IEEE Transactions on Biomedical Engineering最新文献

{"title":"Impact of a Lower Limb Exosuit Anchor Points on Energetics and Biomechanics.","authors":"Chiara Lambranzi, Giulia Oberti, Christian Di Natali, Darwin G Caldwell, Manuela Galli, Elena De Momi, Jesus Ortiz","doi":"10.1109/TBME.2025.3593040","DOIUrl":"https://doi.org/10.1109/TBME.2025.3593040","url":null,"abstract":"<p><p>Anchor point placement is a crucial yet often overlooked aspect of exosuit design since it determines how forces interact with the human body. This work analyzes the impact of different anchor point positions on gait kinematics, muscular activation and energetic consumption. A total of six experiments were conducted with 11 subjects wearing the XoSoft exosuit, which assists hip flexion in five configurations. Subjects were instrumented with an IMU-based motion tracking system, EMG sensors, and a mask to measure metabolic consumption. The results show that positioning the knee anchor point on the posterior side while keeping the hip anchor on the anterior part can reduce muscle activation in the hip flexors by up to 10.21% and metabolic expenditure by up to 18.45%. Even if the only assisted joint was the hip, all the configurations introduced changes also in the knee and ankle kinematics. Overall, no single configuration was optimal across all subjects, suggesting that a personalized approach is necessary to transmit the assistance forces optimally. These findings emphasize that anchor point position does indeed have a significant impact on exoskeleton effectiveness and efficiency. However, these optimal positions are subject-specific to the exosuit design, and there is a strong need for future work to tailor musculoskeletal models to individual characteristics and validate these results in clinical populations.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144730115","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":"Multimodal Experimental Platform to Disentangle Emotional and Physiological Pain Components.","authors":"Haotian Yao, Giuseppe Valerio Aurucci, Noemi Gozzi, Flavia Davidhi, Stanisa Raspopovic","doi":"10.1109/TBME.2025.3593280","DOIUrl":"https://doi.org/10.1109/TBME.2025.3593280","url":null,"abstract":"<p><strong>Objective: </strong>Pain is a disabling experience significantly impacting individuals' lives. The complex interplay between physiological and psychological factors poses challenges for assessing pain and developing effective therapies. Hence, healthcare providers advocate for reliable, objective, multidimensional metrics to quantify pain. Developing and validating such metrics requires standardized experimental tools capable of probing physical and emotional dimensions.</p><p><strong>Methods: </strong>To these aims, we designed a synergistic platform combining virtual reality (VR) and electro-cutaneous stimulation (ECS). The platform targeted physical (via ECS) and emotional (flames appearing on a virtual hand) pain components. We tested it with 20 participants, each undergoing 120 painful stimuli. During stimulation, we collected neural, physiological signals, and self-reported pain.</p><p><strong>Results: </strong>We demonstrated the platform's effectiveness in modulating pain through physical (NRS_HP = 5.85±1.23, NRS_LP = 1.69±0.87, p<0.001) and emotional (NRS_FIRE = 6.04±1.21, NRS_NEUTRAL = 5.66±1.25, p<0.001) stimuli. In parallel, we leveraged explainable ML to identify objective signatures of pain modulation in neural activity and physiological signals. Using multilevel mixed-effect-models (MEM), we predicted self-reported pain based on physiological signals and a subjective bias term, showing that physiological data alone cannot fully capture pain's complexity. To bridge this gap, we calculated TIP (subjective Index of Pain) quantifying the mismatch between reported pain and objective signals. We validated TIP as a reliable indicator of subjective predisposition to pain by showing its sensitivity to emotional modulations (Δ_{TIPFIRE-NEUTRAL} = 0.363±0.270, p<0.001).</p><p><strong>Conclusion: </strong>We developed a robust framework to investigate distinct pain dimensions and validated TIP for assessing individuals' subjective pain.</p><p><strong>Significance: </strong>In the future, multidimensional tools and reliable metrics could foster personalized effective pain therapies.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144730116","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":"Dynamic Ultrasound Imaging Based on Variable-Angle Transducer for Accurate Monitoring of Human Motion.","authors":"Yiheng Li, Zhengxin Yang, Lihao Liu, Shuai Wu, Yang Jiao, Yaoyao Cui","doi":"10.1109/TBME.2025.3592907","DOIUrl":"https://doi.org/10.1109/TBME.2025.3592907","url":null,"abstract":"<p><strong>Objective: </strong>Conventional medical imaging modalities (computed tomography, X-ray radiography, magnetic resonance imaging, and classical ultrasound imaging) exhibit inherent limitations in dynamic tissue monitoring. Although wearable ultrasound has addressed some of these shortcomings, existing solutions face significant challenges in achieving precise dynamic imaging during large-amplitude movements. This study develops a variable-angle ultrasound transducer (VA-US) and its imaging method to enable high-accuracy anatomical tracking under large-scale motion conditions.</p><p><strong>Methods: </strong>VA-US consists of two micro ultrasonic phased arrays connected by a sensible micro hinge, ensuring conformal contact with highly curved skin surfaces. Integrated magnetic induction sensor dynamically monitor spatial relationships of ultrasonic elements, guaranteeing accurate beamforming.</p><p><strong>Results: </strong>The capability of dynamic monitoring and real-time imaging of VA-US was verified by simulations, standard phantom and in-vivo experiments, showing potential applications in clinical diagnosis, rehabilitation and sport medicine.</p><p><strong>Conclusion: </strong>The proposed methodology is capable of capturing the dynamic anatomical changes of regions with significant cutaneous curvature variations.</p><p><strong>Significance: </strong>VA-US provides a novel approach for reflecting the health status of human tissues and organs under dynamic conditions, facilitating the monitoring and assessment of biomedical phenomena that are difficult to observe in static examinations.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715064","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":"Physiological Denoising Method for Unbiased Analysis of Biomedical Signals: Application on Heartbeat Dynamics.","authors":"Andrea Scarciglia, Claudio Bonanno, Gaetano Valenza","doi":"10.1109/TBME.2025.3592303","DOIUrl":"https://doi.org/10.1109/TBME.2025.3592303","url":null,"abstract":"<p><strong>Background: </strong>Physiological systems show nonlinear deterministic behavior influenced by dynamical stochastic components, also known as physiological noise. Those components may bias deterministic system modeling and characterization.</p><p><strong>Objective: </strong>This study presents a model-free physiological denoising method for biomedical signals, such as heart rate variability (HRV) series, specifically focusing on the reduction of dynamical noise.</p><p><strong>Methods: </strong>The proposed method employs state-space reconstruction and time-reversing one-step forecasting, selecting optimal values within a neighborhood in the multidimensional space. The neighborhood size is determined as proportional to the physiological noise power. Synthetic time series analysis validate the correctness of the proposed method. Real HRV series from healthy subjects, patients with congestive heart failure, and those with atrial fibrillation were denoised, and unbiased complexity analysis was then performed. Physiological denoising performance was evaluated using root mean square error and median absolute deviation statistics.</p><p><strong>Results: </strong>Synthetic data analysis on canonical nonlinear maps demonstrated that the proposed method outperforms existing techniques in dynamical noise reduction. For HRV series, the proposed method effectively reduced physiological noise while preserving signal characteristics such as mean. While Sample Entropy analysis on original HRV series associated atrial fibrillation with the highest irregularity, unbiased analysis on denoised series revealed that healthy individuals actually exhibit the highest cardiac complexity.</p><p><strong>Conclusion: </strong>The proposed method effectively performs physiological denoising in biomedical signals, providing a reliable tool for unbiased analyses. This method enhances the understanding of underlying physiological dynamics that are intrinsically influenced by stochastic components.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707331","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":"Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.","authors":"Soheil Saghafi, Qiao Li, Thomas C Neylan, Tommy T Thomas, Jennifer S Stevens, Tanja Jovanovic, Laura T Germine, Meredith A Bucher, Megan E Huibregtse, Sarah D Linnstaedt, Xinming An, Nathaniel G Harnett, Seth D Norrholm, Alana C Conti, Antonia V Seligowski, Daniel G Dillon, Lisa M Vizer, Lauren A McKibben, Liz Marie Albertorio-Saez, Francesca L Beaudoin, Liana Matson, Vince D Calhoun, Steven E Harte, Steven E Bruce, John P Haran, Alan B Storrow, Christopher Lewandowski, Paul I Musey, Phyllis L Hendry, Robert A Swor, Claire Pearson, David A Peak, Brian J O'Neil, Ronald C Kessler, Karestan C Koenen, Samuel A McLean, Gari D Clifford, Ali Bahrami Rad","doi":"10.1109/TBME.2025.3592009","DOIUrl":"https://doi.org/10.1109/TBME.2025.3592009","url":null,"abstract":"<p><strong>Background: </strong>Traumatic Brain Injury (TBI) is a major public health concern, and accurate classification is essential for effective treatment and improved patient outcomes. Sleep/wake behavior has emerged as a potential biomarker for TBI classification, yet the optimal time window in which to identify sleep/wake changes after TBI remains unclear.</p><p><strong>Methods: </strong>We evaluated daily longitudinal sleep/wake data from a prospective cohort of more than 2,000 emergency department patients with and without blood biomarker-documented TBI (Glial Fibrillary Acidic Protein - GFAP $ > 268 frac{pg}{ml}$). We utilized a deep learning model to identify the impact of time from trauma and duration of data collection on the model's ability to distinguish between TBI-positive (TBI+) and TBI-negative (TBI-) cases.</p><p><strong>Results: </strong>Our analysis showed that sleep/wake data from the first 7 days after TBI most accurately identified TBI. Sleep-wake data from the first 7, 14, and 21 days after trauma achieved sensitivity/specificity of 81%/25%, 40%/66%, and 45%/58%, respectively. F1 scores of deep learning models developed from the first 7, 14, and 21 days were 22%, 21%, and 20%, respectively.</p><p><strong>Conclusions: </strong>The results suggest that early sleep/wake data has promise for assisting with TBI identification.</p><p><strong>Significance: </strong>In the future, the incorporation of sleep/wake derived biomarkers into TBI identification tools could assist in the identification of individuals with potential TBI for further screening and intervention.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707332","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":"Explainable Temporal Inference for Irregular Multivariate Time Series. A Case Study for Early Prediction of Multidrug Resistance.","authors":"Oscar Escudero-Arnanz, Cristina Soguero-Ruiz, Joaquin Alvarez-Rodriguez, Antonio G Marques","doi":"10.1109/TBME.2025.3591924","DOIUrl":"https://doi.org/10.1109/TBME.2025.3591924","url":null,"abstract":"<p><strong>Objective: </strong>Many healthcare problems involve complex patient trajectories represented as Multivariate Time Series (MTS), with predictions often coming as Time Series (TS) outputs. Despite recent advances, these \"MTS-to-TS\" inference tasks remain challenging due to data irregularity, temporal dependencies, and the need for clinical explainability. To address these demands, we propose novel eXplainable Artificial Intelligence (XAI) methods for \"MTS-to-TS\" architectures, enabling tracking of patient evolution and identification of key variable patterns associated with adverse outcomes. We evaluate our approach on private ICU data from the University Hospital of Fuenlabrada (UHF) for Multidrug Resistance (MDR) prediction and the public HiRID dataset (circulatory failure).</p><p><strong>Methods: </strong>We introduce three XAI techniques: i) Irregular Time SHapley Additive exPlanation (IT-SHAP), a post-hoc extension of TimeSHAP to TS outputs; ii) Hadamard Attention, an intrinsic mechanism for capturing temporal dependencies; and iii) Causal Conditional Mutual Information, a pre-hoc approach for feature selection.</p><p><strong>Results: </strong>MDR prediction achieved highest performance with a GRU using Hadamard Attention (ROC-AUC=0.783$pm$0.023), while circulatory failure was best predicted with LSTM (ROC-AUC of 0.9970$pm$1.6e$^{-3}$). In terms of explainability, IT-SHAP uncovered clinically relevant risk factors-early antibiotic use and bacterial cultures-later validated by UHF clinicians.</p><p><strong>Conclusion: </strong>Our framework offers temporal explainability in \"MTS-to-TS\" architectures, allowing clinicians to trace disease trajectories and understand the contribution of each variable at each time step.</p><p><strong>Significance: </strong>Integrating explainable MDR risk predictions into EHR systems enables early interventions, improved antimicrobial stewardship, and infection control. The framework's scalability to other ICU challenges underscores its clinical impact.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698437","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 NMF-based non-Euclidean Adaptive Feature Extraction Scheme for Limb Motion Pattern Decoding in Pattern Recognition System.","authors":"Frank Kulwa, Pengrui Tai, Doreen S Sarwatt, Mojisola G Asogbon, Rami Khushaba, Tolulope T Oyemakinde, Sunday T Aboyeji, Guanglin Li, Oluwarotimi W Samuel, Yongcheng Li","doi":"10.1109/TBME.2025.3592183","DOIUrl":"https://doi.org/10.1109/TBME.2025.3592183","url":null,"abstract":"<p><p>Feature extraction is a crucial step in electromyogram (EMG)-based pattern recognition systems for decoding motor intents. However, despite the existence of numerous proposed techniques for feature extraction, their decoding performances have remained relatively low. Furthermore, these techniques are often evaluated without taking into account the drift between the training and test datasets. This study proposes a feature extraction scheme that operates in an unsupervised manner to address these limitations. This approach focuses on reducing drift between the training and test sets by utilizing feature adaptation based on non-negative matrix factorization (NMF) and Riemann operations. Additionally, we minimize drift by aligning the distribution of the test data with that of the training set. The results demonstrate that the proposed feature extraction technique exhibits significantly higher performance (p < 0.05) in decoding motor intent for 13 hand and finger movements, achieving an average accuracy of 99.91 ± 0.35% for amputee participants and 99.99 ± 0.02% for able-bodied participants. We also conducted further investigations to assess the effectiveness of the proposed feature scheme against varied signal-to-noise ratios (SNRs). These investigations revealed that our technique outperforms other feature extraction techniques in terms of decoding performance, even in the presence of varied SNRs. Overall, the findings show that the proposed feature extraction technique can effectively enhance the reliability and robustness of EMG control systems in both clinical and commercial applications.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698436","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":"Multi-scale feature extraction and aggregation network for electroencephalography classification in face photo-sketch recognition task.","authors":"Xi Zhang, Chunze Yang, Fu Li, Yang Li, Boxun Fu, Shenhong Wang, Lijian Zhang, Huaning Wang, Guangming Shi","doi":"10.1109/TBME.2025.3591030","DOIUrl":"https://doi.org/10.1109/TBME.2025.3591030","url":null,"abstract":"<p><p>Face photo-sketch recognition task plays a crucial role in forensic investigation, human visual perception, and facial biometrics applications. The substantial modality gap between photographs and sketches, compounded by the influence of the semantic gap, poses a formidable challenge to recognition tasks. This study aims to propose an effective electroencephalography (EEG)-based approach to bridge this gap. In this paper, we introduce a face photo-sketch recognition paradigm (FPSR), a rapid serial visual presentation (RSVP) paradigm for the matching of face sketches. Based on this paradigm, we further proposed a new EEG signal feature decoding method called multi-scale feature extraction and aggregation network (MFEA). This network extracts shallow features in three dimensions and reconstructs three dimensional abstract features. Subsequently, the shallow features are aggregated with the deeper features to enhance the retention of all effective EEG signal features. These combined features are then input into the spatial module for specific dimensionality reduction. Experiments were conducted on one public and one self-conducted EEG RSVP datasets to evaluate the performance of our proposed MFEA. The experimental results demonstrate that, compared to previous methods, our MFEA exhibits superior performance in the EEG classification task.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144690070","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":"IEEE Transactions on Biomedical Engineering Information for Authors","authors":"","doi":"10.1109/TBME.2025.3580128","DOIUrl":"https://doi.org/10.1109/TBME.2025.3580128","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 8","pages":"C3-C3"},"PeriodicalIF":4.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11085001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrafast Simultaneous T₁, T₂, T₂*, PD, ΔB₀, and B₁ Mapping via Longitudinal Magnetization Controlled MOLED Acquisition.","authors":"Weikun Chen, Taishan Kang, Qing Lin, Xinyu Guo, Jian Wu, Simin Li, Yuchen Zheng, Jianzhong Lin, Liangjie Lin, Jiazheng Wang, Xiaobo Qu, Zhong Chen, Shuhui Cai, Congbo Cai","doi":"10.1109/TBME.2025.3590286","DOIUrl":"https://doi.org/10.1109/TBME.2025.3590286","url":null,"abstract":"<p><strong>Objective: </strong>Multi-parametric quantitative mag- netic resonance imaging (mqMRI) provides comprehensive and accurate information about tissue microstructure and holds significant clinical value for the diagnosis and treatment of diseases. However, conventional methods require long scan time, leading to registration errors and physiological variability between different sequence acqui- sitions. This study aims to propose an advanced imaging method that addresses these limitations.</p><p><strong>Methods: </strong>A novel approach called longitudinal magnetization controlled multiple overlapping-echo detachment (LMC-MOLED) imaging was proposed. LMC-MOLED leverages a deep neural network trained on synthetic data generated from Bloch simulation, incorporating non-ideal factors such as B0 and B1 inhomogeneities to efficiently reconstruct parametric maps.</p><p><strong>Results: </strong>LMC-MOLED enables simulta- neous quantification of T1, T2, T2*, proton density (PD), ΔB0, and B1 parameters in approximately 1.2 seconds per slice. Validation experiments using numerical brain, phantom, and human brains demonstrate its excellent performance, particularly in terms of acquisition speed, image quality, and robustness. Additionally, LMC-MOLED effectively corrects distortions introduced by long echo train acquisition.</p><p><strong>Conclusion and significance: </strong>LMC-MOLED offers a rapid, robust solution for mqMRI, providing multi-parametric mapping in a single scan with signify- cantly reduced acquisition time. It holds potential to improve diagnostic accuracy and alleviate patient burden.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663898","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}