Biocybernetics and Biomedical Engineering最新文献

{"title":"A Complexity-Based analysis of postural stability dynamics during gait on dry and slippery surfaces","authors":"Mahdi Yousefi Azar Khanian ,&nbsp;Zahra Sadat Hosseni ,&nbsp;S.Mohammadreza Hashemi Gholpayeghani ,&nbsp;Mostafa Rostami","doi":"10.1016/j.bbe.2025.11.005","DOIUrl":"10.1016/j.bbe.2025.11.005","url":null,"abstract":"<div><div>Human postural stability represents a complex, nonlinear system influenced by a range of biomechanical and environmental factors. The trajectories of the center of pressure (CoP) serve as key indicators of this system’s underlying dynamics and are increasingly being analyzed using nonlinear methods. However, the impact of surface-induced instability during gait remains underexplored. This study aimed to analyze CoP behavior during gait on stable (dry) and unstable (slippery) surfaces by testing three hypotheses: (1) task-induced instability is associated with an increase in CoP complexity; (2) individual variability amplifies dynamic fluctuations under unstable conditions; and (3) repeated exposure to the task attenuates this complexity. Twenty healthy young males completed each walking task three times. CoP dynamics were quantified using nonlinear analyses, including phase-space reconstruction (embedding dimension and time lag) and correlation dimension (CD).</div><div>Complexity metrics, specifically the optimal embedding dimension and CD, were significantly elevated during the slippery surface condition, clearly distinguishing between the two task environments (p &lt; 0.001, classification accuracy &gt; 90 %). The greater variability in features observed under the slippery condition suggested broader dynamic adaptations to instability. Additionally, the reduction in CD across repeated trials indicated a moderating effect of prior exposure.</div><div>The findings support all three hypotheses, demonstrating the effectiveness of CoP-based nonlinear measures in capturing adaptive postural responses to changing stability demands. This study contributes a novel multi-trial nonlinear analysis approach for evaluating dynamic postural control under environmental challenges.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 1-8"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600576","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}

{"title":"Method for epileptic spike detection in EEG signals contaminated by muscle artifacts","authors":"Marcin Kołodziej ,&nbsp;Andrzej Majkowski ,&nbsp;Marcin Jurczak ,&nbsp;Andrzej Rysz ,&nbsp;Bruno Andò ,&nbsp;Remigiusz J. Rak","doi":"10.1016/j.bbe.2025.12.002","DOIUrl":"10.1016/j.bbe.2025.12.002","url":null,"abstract":"<div><div>A method for detecting epileptic spikes in EEG recordings that leverages additional EMG channels to identify and remove muscle artifacts is presented. Unlike conventional approaches, our method models the uneven propagation of muscle artifacts by applying a filter bank and linear regression to clean the EEG signal. Spike detection is then performed using template matching with user-defined parameters, such as amplitude and duration, designed for neurophysiological interpretability. To validate our approach, we developed a dedicated database comprising EEG and EMG recordings from 20 participants. Artificial triangular spikes were added to EEG segments contaminated with muscle artifacts, creating numerous examples of spikes masked by artifacts. This dataset enabled a systematic evaluation of both preprocessing and spike detection techniques. Our method achieved a sensitivity of 0.88, specificity of 1.00, and precision of 0.79 in the detection of simulated spikes. Further testing on real EEG data with interictal spikes and added muscle artifacts yielded a sensitivity of 0.83, specificity of 0.99, and precision of 0.71, demonstrating robust performance even under challenging conditions. These results indicate that incorporating EMG channels to account for muscle activity substantially improves the effectiveness of EEG signal analysis. The proposed approach facilitates reliable detection of epileptic spikes, even when masked by muscle artifacts, and allows neurophysiologists to tailor detection criteria to specific amplitude and temporal features.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 50-75"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790723","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}

{"title":"Advances in wound healing: physiology, complications, the role of oxygen and innovative treatment strategies enhancing oxygenation","authors":"Monika Drabik,&nbsp;Ludomira H. Granicka","doi":"10.1016/j.bbe.2025.12.001","DOIUrl":"10.1016/j.bbe.2025.12.001","url":null,"abstract":"<div><div>The skin is an important organ of our bodies, and its ability to restore its function after injury is crucial. Wound healing is a complex, multi-stage process that restores the integrity and function of damaged tissues. Unfortunately, due to the process’s complexity, there is no ideal treatment to enhance the process and reduce scarring. Oxygen is essential for various energy-dependent cellular activities involved in repair, such as immune responses, collagen synthesis, and angiogenesis. Chronic wounds, such as diabetic foot ulcers and pressure ulcers, often result from an imbalance between oxygen supply and demand at the wound site. Understanding the role of oxygen in wound healing is essential for developing effective therapeutic strategies to address hypoxia-related impairments in the healing process. This article highlights the informaftion, including (i) the function of the skin, (ii) the skin tissue wound healing process, (iii) the role of oxygen in wound healing, and (iv) strategies to improve oxygenation. We highlight the advances in wound treatment therapies and the potential benefits and limitations of supplemental oxygen strategies, including hyperbaric oxygen therapy and oxygen-releasing dressings.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 113-129"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884115","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}

{"title":"A multi-scale spatiotemporal learning framework for Parkinsonian gait analysis","authors":"Jiangtao Wang ,&nbsp;Zhenjie Hou ,&nbsp;En Lin ,&nbsp;Xing Li ,&nbsp;JiuZhen Liang ,&nbsp;Xinwen Zhou","doi":"10.1016/j.bbe.2025.12.004","DOIUrl":"10.1016/j.bbe.2025.12.004","url":null,"abstract":"<div><div>Patients with Parkinson’s disease typically exhibit varying degrees of motor impairment, and gait analysis can reveal underlying movement patterns, facilitating accurate diagnosis and severity assessment. However, existing methods often struggle to effectively extract local features from multi-sensor signals. In addition, attention mechanisms that operate solely in the time domain are insufficient for capturing the latent discriminative information embedded in complex gait signals, and they also face challenges in modeling global temporal dynamics.To address these issues, we propose a novel gait analysis model named PASgait. The model comprises three functional modules: the Parallel Convolutional Feature Extractor Module (PCFEM), which independently models each sensor signal to enhance the representation of local features; the Adaptive Frequency Attention Module (AFAM), which integrates discrete cosine transform and learnable frequency-domain filters, and feeds frequency-domain attention back into the original time domain via inverse transformation, thereby enriching feature representation; and the Sparse-Aware Gait Encoder (SAGE), which incorporates a sparse attention mechanism and positional encoding to strengthen the modeling of global temporal dependencies.The synergy of these modules significantly improves the model’s ability to capture complex gait dynamics and enhances its discriminative performance. In Parkinson’s disease diagnosis and severity assessment tasks, PASgait achieved accuracies of 97.0% and 87.9%, respectively, outperforming existing mainstream methods and demonstrating strong potential for clinical decision support.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 95-112"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884114","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}

{"title":"Tailoring Endoscopic sleeve gastroplasty: computational biomechanics for the evaluation and prediction of post-surgical outcomes","authors":"Ilaria Toniolo ,&nbsp;Emanuele Luigi Carniel ,&nbsp;Claudio Fiorillo ,&nbsp;Giuseppe Quero ,&nbsp;Silvana Perretta ,&nbsp;Alice Berardo","doi":"10.1016/j.bbe.2025.11.006","DOIUrl":"10.1016/j.bbe.2025.11.006","url":null,"abstract":"<div><div>Endoscopic Sleeve Gastroplasty (ESG) is currently being used successfully in people with obesity. However, potential long-term side effects are still unknown. Computational biomechanics has emerged as a valid tool to improve the intervention effectiveness.</div><div>The aim of this work is to provide an in silico framework to estimate stomach mechanics, as volumetric capacity, structural stiffness, and wall tissue strain, in response to food intake before and after ESG. A cohort of patients who underwent ESG was studied to rationally analyze the reduction in gastric volume and the changes in structural response and strain distribution. Computational predictions were compared with Magnetic Resonance Imaging (MRI) data from post-operative stomachs, allowing the reliability and reproducibility of the methodology to be assessed. Significant differences in stomach mechanics before and after surgery were observed, considering both structural stiffness and tissue strain distribution. This difference may lead to improper activation of mechanoreceptors and thus to variations in satiety after ESG.</div><div>The results confirm the suitability of the in silico approach for evaluating bariatric surgery in the short-term, because it shed light on the reduction of stomach capacity and pressurization depending on the amount of food ingested, on the variation of tissue strain distribution, giving to the surgeon information that are currently not available. Leveraging computational modeling may help prevent complications, such as reflux or misplacement of sutures, and enhance outcomes by prescribing gastric-wall loading conditions associated with lower postoperative weight-regain rates.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 9-18"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685164","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}

{"title":"Decoding daily Living activities from pre-movement EEG using Cross-Power correlation in source and sensory Spaces","authors":"Mahdie Khaliq fard ,&nbsp;Ali Fallah ,&nbsp;Ali Maleki","doi":"10.1016/j.bbe.2026.02.001","DOIUrl":"10.1016/j.bbe.2026.02.001","url":null,"abstract":"<div><div>Classifying pre-movement neural commands from voluntary actions offers great potential to improve the quality of life for paralyzed individuals through neurotechnology such as brain-computer interface (BCI) systems. Although many efforts have been made to classify simple limb movements, the challenge of classifying complex daily life movements based on pre-movement electroencephalographic signals remains. This study proposes a new feature extraction approach focusing on the analysis of functional connectivity patterns in the brain. Transitioning the analysis from the sensor level to the brain-related component space allows for a more distinct isolation of neural generators by mitigating the effects of volume conduction, thereby providing a more realistic analysis of their functional connections. For this purpose, we used several standard classification models to identify pre-movement intentions in four ADL tasks from seven participants. The results showed a significant performance difference: analysis in the brain-related component space achieved an average accuracy of 83% and a kappa statistic of 77.5%, substantially outperforming the 70.82% accuracy and 61.09% kappa statistic obtained in the sensor space. Thus, our findings confirm that pre-movement EEG signals, when analyzed in the independent component (IC) space, can more accurately encode distinct motor commands for complex activities of daily living through functional connectivity. This validated approach holds promise for the development of next-generation neurorehabilitation systems.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 224-235"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173105","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}

{"title":"A noisy label correction framework for apnea-hypopnea index estimation from sleep breathing sounds","authors":"Yujun Song ,&nbsp;Jianxin Peng ,&nbsp;Li Ding ,&nbsp;Lijuan Song ,&nbsp;Xiaowen Zhang","doi":"10.1016/j.bbe.2026.01.003","DOIUrl":"10.1016/j.bbe.2026.01.003","url":null,"abstract":"<div><h3>Background</h3><div>Sleep breathing-sound analysis offers a non-contact option for Apnea–Hypopnea Index (AHI) estimation and obstructive sleep apnea–hypopnea syndrome (OSAHS) screening. However, labels for audio segments are typically assigned by automatic alignment with polysomnography (PSG) annotations, which can introduce label noise around apnea–hypopnea events and degrade AHI estimation performance. This work proposes an AHI estimation framework that explicitly corrects noisy labels in large-scale breathing-sound datasets.</div></div><div><h3>Methods</h3><div>Whole-night sleep breathing sounds from the PSG-Audio dataset were divided into fixed-length segments and automatically labeled according to PSG annotations. An ensemble noisy-label classifier based on three ConvNeXt variants was trained to identify and correct mislabeled labels. The corrected labels were then used to train a lightweight model that combines ConvNeXt with Long Short-Term Memory (LSTM) for apnea–hypopnea event detection. Night-level prediction summaries were then mapped to AHI using a robust RANSAC linear regression model.</div></div><div><h3>Results</h3><div>Approximately 8% of the audio segments had their labels corrected. On a subject-independent test set of 50 subjects, training with corrected labels improved event-detection accuracy by 4.99% and F1-score by 2.3% compared with the raw-label baseline. The estimated AHI achieved a Pearson correlation coefficient of 0.85 with AHI from PSG. For severe OSAHS screening, the system achieved 0.94 sensitivity and 0.86 specificity.</div></div><div><h3>Conclusions</h3><div>Explicit label-noise correction improves fully non-contact AHI estimation from breathing sounds without additional sensors or substantially increased complexity. The proposed framework supports scalable AHI-based screening and triage and motivates prospective validation in diverse home settings.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 162-175"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077618","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}

{"title":"Detecting, characterizing and visualizing multiple sclerosis in optical coherence tomography through biomarker selection","authors":"Emilio López-Varela ,&nbsp;Noelia Barreira ,&nbsp;Nuria Olivier Pascual ,&nbsp;J. Quezada-Sánchez ,&nbsp;C. Oreja-Guevara ,&nbsp;José Rouco","doi":"10.1016/j.bbe.2026.01.005","DOIUrl":"10.1016/j.bbe.2026.01.005","url":null,"abstract":"<div><div>Multiple sclerosis (MS) is a chronic neurodegenerative disease affecting the central nervous system, which is the primary cause of non-traumatic neurological disability among young adults. Diagnosing MS is challenging yet crucial for effective patient treatment. Optical Coherence Tomography (OCT) has emerged as a non-invasive and efficient tool for analysing optic nerv alterations and assessing neurodegeneration in MS, particularly through changes in retinal thickness of layers such as the retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL). In this work, we investigate the potential of using changes across all retinal layer thickness as a biomarker for MS detection. To accomplish this, we propose a fully automatic system consisting of an MS classification model and a pathological thickness visualization model. First, our system employs a fully convolutional neural network to segment retinal layers and choroidal vessels and to calculate the extraction of layer thickness at varying granularities. Global and local layer thickness are used as inputs for the MS classification model. A genetic multi-objective algorithm is used for effective feature selection. Concurrently, voxel-level layer thickness serves as input for the visualization model that generates a 2D probability map where the pathological regions are highlighted. This map contributes to an interactive 3D reconstruction that provides a swift overview of MS-associated thickness changes. Extensive experimentation on real clinical MS cases validates the significant potential of the proposed system for practical clinical applications and showcases its efficacy in enhancing diagnostic precision and efficiency.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 188-201"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173102","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}

{"title":"Neuro-biomechanical coupling in exoskeleton assisted walking for stroke patients demonstrates adaptive compensation","authors":"Yujia Gao,&nbsp;Jiayi Sun,&nbsp;Chenhao Li,&nbsp;Yufeng Lin,&nbsp;Zilin Wang,&nbsp;Chenghua Jiang,&nbsp;Wenxin Niu","doi":"10.1016/j.bbe.2025.11.007","DOIUrl":"10.1016/j.bbe.2025.11.007","url":null,"abstract":"<div><div>Stroke-induced decoupling of neural control and biomechanics impairs walking. The mechanism by which exoskeleton modulates neuro-biomechanical coupling through mechanical support and assistance remains unclear. This study aims to reveal the coupling relationship between neural control and biomechanics in exoskeleton assisted walking for stroke patients through multimodal analysis. Sixteen stroke and sixteen healthy subjects participated, with kinematic, surface electromyography, and cerebral hemodynamic data collected in 4 exoskeleton assisted walking conditions. We analyzed spatiotemporal parameters, movement coordination, muscle synergy, cortical activation and functional connectivity, as well as lateralization and neural network parameters using hierarchical generalized additive mixed-effects model regression and distance correlation to explore the dynamic nonlinear effects of neuro-biomechanics and symmetry associations. Subjects after stroke showed disturbed movement coordination, simplified muscle synergy, and suppressed cortical activation. The exoskeleton activated ankle anti-phase coordination and partially restores muscle synergy, but led to reduced multi-joint coordination and increased gait speed asymmetry. Cortical activation and functional connectivity decreased for stroke subjects, and cognitively oriented lateralization as well as neural network integration efficiency were increased with exoskeleton intervention. Neuro-biomechanical coupling results indicated that subjects after stroke relied on centralized modulation of supplementary motor area activation to integrate motor planning and execution, and dynamic laterality fluctuation of premotor cortex reflected motor control rhythms by regulating movement variability. The exoskeleton reconfigured neuro-biomechanical coupling, prompting a shift from pathological compensatory discoordination toward motor planning-orientated adaptive control strategy, and providing a rationale for rehabilitation assistance targeting the adaptive reorganization of motor function.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 19-32"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685165","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}

{"title":"Scapulohumeral rhythm preserves glenohumeral stability: Insights from a biomechanical simulation","authors":"Matej Daniel ,&nbsp;Jan Votava ,&nbsp;Júlia Bodnárová ,&nbsp;Adam Kratochvíll ,&nbsp;Zbyněk Šika ,&nbsp;David Pokorný ,&nbsp;Petr Fulín","doi":"10.1016/j.bbe.2025.11.002","DOIUrl":"10.1016/j.bbe.2025.11.002","url":null,"abstract":"<div><div>The shoulder’s dynamic function is largely influenced by scapulohumeral rhythm (SHR), a coordinated movement of the scapula and humerus that facilitates a safe range of motion. While SHR has been described and quantified in terms of shoulder kinematics, its specific contribution to glenohumeral joint stability. This study aims to estimate the impact of SHR on glenohumeral stability using a biomechanical model. A five-segment musculoskeletal model based on the work of Wu et al. (2016) was implemented in OpenSim. Three SHR patterns and two loading scenarios were evaluated: a fixed scapula, a humeral-to-scapular motion ratio, and an experimentally measured SHR with free abduction or abduction while holding a 2 kg weight in the hand. Muscle forces and glenohumeral stability ratios were calculated using static optimization, and the model predictions were compared to electromyography and in vivo joint force data. While glenohumeral contact forces showed minimal variation across different SHR conditions, the stability ratio analysis revealed that the absence of SHR significantly increased the risk of joint instability. In scenarios without SHR, even small shoulder elevations resulted in overloading of the superior glenoid. The addition of weight further destabilized the joint, while substantially increasing glenohumeral force. SHR does not reduce the overall glenohumeral load but plays a critical role in maintaining glenohumeral stability, particularly during early phases of shoulder elevation and when holding additional weight. These findings highlight the importance of scapular kinematics in shoulder joint function and may have implications for managing shoulder pathologies such as rotator cuff tears and impingement, where scapular motion is often compromised.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"46 1","pages":"Pages 33-39"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685166","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}