Technology and Health Care最新文献

{"title":"Anthropometric classification of foot morphology in Korean adults using K-means cluster analysis.","authors":"Jun-Hee Kim","doi":"10.1177/09287329261495504","DOIUrl":"https://doi.org/10.1177/09287329261495504","url":null,"abstract":"<p><p>BackgroundFoot morphology reflects genetic, environmental, and lifestyle influences and affects biomechanics, footwear design, and healthcare outcomes.ObjectiveThis study aimed to derive practical anthropometric profiles of adult foot morphology using anthropometric measurements and K-means clustering as a basis for ergonomic and clinical research.MethodsFoot measurement data from 4045 adults aged 20-59 years were obtained from the 8th Size Korea Anthropometric Survey (2020-2023) using standardized three-dimensional scanning. Twelve variables, including nine height-normalized linear dimensions and three angular measures, were analyzed using K-means clustering separately by sex. The number of clusters was selected based on complementary validity criteria, subsampling-based stability, morphological granularity, and interpretability. Cluster profiles were compared using one-way analysis of variance (ANOVA) and post-hoc tests.ResultsFour clusters were identified for each sex. Although the validation indices did not indicate a single optimal solution, k = 4 was selected because it captured an elevated navicular profile absent at k = 3, whereas k = 5 showed lower stability without revealing an additional morphological pattern. The profiles differed primarily in foot size, dorsal dimensions, and navicular height, while forefoot width and hallux alignment further distinguished the female profiles. Overall ANOVA results were significant for all 12 variables (p < 0.001); however, some pairwise post-hoc comparisons were not significant.ConclusionFour sex-specific profiles provided a practical representation of continuous foot morphological variation among Korean adults. These profiles may support shoe-last and orthotic design, but their functional and clinical relevance requires external validation using footwear fit, plantar pressure, and comfort outcomes.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261495504"},"PeriodicalIF":1.3,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elderly patients with type 2 diabetes mellitus in the community: A study of self-management, depression, and their correlation.","authors":"Jinqing Wu, Xianchun Zhou","doi":"10.1177/09287329261490665","DOIUrl":"https://doi.org/10.1177/09287329261490665","url":null,"abstract":"<p><p>ObjectivesThis study sought to assess the self-management capabilities and depressive symptoms of community-dwelling older adults with type 2 diabetes mellitus (T2DM) and to investigate the correlation between these variables.MethodsThis cross-sectional study employed a convenience sampling technique to administer a questionnaire-based survey among 300 elderly patients diagnosed with T2DM, who also had existing health records at the Yanji City Park Community Health Center. The data collection took place between December 2023 and February 2024. The survey comprised sections on general demographic information, a diabetes self-care scale, and a self-rating depression scale.ResultsThe self-management capabilities of older patients with T2DM were found to be moderate, with 26.0% of the cohort experiencing symptoms of depression. A significant negative correlation was identified between self-management and depression scores (<i>r</i> = -0.219, <i>P</i> < 0.01). Univariate analysis indicated significant variations in terms of age, marital status, educational attainment, living conditions, income, occupation, presence of complications, acceptance of diabetes health education, and self-management levels among patients with differing depressive statuses (<i>P</i> < 0.05). Furthermore, binary logistic regression analysis demonstrated that age, presence of complications, acceptance of diabetes health education, and self-management level significantly influenced depression among older patients with T2DM (<i>P</i> < 0.05).ConclusionHealthcare professionals are encouraged to enhance patient health education by taking into account factors such as the individual's self-management capabilities, age, existing complications, prior health education, and physiological indicators and assessments. It is imperative to integrate depression screening into the standard diagnostic and therapeutic protocols for elderly patients with T2DM.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261490665"},"PeriodicalIF":1.3,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven biosensing technology for early disease detection: A comprehensive review.","authors":"Tanzeel Ur Rehman, Khawaja Daim Asif Ansari, Muhammad Zaeem Kahlid, Jawwad Sami Ur Rahman, Mohamed Al-Harbi, Lama Almudaimeegh, Amna Mohamed Ahmed, Zuhal Y Hamd, Babar Ali","doi":"10.1177/09287329261479178","DOIUrl":"https://doi.org/10.1177/09287329261479178","url":null,"abstract":"<p><p>BackgroundArtificial intelligence (AI) is quickly changing the field of biosensing technologies to the point of early, real-time, and individualized disease detection and overcoming significant drawbacks of traditional biosensors, including noise interference, signal drift, and inability to process non-linear biological data.ObjectiveThis review examines the latest developments in AI-driven biosensing systems and assess their impact on diagnostic performance and clinical relevance.MethodsThis critical review focuses on recently published works (2021-2025) on AI-integrated biosensing platforms, such as optical, electrochemical, wearable, and nanomaterial-based platforms, and machine learning, deep learning, edge AI, multimodal data fusion, and federated learning. Results: CNNs and LSTMs, Transformers and ensemble learning are advanced AI models with better sensitivity, specificity, automatic extraction of features, and prediction accuracy of critical diseases like cancer, cardiovascular, infectious, neurological, and metabolic diseases. Explainable Artificial Intelligence (XAI), nano-enabled biosensors, digital twins, and the Internet of Medical Things (IoMT) are examples of emerging technologies and ideas that are improving real-time, dependable, customized, and privacy-preserving diagnostic systems.ConclusionThe change to proactive and predictive healthcare is being facilitated by AI-enhanced biosensing. It has a good chance in early diagnosis, better treatment planning as well as the decrease in healthcare expenses. Nevertheless, there are still challenges such as lack of data, generalization of models, regulatory authorization, and moral issues.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261479178"},"PeriodicalIF":1.3,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A small-data machine learning framework with interpretable scorecards for disease activity assessment: A systemic lupus erythematosus case study.","authors":"Kun Zhu, Nur Hana Samsudin, Yuxuan Fang","doi":"10.1177/09287329261488670","DOIUrl":"https://doi.org/10.1177/09287329261488670","url":null,"abstract":"<p><p>BackgroundAssessment of Systemic Lupus Erythematosus (SLE) activity is complicated by clinical heterogeneity and data scarcity. Additionally, the \"black-box\" nature of many machine learning models creates significant barriers to their adoption in reliable clinical decision-making.ObjectiveThis study aims to develop an interpretable machine learning framework that transforms high-dimensional clinical data into a transparent scorecard for disease activity assessment, specifically addressing small-sample constraints.MethodsIn a cohort of 104 female SLE patients, LASSO regression selected 14 robust predictors from 149 variables spanning immunological and hematological domains. Eleven machine learning algorithms were systematically evaluated using stratified five-fold cross-validation. The optimal base model was transformed into a disease activity scorecard using a dynamic binning strategy to capture nonlinear effects.ResultsWhile Random Forest achieved the highest test-set AUC (0.883) among standalone algorithms, Logistic Regression was selected for its superior stability (AUC: 0.867) and interpretability. The derived scorecard demonstrated strong discrimination on the hold-out internal test set (AUC: 0.917), with the highest observed AUC among the evaluated models. At an optimized threshold, it achieved balanced sensitivity (0.917) and high specificity (0.840), with key predictors aligning with established pathophysiology.ConclusionsReliable prediction in data-limited settings can be achieved without sacrificing interpretability. The proposed framework provides an interpretable proof-of-concept decision-support tool for disease activity assessment in data-limited clinical settings. Although the proposed scorecard demonstrated promising performance on internal validation, prospective multi-center and external validation are required before routine clinical implementation.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261488670"},"PeriodicalIF":1.3,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FDCTGAN: Client-local CTGAN-augmented federated learning for imbalanced breast cancer classification.","authors":"Arash Salehpour, Mohammad Ali Balafar, Alireza Souri","doi":"10.1177/09287329261484379","DOIUrl":"https://doi.org/10.1177/09287329261484379","url":null,"abstract":"<p><p>ObjectiveFederated learning enables collaborative medical model development without centralizing patient records; however, client-level class imbalance may reduce sensitivity to malignant cases. This study evaluated FDCTGAN, a client-local CTGAN-augmented federated learning framework for imbalanced breast cancer classification.MethodsIn FDCTGAN, each eligible client trained a local conditional generative adversarial network and generated synthetic malignant records equal to 50% of its local class deficit. Raw and synthetic data remained on the originating client. Only feature extrema were shared once per outer fold, while classifier parameters were exchanged through Federated Averaging. Five-fold stratified cross-validation on the Wisconsin Diagnostic Breast Cancer dataset compared FDCTGAN with Vanilla FL, Local SMOTE-FL, Centralized CTGAN-FL, and Centralized Pooled learning. Each augmentation method was evaluated using three augmentation seeds and three classifier seeds, producing nine stochastic runs per fold.ResultsUnder full-batch local training, FDCTGAN increased malignant-class recall from 0.9448 ± 0.0206 with Vanilla FL to 0.9632 ± 0.0117. However, the paired recall difference was not statistically significant using a two-sided Wilcoxon signed-rank test (p = 0.125; five folds), and an increase in false positives accompanied the improvement. With a batch size of 32, FDCTGAN no longer outperformed Vanilla FL in malignant recall. Local SMOTE-FL provided the strongest overall balance among the federated approaches, whereas Centralized Pooled learning achieved the highest mean accuracy and F1 score. Property-inference analysis revealed substantial information leakage from individual client updates, and CTGAN training accounted for most of the computational runtime.ConclusionFDCTGAN represents a client-local proof of concept for addressing class imbalance in federated breast cancer classification. Its sensitivity advantage was dependent on the full-batch training protocol and was not statistically significant across the five outer folds. External validation, nested hyperparameter tuning, synthetic-data fidelity evaluation, and formal privacy-preserving mechanisms are required before clinical deployment.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261484379"},"PeriodicalIF":1.3,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to improve the classification management of hospital medical waste at the source? A two-stage multi-agent simulation based on RFID real-time data streams.","authors":"Hao Liu, Sebastiaan Meijer, Donghai Wang, Lu Liu, Futao Zhao","doi":"10.1177/09287329261481687","DOIUrl":"https://doi.org/10.1177/09287329261481687","url":null,"abstract":"<p><p><b>Background:</b> At present, the business processes of hospital medical waste classification management are mainly completed by manual work. The related business procedures are complex, time-consuming, and laborious, the digitization level of classification business is low, and data collection is difficult. These problems have seriously affected the daily management of hospital medical waste.<b>Objective:</b> Radio Frequency Identification (RFID) technology provides new ideas for solving these problems. The research purpose of this article is to use RFID technology to change the business operation mode and data collection management method of medical waste classification, and improve the digitization level of medical waste classification management.<b>Methods:</b> The business process of hospital medical waste classification management is divided into two stages: departmental generation, cleaning and classification. Based on the two-stage business scenario and RFID technology, this paper adopts a multi-agent modeling approach to design a multi-agent simulation model for hospital medical waste classification management integrated with an RFID system.<b>Results:</b> An RFID dataset containing 10 key data fields of hospital medical waste classification management was refined and formed. The two-stage business scenario and RFID simulation data stream are presented. It enables the automatic collection and real-time tracking of data in each business process of medical waste classification management.<b>Conclusions:</b> This study provides a technical model of the RFID-medical waste classification management system, and explains the setting and usage of key data fields in each business link, which provides technical application scenarios and theoretical methods support for decision-making of medical waste classification management.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261481687"},"PeriodicalIF":1.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision localization of the anus using a smart defecation assistance system based on the YOLOv11n model.","authors":"Liang Wang, Xuan Wang, An Shi, Xiwei Wang, Jian Cheng, Min Deng, Li Gou","doi":"10.1177/09287329261485172","DOIUrl":"https://doi.org/10.1177/09287329261485172","url":null,"abstract":"<p><p>BackgroundExisting defecation-assistance devices generally lack automated anal localization and image-based screening for conditions that may preclude probe operation.ObjectiveTo develop and evaluate an image-based method for anal localization and abnormal-region detection in an intelligent defecation-assistance system.MethodsThe dataset comprised 1600 human images from 1000 patients and 500 animal images. YOLOv11n and YOLOv11s were trained using transfer learning. A mixed-species technical test set was used for model comparison and ablation analyses, whereas final performance was evaluated on 160 human images. Precision, recall, F1-score, mAP, inference speed, latency, and GPU memory usage were assessed.ResultsOn the mixed-species technical test set, YOLOv11n achieved an F1-score of 97.1%, mAP@0.5 of 99.1%, and 67.1 FPS. On the human-only final evaluation set, it achieved an F1-score of 96.5% and mAP@0.5 of 98.6%. Removing data augmentation and animal images reduced mAP@0.5 by 2.1 and 1.8 percentage points, respectively. Edge-device latency was 14.9 ms per frame, with 2.8 GB GPU memory usage.ConclusionYOLOv11n showed high performance and real-time inference on static images, supporting its technical feasibility for probe localization and abnormality screening. Clinical safety and effectiveness require prospective patient evaluation.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261485172"},"PeriodicalIF":1.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface electromyography-based assessment of upper-limb muscle strength, endurance, and muscle quality during graded handgrip tasks in healthy adults.","authors":"Chun-Ju Hou, Rosalie Vertudes, Ji-Jer Huang, Min-Wei Huang, Yen-Ting Chen","doi":"10.1177/09287329261481702","DOIUrl":"https://doi.org/10.1177/09287329261481702","url":null,"abstract":"<p><p><b>Background:</b> Muscle strength, endurance, and muscle quality are important determinants of upper-limb function. Although surface electromyography (sEMG) characterizes muscle activation and fatigue, its integration with bioelectrical impedance analysis (BIA) for muscle quality assessment remains limited. <b>Objective:</b> This study investigated neuromuscular responses during graded handgrip contractions and evaluated an exploratory muscle quality (MQ) index integrating sEMG and BIA measures in healthy adults. <b>Methods:</b> Fifty-three healthy adults (35 males and 18 females) performed maximal voluntary contraction (MVC) followed by graded handgrip contractions at 0%, 20%, 40%, 60%, 80%, and 100% MVC. Surface EMG was recorded from the biceps brachii, triceps brachii, brachioradialis, and flexor carpi ulnaris. Root mean square (RMS) and frequency-domain features were extracted, with median frequency (<i>f</i>_median) selected as the representative frequency-domain outcome for nonparametric analyses and linear mixed-effects analysis. The MQ index integrated grip strength, muscle mass, body fat, and RMS and was evaluated using Spearman correlation. <b>Results:</b> RMS increased with contraction intensity, whereas <i>f</i>_median increased to a peak at 60% MVC and then gradually declined with the lowest values at 100% MVC. Significant fixed effect and interaction indicated that neuromuscular responses varied by gender, muscle, contraction stage, and muscle mass. The MQ index correlated moderately with right-arm muscle mass and phase angle but weakly with the conventional muscle quality index. <b>Conclusion:</b> Integrating sEMG and BIA provides complementary information on muscle activation, fatigue, and tissue characteristics beyond conventional strength-based measures, supporting assessment of upper-limb neuromuscular function and muscle quality.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261481702"},"PeriodicalIF":1.3,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The triple burden of malnutrition and the role of machine learning: A systematic review.","authors":"Harminder Kaur, Pooja Sharma","doi":"10.1177/09287329261481452","DOIUrl":"https://doi.org/10.1177/09287329261481452","url":null,"abstract":"<p><p>BackgroundMalnutrition remains a key global health concern encompassing the triple burden of undernutrition (stunting and wasting), micronutrient deficiencies, and overnutrition. These situations persist across all life phases and regions, significantly contributing to diseases, mortality, and impaired development. With increasing data availability, machine learning has emerged as a powerful tool capable of identifying complex, and diverse determinants of malnutrition and providing early, data-driven predictions for interventions.ObjectiveThe primary objective of this article is to provide a systematically review of existing literature to identify the determinants and contributing factors of malnutrition across different age groups and to highlight the application of machine learning algorithms in predicting malnutrition.MethodsA systematic literature review was conducted using multiple databases. The review analysed determinants of malnutrition and assessed the use of various ML algorithms such as decision trees, random forests, support vector machines, and neural networks in predicting malnutrition. Studies included were peer-reviewed and focused on assessable features such as anthropometric, socioeconomic, dietary, and environmental factors.ResultsThe review highlights that the triple burden of malnutrition continues as a global health challenge. Socioeconomic status, demographic characteristics, dietary patterns, maternal education, and health indicators were identified as chief determinants. ML models have demonstrated high predictive accuracy in identifying at-risk populations by studying multidimensional relationships that traditional methods often overlook. Among these, decision trees, random forests, and neural networks performed constantly well across diverse datasets and contexts.ConclusionThis review founds that machine learning plays a transformative role in addressing malnutrition by enabling early prediction, identification of risk factors, and policymaking. However, current studies are largely centred on children and pregnant women, with limited focus on adolescents especially adolescent girls who are equally vulnerable to undernutrition, micronutrient deficiencies, and obesity. Future research should expand to include adolescent populations and employ real-time, primary data from wearable devices, mobile health applications, and IoT-based nutrition monitoring tools. The adoption of explainable AI and deep learning will further enhance model interpretability and scalability. Collaborating these advanced technologies will reinforce global efforts to achieve sustainable nutrition and health equity.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261481452"},"PeriodicalIF":1.3,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational identification of isoflavonoids as novel PPAR-γ agonists for the treatment of type 2 diabetes mellitus.","authors":"Abdulkadir Abdu, Hossamaldeen Bakreys, Eman M Abdulkareem, Israa Abdelrahim, Mazin Aboobaida Abdalla Abdelaziz, Hind A Rikabi, Soliman S Ghanem, Lina Eltaib","doi":"10.1177/09287329261481865","DOIUrl":"https://doi.org/10.1177/09287329261481865","url":null,"abstract":"<p><p>BackgroundThe management of type 2 diabetes mellitus (T2DM) using peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists is currently limited by the adverse effects of thiazolidinediones (TZDs), such as fluid retention and weight gain. This necessitates the search for safer natural alternatives.MethodsThis study applied an integrated multi-layer <i>in silico</i> pipeline to screen 19 isoflavonoids from the FooDB database against the PPAR-γ crystal structure (PDB: 3DZY). The workflow combined molecular docking (Glide SP), structural interaction fingerprint (SIFt) validation and density functional theory (DFT) for electronic characterization followed by ADMET profiling and prediction of PASS bioactivity. Molecular dynamics (MD) simulations (100 ns) and MM-GBSA binding free energy calculations were performed to explore dynamic stability and thermodynamic affinity of the complex.ResultsGenistin (FDB012221) and daidzin (FDB012225) were identified as lead candidates with docking score of -10.837 and -10.125 kcal/mol respectively. DFT analysis revealed that FDB012221has better kinetic stability with a large HOMO-LUMO gap of 7.80 eV, in addition, it was identified as drug-like by ADMET profiling, showing 58% human intestinal absorption and PASS analysis predicted. Molecular dynamics (MD) simulations validated the stable complex formation and FDB012221retained consistent hydrogen bonds with HIS435 (91%) and LEU325 (94%)further, the MM-GBSA values were -60 kcal/mol indicating strong thermodynamic binding.ConclusionFDB012221 is a potent PPAR-γ agonist with high binding affinity, electronic stability and a favorable pharmacokinetic profile. This isoflavonoid is a potential natural scaffold for the development of safer therapies for T2DM and is recommended for further <i>in-vitro</i> and <i>in-vivo</i> validation.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261481865"},"PeriodicalIF":1.3,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}