Technology and Health Care最新文献

{"title":"Tinybionet: A lightweight time-frequency network for biomedical signal classification on edge devices.","authors":"Sandra Stankovic, Stevica Cvetkovic, Sasa V Nikolic","doi":"10.1177/09287329261473368","DOIUrl":"https://doi.org/10.1177/09287329261473368","url":null,"abstract":"<p><p>Real-time analysis of biomedical time-series on edge devices faces significant challenges due to signal non-stationarity, environmental noise, and the strict computational limitations of embedded hardware. While deep learning offers superior accuracy, standard architectures are often too computationally demanding for embedded deployment. In this work, we propose TinyBioNet, a highly compact convolutional neural network designed explicitly for the efficient affective state classification on devices with limited resources. Unlike traditional 1D CNNs that implicitly learn spectral features, or lightweight architectures that treat the STFT as an isolated preprocessing operation, our approach natively embeds a fixed-basis time-frequency transformation directly within the network execution graph using parallel 1D convolutional layers. This design eliminates separate preprocessing stages, reduces runtime, and allows a highly compact backend 2D CNN topology to operate immediately on structured complex time-frequency feature maps. By combining this structured input with depthwise convolutions and residual connections, TinyBioNet achieves state-of-the-art performance with only 5.6k parameters. We further optimize the model for embedded targets through aggressive low-bit quantization. Comprehensive evaluations on three public datasets demonstrate robust generalization across various biomedical signal modalities. The proposed framework achieves classification accuracies of up to 98.89% and 99.38% for PPG and ACC signals, respectively, while maintaining negligible performance degradation under 4-bit integer quantization.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261473368"},"PeriodicalIF":1.3,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674202","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":"In silico evaluation of novel metabolites from <i>Tricholoma pardinum</i>: Insights into drug-likeness, toxicity and target interactions via molecular docking and dynamics.","authors":"Abdalla Ali Amin, Hawraz Ibrahim M Amin, Ali Rasw Hamad, Yousif Hussein Azeeza, Rebaz Obaid Kareem, Rzgar Faruq Rashid, Rebaz Anwar Omer","doi":"10.1177/09287329261472774","DOIUrl":"https://doi.org/10.1177/09287329261472774","url":null,"abstract":"<p><p>Natural products from fungi are a significant source for drug discovery. This study investigates six previously uncharacterized metabolites isolated from the poisonous mushroom <i>Tricholoma pardinum</i> using an integrated in silico approach to evaluate their therapeutic potential. Pharmacokinetic (ADMET) profiling predicted varied drug-likeness and toxicity profiles, with several compounds showing potential to cross the blood-brain barrier. Molecular docking and MM-GBSA calculations identified compound 1 as a potent inhibitor of Poly [ADP-ribose] polymerase 1 (PARP1), with a strong binding affinity (XP GScore: -6.756; Δ<i>G_bind</i>: -48.75 kcal/mol). This interaction is anchored by a robust network of hydrogen bonds with key residues, including ASP914, CYS908, and THR866. Similarly, compound 6 emerged as a strong binder to Phosphatidylinositol 5-phosphate 4-kinase type-2 gamma (PIP4K2γ) (XP GScore: -7.705; Δ<i>G_bind</i>: -45.94 kcal/mol), with its binding stabilized by extensive hydrophobic interactions complemented by a critical hydrogen bond with the residue Methionine 206 (MET206). Subsequent 100 ns molecular dynamics simulations confirmed the high stability of both protein-ligand complexes, validating the persistence of these key interactions. These computational findings highlight the potential of metabolites from <i>T. pardinum</i> as novel scaffolds for developing anticancer agents targeting PARP1 and PIP4K2γ, warranting further experimental validation.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261472774"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654801","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 role of machine learning in early detection, accurate diagnosis, and timely treatment of diseases.","authors":"Abhishek Shrivastava, Abhijit Tripathi, Jivanshi Rajput","doi":"10.1177/09287329261468976","DOIUrl":"https://doi.org/10.1177/09287329261468976","url":null,"abstract":"<p><p>The contemporary healthcare landscape is experiencing a significant transformation driven by the rapid growth of digital health data and advancements in computational technologies. At the center of this evolution is Machine Learning (ML), a branch of artificial intelligence that enables systems to learn from data, recognize patterns, and support decision-making with minimal human intervention. This paper presents a comprehensive analysis of the role of ML in enhancing early disease detection, accurate diagnosis, and timely treatment across modern healthcare systems. It begins by discussing key ML paradigms, including supervised, unsupervised, and reinforcement learning, and their applications in medical practice. The study further highlights how advanced ML and deep learning algorithms achieve human-level or even superior performance in analyzing complex healthcare data such as medical imaging, genomics, and electronic health records. ML applications in the early detection of diseases such as cancer, diabetic retinopathy, and sepsis are explored, emphasizing their ability to identify subtle pre-symptomatic patterns. Additionally, the paper examines the role of ML in differential diagnosis, risk stratification, and personalized medicine through multi-omics data integration. Furthermore, the paper discusses the contribution of ML to precision oncology, drug discovery, and chronic disease management. Despite its potential, challenges such as data quality, interpretability, ethical concerns, regulatory barriers, and privacy issues continue to hinder widespread clinical adoption. The paper concludes that ML will augment rather than replace clinicians, enabling predictive, personalized, and data-driven healthcare.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261468976"},"PeriodicalIF":1.3,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148609144","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 study of the mechanism of ZhenWu Tang in the treatment of hepatocellular carcinoma based on network pharmacology and molecular docking.","authors":"Xiangyu Zhang, Dong Wang, Rui Gao, Shu Jing Hou","doi":"10.1177/09287329261470849","DOIUrl":"https://doi.org/10.1177/09287329261470849","url":null,"abstract":"<p><p>BackgroundAs an integral part of the comprehensive treatment strategy for hepatocellular carcinoma (HCC) in China, Traditional Chinese Medicine (TCM) offers unique therapeutic advantages. This study aims to elucidate the anti-HCC mechanism of ZhenWu Tang, a classic TCM formula, by employing an integrated approach of network pharmacology and molecular docking.MethodsThe chemical components of ZhenWu Tang were retrieved from the TCMSP database and published literature. Their corresponding potential targets were then predicted via the SwissTargetPrediction database. The names of target proteins were standardized using the UniProt database. Effective therapeutic targets for hepatocellular carcinoma (HCC) were obtained from databases including OMIM and GeneCards. A Venn diagram was generated to identify the overlapping targets between ZhenWu Tang and HCC. A protein-protein interaction (PPI) network for these potential therapeutic targets was constructed using the STRING database and visualized with Cytoscape 3.7.2. Finally, \"drug-component-target\" and \"drug-component-anti-HCC-target\" network diagrams were constructed. Using metascape platform, GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Gene and Genomes) enrichment analysis were carried out on the potential anti hepatoma targets of ZhenWu Tang. Autodock was used to predict the binding mode and affinity between the active compounds and the core targets.ResultsAccording to oral bioavailability and drug like conditions, 61 chemical components of ZhenWu Tang were collected, including the main active components <i>α</i>-Amyrin, Pachymic acid, Kaempferol, <i>β</i>-Sitosterol et al., 249 Zhenwu Tang anti HCC targets were obtained. Protein-Protein interaction (PPI) network calculated that there are mainly TP53, AKT1, GAPDH, TNF and other key targets. Enrichment analysis yielded 218 signal pathways, involving the role of MicroRNAs in cancer, cancer proteoglycan, PI3K/AKT signal pathway, MAPK signal pathway, FoxO signal pathway, etc. Molecular docking results showed that Zhenwu Tang had good affinity with liver cancer protein targets.ConclusionZhenWu Tang can treat HCC by inhibiting tumor cell proliferation and promoting tumor cell apoptosis through multiple components, multiple targets and multiple pathways.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261470849"},"PeriodicalIF":1.3,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580885","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":"Mapping the knowledge landscape and research trends of artificial intelligence in breast cancer diagnosis and treatment: A bibliometric analysis.","authors":"Cong Wang, Ke Hong, Yu Ying, Yanlei Xu, Chang Yao","doi":"10.1177/09287329261466487","DOIUrl":"https://doi.org/10.1177/09287329261466487","url":null,"abstract":"<p><p>BackgroundArtificial intelligence (AI) has generated rapidly growing research in breast cancer diagnosis and treatment, yet its intellectual structure, collaboration patterns, and thematic evolution remain unmapped.ObjectiveTo analyze the global research landscape of AI in breast cancer from 2001 to 2025, identifying publication trends, contributors, collaboration networks, thematic clusters, and translational gaps.MethodsA bibliometric analysis of 7673 Web of Science articles. VOSviewer was used for co-authorship networks and keyword co-occurrence with overlay visualization; CiteSpace for burst detection.ResultsThe field grew exponentially (29.20% annual growth), with 88.41% of publications from 2020-2025. China (31.63%) and the United States (21.10%) dominated output, but co-authorship networks revealed limited international collaboration for China (22.4% non-Chinese co-authors) and structural exclusion of low- and middle-income countries. Seven keyword clusters showed persistent separation between technology-centric and clinically oriented terms. Temporal overlay revealed a shift from computer-aided detection (pre-2015) to deep learning (2015-2022) and explainable AI, federated learning, and vision transformers (2022-2025). Burst detection confirmed \"feature selection\" (strength=19.53) and \"computer aided detection\" (strength=20.24) as historical hotspots; limited recent bursts indicate emerging frontiers are still accumulating citation impact.ConclusionsAI research in breast cancer has expanded rapidly with evolving themes, yet a translational gap persists between innovation and clinical integration. Future efforts should prioritize prospective validation, international collaboration with underrepresented regions, and standardized frameworks for integrating explainable and privacy-preserving AI into workflows.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261466487"},"PeriodicalIF":1.3,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563105","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":"Investigating the association between osteoporosis and acute myocardial infarction using Mendelian randomisation and bioinformatics.","authors":"Bin Zhou, Shaoqiang Chai, Jiaxing Chen, Zhiyu Chen, Yunhong Fei, Yuhuai Wu, Shihong Luo","doi":"10.1177/09287329261461171","DOIUrl":"https://doi.org/10.1177/09287329261461171","url":null,"abstract":"<p><p><b>Background:</b> Osteoporosis and acute myocardial infarction (AMI) are major health challenges in the aging population. Osteoporosis increases bone fragility, while AMI, often due to atherosclerosis, causes myocardial ischemia and inflammation. Their co-morbidity and shared mechanisms remain unclear. <b>Objective:</b> To explore the causal relationship and shared molecular mechanisms between osteoporosis and AMI. <b>Methods</b>: GWAS data from the Risteys FinnGen R9 database (osteoporosis: 621 cases, 122,861 controls) and the IEU Open GWAS program (AMI: 20,917 cases, 461,823 controls) were analyzed using Mendelian Randomization (IVW, MR-Egger, weighted median). Gene expression datasets (GSE56815, GSE48060) were used to identify differentially expressed genes (DEGs). Functional enrichment, immune infiltration (MCPcounter), and weighted gene co-expression network analysis (WGCNA) were performed, and overlapping hub genes were identified. <b>Results:</b> MR analysis demonstrated a significant causal association between osteoporosis and AMI. Transcriptomic analysis revealed 2434 DEGs in osteoporosis and 2827 in AMI. Enrichment highlighted pathways including immune regulation, MAPK signaling, and cancer-related pathways. Immune infiltration showed altered monocytes and dendritic cells in osteoporosis, and cytotoxic lymphocytes and neutrophils in AMI. WGCNA identified 6 modules in osteoporosis and 11 in AMI, with 1423 common hub genes. <b>Conclusion:</b> Osteoporosis and AMI share genetic and molecular mechanisms, especially involving inflammation and calcium signaling. These findings provide new insights into their co-morbidity and suggest that targeting shared therapeutic pathways may support integrated strategies for improving bone and cardiovascular health.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261461171"},"PeriodicalIF":1.3,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537532","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":"Automated comet assay analysis using YOLOv5-based deep learning.","authors":"Adna Softić, Faruk Bećirović, Ilma Mujković, Renata Klasan, Lejla Mahmutović, Abas Sezer, Lejla Gurbeta Pokvić, Daria Ler, Ernesto Iadanza","doi":"10.1177/09287329261466485","DOIUrl":"https://doi.org/10.1177/09287329261466485","url":null,"abstract":"<p><p>BackgroundThe comet assay is a sensitive and widely used technique for assessing DNA damage at the single-cell level. Despite its advantages, traditional manual scoring methods remain time-consuming, subjective and limited in scalability, posing challenges for high-throughput and standardized analysis.ObjectiveThis study aims to develop and evaluate a deep learning-based system for automated comet assay image classification, addressing limitations of manual and semi-automated approaches while enhancing accuracy, reproducibility and processing efficiency.MethodA YOLOv5-based object detection model was trained on a dataset of 875 annotated comet assay images, curated through a three-step expert-reviewed process. Various hyperparameters and data augmentation techniques were optimized to improve performance. The dataset was split into training, validation and test sets, and model performance was evaluated using mAP, precision, recall and confusion matrix analysis.ResultsThe model achieved strong performance, with mAP@0.5 reaching 0.98 and recall exceeding 0.8. Detailed analyses revealed robust learning behavior and generalization capacity. Visual outputs, including precision-recall curves and class-wise confusion matrices, confirmed high classification accuracy, although overlapping comet structures and class imbalance posed challenges. The model demonstrated improved scalability and processing speed compared to traditional tools, supporting its integration into web-based applications.ConclusionThe proposed YOLOv5-based system offers a scalable and accurate solution for automating comet assay analysis. It significantly enhances throughput and reduces human error, supporting its application in genotoxicity testing, biomonitoring and molecular epidemiology. Future work will focus on handling overlapping structures, benchmarking against existing tools and optimizing deployment in real-world laboratory settings.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261466485"},"PeriodicalIF":1.3,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148450198","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":"Gait adaptation in adults with intellectual disability with and without Down syndrome: A Kinect-based neural network approach.","authors":"Mehmet Uçar, Fatih Mirze, Emin Ağrali","doi":"10.1177/09287329261468631","DOIUrl":"https://doi.org/10.1177/09287329261468631","url":null,"abstract":"<p><p>BackgroundRecognizing that walking is a critical marker of independence, fall risk, and overall health in adults with intellectual disability (ID) and that comparative evidence by Down syndrome (DS) status remains limited, this study aimed to quantify spatial, temporal, and kinematic gait differences across flat and compliant surfaces in adults with and without DS and to evaluate the classification performance of Kinect-derived gait data using artificial neural networks (ANNs).MethodsCross-sectional, exploratory study at a state special education center in eastern Türkiye. Sixty-nine participants aged 18-27 years (ID without DS: n = 46; ID with DS: n = 23; mild-moderate levels) completed three 3-m trials at preferred speed on flat (concrete) and compliant (foam) surfaces. Kinect V1 recorded 3D joint trajectories. Step count, step length, step duration, and walking speed were computed via a threshold-based method. ANN models were trained to classify by DS status, gender, and disability level/age.ResultsOn compliant surfaces, step count and walking time increased while walking speed decreased relative to flat surfaces. Performance decrements were more pronounced in participants with DS and those with moderate ID. Women took more steps and walked more slowly than men on both surfaces. ANN models showed high correlation values across tasks (R = 0.96-0.99); however, these findings should be interpreted as exploratory.ConclusionA Kinect-based approach may provide a practical and accessible method for characterizing gait in adults with ID with and without DS; however, the findings should be interpreted with caution due to methodological limitations and the need for further validation. Findings support integrating surface-specific balance and gait training, age- and gender-sensitive strategies, and individualized programs for DS. ANN-based analyses may provide preliminary insights for classification purposes; however, their clinical applicability requires further validation and should be interpreted cautiously.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261468631"},"PeriodicalIF":1.3,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148438529","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 bibliometric analysis of patient-centred digital healthcare services studies.","authors":"Lei Mao, Ivan Parati, Cheng-Hung Lo","doi":"10.1177/09287329261464405","DOIUrl":"10.1177/09287329261464405","url":null,"abstract":"<p><p>BackgroundThe rapid advancement of digital technologies has transformed healthcare delivery, yet significant gaps remain in patient-centred research on digital healthcare services (DHCS).ObjectiveThis study aims to map the global research landscape of patient-centred DHCS, identify key themes and collaboration patterns, and highlight gaps to guide future research.MethodsFollowing a narrative review of DHCS evolution with China as a regional case study, a bibliometric analysis was conducted on 4163 publications retrieved from Scopus (2020-2025). VOSviewer and the Bibliometrix R package were used for visual mapping and trend analysis.ResultsPatient satisfaction with telemedicine has emerged as a key research focus, while on-site healthcare scenarios receive comparatively less attention. Additionally, the elderly population, as primary users of healthcare services, remains underrepresented in studies on the usability and experience of digital healthcare services. Research methodologies, including questionnaires, interviews, and randomised controlled trials, address diverse demographics and dimensions. International collaboration is dominated by the United States and European countries, reflecting their scientific influence.ConclusionCritical gaps identified include insufficient attention to on-site DHCS and elderly users, alongside disparities in global research influence. Future research should prioritise these areas and strengthen cross-continental collaboration to advance patient-centred digital healthcare delivery.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261464405"},"PeriodicalIF":1.3,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413494","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":"Numerical study on the effects of stent implantation on hemodynamics in the stenotic artery-microcirculation interaction system.","authors":"Fan He, Chun Luo, Minru Li, Lu Hua, Tingting Guo","doi":"10.1177/09287329261466928","DOIUrl":"https://doi.org/10.1177/09287329261466928","url":null,"abstract":"<p><p>BackgroundIn recent years, for patients with arterial stenosis up to 75% and poor medical control, stent implantation has become the mainstream choice.ObjectiveThe purpose of this work is to study the effects of stent implantation on hemodynamics in the stenotic artery considering microcirculation.MethodsA stent implantation model with microcirculation is constructed. Expansion simulations are carried out for the grid stent and the link stent respectively to analyze the changes in hemodynamic parameters and the mechanical responses of the plaque and the stent.ResultsCompared with the stent implantation study without microcirculation, the results show that the pressure gradient decreases, and the probability of high time averaged wall shear stress near the stent wires reduces after the grid stent is implanted and expanded. The link stent generates time averaged wall shear stress close to the normal physiological range, and no regions with high oscillatory shear index and high relative residence time are found near the stent wire upstream of the stent.ConclusionsThe link stent can not only better improve the hemodynamic environment, but also reduce the risk of intimal hyperplasia and atherosclerosis. These findings reflect the importance of microcirculation in the regulation of hemodynamics after different stent implantations, providing a new perspective for optimizing stent design.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261466928"},"PeriodicalIF":1.3,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413491","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}