associated with both phenotypes. Least absolute shrinkage and selection operator (LASSO) regression was applied to select strong, statistically significant biomarkers. The methylated biomarker EEF1A1 was identified, and its structure predicted via AlphaFold, was subjected to <em>in silico</em> structure-based virtual screening (SBVS) against the Comprehensive Marine Natural Product Database (CMNPD). Four marine natural products (CMNPD17984, CMNPD27318, CMNPD26200, and CMNPD26011) showed significant binding affinity for EEF1A1. Furthermore, EEF1A1-MNP complexes were simulated for 150 ns using GROMACS, and PCA-based free energy landscape (FEL) analyses were performed to characterize the dynamic behavior and identify energy minima. This integrated computational approach provides a comprehensive platform for biomarker discovery and validation in RA and MDD, with potential applications in early diagnosis, therapeutic targeting, and precision medicine.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111480"},"PeriodicalIF":6.3,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1016/j.compbiomed.2026.111485
Malek Y. Almallah , Belal H. Sababha
The growing trend of solitary living among the elderly and young, coupled with the high risk of falls leading to injuries and death, highlights the need for fall monitoring systems. Emphasizing individuals' privacy and comfort, these systems should rely on radar sensors instead of visual-based, acoustic-based, or wearable solutions. Current radar-based systems have yet to reach satisfactory real-world performance. This work proposes a radar-based fall detection system that offers superior performance in complex real-world scenarios while maintaining edge computing capabilities and utilizing minimal hardware resources. The proposed deep learning system achieved a recall of 98.99 % and a precision of 99.32 %. These unprecedented performance numbers are measured on the proposed dataset, which is the most real-life representative dataset in the literature. The system has 211.8k parameters and ∼8.84 M Floating Point Operations (FLOPs), achieving an edge computing capability. Moreover, the efficient model construction eliminates redundant computation in real-time operation. Furthermore, this work proposes a novel performance comparison methodology that can be used in all classification problems. This methodology compares performance metrics, which are calculated based on different datasets, with a high level of fairness.
{"title":"IntNet: Lightweight yet high-performance deep learning system for intuitive radar patterns analysis and human fall detection","authors":"Malek Y. Almallah , Belal H. Sababha","doi":"10.1016/j.compbiomed.2026.111485","DOIUrl":"10.1016/j.compbiomed.2026.111485","url":null,"abstract":"<div><div>The growing trend of solitary living among the elderly and young, coupled with the high risk of falls leading to injuries and death, highlights the need for fall monitoring systems. Emphasizing individuals' privacy and comfort, these systems should rely on radar sensors instead of visual-based, acoustic-based, or wearable solutions. Current radar-based systems have yet to reach satisfactory real-world performance. This work proposes a radar-based fall detection system that offers superior performance in complex real-world scenarios while maintaining edge computing capabilities and utilizing minimal hardware resources. The proposed deep learning system achieved a recall of 98.99 % and a precision of 99.32 %. These unprecedented performance numbers are measured on the proposed dataset, which is the most real-life representative dataset in the literature. The system has 211.8k parameters and ∼8.84 M Floating Point Operations (FLOPs), achieving an edge computing capability. Moreover, the efficient model construction eliminates redundant computation in real-time operation. Furthermore, this work proposes a novel performance comparison methodology that can be used in all classification problems. This methodology compares performance metrics, which are calculated based on different datasets, with a high level of fairness.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111485"},"PeriodicalIF":6.3,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1016/j.compbiomed.2026.111473
Alberto Porta , Beatrice Cairo , Vlasta Bari , Chiara Arduino , Ilaria Burzo , Beatrice De Maria , Paolo Castiglioni , Luc Quintin , Aparecida Maria Catai , Franca Barbic , Raffaello Furlan
Symbolic analysis (SA) infers cardiac control from spontaneous stationary sequences of heart period (HP) by estimating the probability of symbolic pattern classes. Unfortunately, SA does not assess the fraction of HP variability associated with symbolic pattern families. This study proposes amplitude SA (ASA) accounting for absolute changes between consecutive HPs. ASA leverages uniform 6-bin quantization to symbolize HP, the delay embedding procedure to form length-3 symbolic patterns and a traditional strategy to group symbolic patterns into four classes families according to number and sign of variations between adjacent symbols. ASA computes the fraction of variance associated with symbolic pattern classes. ASA was applied to HP variability derived from: 1) healthy subjects during pharmacological challenges (n = 9; age: 25–46 yrs, 9 males); 2) healthy subjects during graded postural stimuli (n = 19; age: 21–48 yrs, 8 males); 3) Parkinson disease (PD) patients (n = 12; age: 55–79 yrs, 8 males) and matched healthy controls (n = 12; age: 58–72 yrs, 7 males). We computed both global and local ASA markers and we compared them with SA indexes. Over stationary HP series we found that: i) ASA provides a general method to decompose HP variance according to symbolic pattern classes; ii) ASA is useful to describe cardiac control; iii) ASA indexes are complementary to SA markers; iv) ASA emphasizes the link of HP variability markers expressed in absolute units with vagal control; v) global and local ASA approaches provide similar information. SA and ASA should be utilized concomitantly for a deeper characterization of cardiac control from spontaneous HP fluctuations.
{"title":"Amplitude symbolic analysis: a tool for the evaluation of the autonomic function complementary to traditional symbolic approach","authors":"Alberto Porta , Beatrice Cairo , Vlasta Bari , Chiara Arduino , Ilaria Burzo , Beatrice De Maria , Paolo Castiglioni , Luc Quintin , Aparecida Maria Catai , Franca Barbic , Raffaello Furlan","doi":"10.1016/j.compbiomed.2026.111473","DOIUrl":"10.1016/j.compbiomed.2026.111473","url":null,"abstract":"<div><div>Symbolic analysis (SA) infers cardiac control from spontaneous stationary sequences of heart period (HP) by estimating the probability of symbolic pattern classes. Unfortunately, SA does not assess the fraction of HP variability associated with symbolic pattern families. This study proposes amplitude SA (ASA) accounting for absolute changes between consecutive HPs. ASA leverages uniform 6-bin quantization to symbolize HP, the delay embedding procedure to form length-3 symbolic patterns and a traditional strategy to group symbolic patterns into four classes families according to number and sign of variations between adjacent symbols. ASA computes the fraction of variance associated with symbolic pattern classes. ASA was applied to HP variability derived from: 1) healthy subjects during pharmacological challenges (n = 9; age: 25–46 yrs, 9 males); 2) healthy subjects during graded postural stimuli (n = 19; age: 21–48 yrs, 8 males); 3) Parkinson disease (PD) patients (n = 12; age: 55–79 yrs, 8 males) and matched healthy controls (n = 12; age: 58–72 yrs, 7 males). We computed both global and local ASA markers and we compared them with SA indexes. Over stationary HP series we found that: i) ASA provides a general method to decompose HP variance according to symbolic pattern classes; ii) ASA is useful to describe cardiac control; iii) ASA indexes are complementary to SA markers; iv) ASA emphasizes the link of HP variability markers expressed in absolute units with vagal control; v) global and local ASA approaches provide similar information. SA and ASA should be utilized concomitantly for a deeper characterization of cardiac control from spontaneous HP fluctuations.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"203 ","pages":"Article 111473"},"PeriodicalIF":6.3,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1016/j.compbiomed.2026.111476
F. Saritha , R. Aswath Kumar , K.V. Dileep
Progesterone (P4) is a steroid hormone involved in the regulation of female reproductive functions. The endogenous progesterone receptor (PR), a member of the nuclear receptor family of ligand-dependent transcription regulators responsible for P4 action in the body through the ‘ligand binding domain’ (LBD). PR isoforms, PR-A and PR-B, are encoded by a single gene, PGR and variations in this gene can disrupt cellular signaling. In the current study, putative disease-causing mutations on PR has been identified through computationally and its mechanistic effects were explored using structural bioinformatics tools. Studies suggested that 11 of 66 missense variants (within the LBD) induce structural destabilization and were identified as potentially deleterious. Our ensemble docking suggested that these variations have a limited impact on P4 binding, however they significantly disrupt the binding of co-activators as evident by the protein-peptide docking. The binding of co-activators to the PR is the determining factor for the P4 signaling. Finally, based on the free energy of binding, we proposed two variations such as R869H and C798Y could cause myoma and progesterone tolerance conditions respectively. These findings were further validated through the use of allostery predictions. Our results reveal distinct mechanisms by which PR mutations modulate receptor function, laying the framework for future mechanistic studies and therapeutic development for PR-associated reproductive disorders.
{"title":"Unravelling the structural impact of progesterone receptor mutations in myoma and progesterone intolerance through computational modeling","authors":"F. Saritha , R. Aswath Kumar , K.V. Dileep","doi":"10.1016/j.compbiomed.2026.111476","DOIUrl":"10.1016/j.compbiomed.2026.111476","url":null,"abstract":"<div><div>Progesterone (P4) is a steroid hormone involved in the regulation of female reproductive functions. The endogenous progesterone receptor (PR), a member of the nuclear receptor family of ligand-dependent transcription regulators responsible for P4 action in the body through the ‘ligand binding domain’ (LBD). PR isoforms, PR-A and PR-B, are encoded by a single gene, PGR and variations in this gene can disrupt cellular signaling. In the current study, putative disease-causing mutations on PR has been identified through computationally and its mechanistic effects were explored using structural bioinformatics tools. Studies suggested that 11 of 66 missense variants (within the LBD) induce structural destabilization and were identified as potentially deleterious. Our ensemble docking suggested that these variations have a limited impact on P4 binding, however they significantly disrupt the binding of co-activators as evident by the protein-peptide docking. The binding of co-activators to the PR is the determining factor for the P4 signaling. Finally, based on the free energy of binding, we proposed two variations such as R869H and C798Y could cause myoma and progesterone tolerance conditions respectively. These findings were further validated through the use of allostery predictions. Our results reveal distinct mechanisms by which PR mutations modulate receptor function, laying the framework for future mechanistic studies and therapeutic development for PR-associated reproductive disorders.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"202 ","pages":"Article 111476"},"PeriodicalIF":6.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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