IEEE Transactions on Biomedical Engineering最新文献

{"title":"In Vivo Classification of Oral Lesions Using Electrical Impedance Spectroscopy.","authors":"Sophie A Lloyd, Torri E Lee, Ethan K Murphy, Allaire F Doussan, Jacob P Thones, Darcy A Kerr, Joseph A Paydarfar, Ryan J Halter","doi":"10.1109/TBME.2025.3581465","DOIUrl":"https://doi.org/10.1109/TBME.2025.3581465","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate a new non-invasive, handheld Electrical Impedance Spectroscopy (EIS) device for assessing oral lesions in real-life surgical scenarios.</p><p><strong>Methods: </strong>A custom-designed probe with a 33-electrode sensor array was used to collect impedance measurements across multiple frequencies (100 Hz - 100 kHz) from non-consecutive patients undergoing surgical resection of oral cancer. In vivo EIS measurements were recorded from lesion and healthy tissue surfaces before resection, with no clinical decisions based on impedance data.</p><p><strong>Results: </strong>The study included 26 participants (median [IQR] age, 64.3 [59 - 70] years; 11 (42%) female) with oral squamous cell carcinoma. Cancerous tissue was found to have significantly lower resistance and reactance than healthy tissue (p<0.0001). Tissue classification using the permittivity at 40 kHz showed the highest accuracy (88%) with an AUC of 0.88. Multiple impedance parameters achieved AUCs >0.85 for differentiating healthy from malignant tissue. Conclusion & Significance: The study indicates that EIS can effectively differentiate between healthy and cancerous oral mucosa through rapid, non-invasive intraoperative measurements. The data processing pipeline developed demonstrates success in maintaining high data quality amidst the external disturbances presented in intraoperative data collection.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333027","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":"From Frequency to Temporal: Three Simple Steps Achieve Lightweight High-Performance Motor Imagery Decoding.","authors":"Yuan Li, Diwei Su, Xiaonan Yang, Xiangcun Wang, Hongxi Zhao, Jiacai Zhang","doi":"10.1109/TBME.2025.3579528","DOIUrl":"https://doi.org/10.1109/TBME.2025.3579528","url":null,"abstract":"<p><strong>Objective: </strong>To address the challenges of high data noise and substantial model computational complexity in Electroencephalography (EEG)-based motor imagery decoding, this study aims to develop a decoding method with both high accuracy and low computational cost.</p><p><strong>Methods: </strong>First, frequency domain analysis was performed to reveal the frequency modeling patterns of deep learning models. Utilizing prior knowledge from brain science regarding the key frequency bands for motor imagery, we adjusted the convolution kernels and pooling sizes of EEGNet to focus on effective frequency bands. Subsequently, a residual network was introduced to preserve high-frequency detailed features. Finally, temporal convolution modules were used to deeply capture temporal dependencies, significantly enhancing feature discriminability.</p><p><strong>Results: </strong>Experiments were conducted on the BCI Competition IV 2a and 2b datasets. Our method achieved average classification accuracies of 86.23% and 86.75% respectively, surpassing advanced models like EEG-Conformer and EEG-TransNet. Meanwhile, the Multiply-accumulate operations (MACs) were 27.16M, a reduction of over 50% compared to the comparison models, and the Forward/Backward Pass Size was 14.33MB.</p><p><strong>Conclusion: </strong>By integrating prior knowledge from brain science with deep learning techniques-specifically frequency domain analysis, residual networks, and temporal convolutions-it is possible to effectively improve the accuracy of EEG motor imagery decoding while substantially reducing model computational complexity.</p><p><strong>Significance: </strong>This paper employs the simplest and most fundamental techniques in its design, highlighting the critical role of brain science knowledge in model development. The proposed method demonstrates broad application potential.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333026","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 Engineering in Medicine and Biology Society Publication Information","authors":"","doi":"10.1109/TBME.2025.3571974","DOIUrl":"https://doi.org/10.1109/TBME.2025.3571974","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 7","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11044977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323079","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":"Rapid 3D Saturation Transfer Imaging for Simultaneous Phosphocreatine and Glycogen Mapping in Human Muscle.","authors":"Xi Xu, Xinran Chen, Yuanyuan Liu, Chongxue Bie, Hao Wu, Siqi Cai, Sen Jia, Lin Chen, Dong Liang, Hairong Zheng, Yang Zhou, Yanjie Zhu","doi":"10.1109/TBME.2025.3581341","DOIUrl":"https://doi.org/10.1109/TBME.2025.3581341","url":null,"abstract":"<p><strong>Objective: </strong>Phosphocreatine (PCr) and glycogen are key metabolites underpinning the skeletal muscle contractions. Simultaneous 3D imaging of these metabolites is valuable for understanding heterogeneous energetic events. While saturation transfer (ST) MRI can detect metabolites, 3D ST acquisition generally requires long scan times. We developed a rapid, high-resolution 3D scanning scheme for simultaneous quantification of PCr and glycogen.</p><p><strong>Methods: </strong>A 3D sequence was implemented on a 5 T MR scanner, using a continuous-wave saturation pulse and golden-angle stack-of-stars readouts. A patch-based low-rank algorithm was incorporated to reduce scan time. The sensitivity of sequence to concentration variations was validated in phantom experiments, and metabolite distribution was assessed in vivo. Furthermore, exercise protocols were employed to investigate metabolic heterogeneity.</p><p><strong>Results: </strong>The optimized acquisition strategy reduced the scan time to 26.7% of full sampling. Phantom studies showed a linear correlation between contrast signals and metabolite concentrations, in-vivo studies demonstrated uniform PCr and glycogen distribution across slices. Post-exercise, PCr and glycogen depletion was clearly observed.</p><p><strong>Conclusion and significance: </strong>The 3D rapid ST imaging framework achieves 100 mm coverage of skeletal muscle in 11.2 minutes, showing the potential to monitor muscle physiological processes.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333028","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":"Camera-based Bipedal Plantar Pulse Transit Time Difference Measurement for Lower Limb Arterial Stenosis Detection.","authors":"Zhiyuan Xu, Shuhan Yi, Yukai Huang, Dongmin Huang, Zi Luo, Ningbo Zhao, Wenjin Wang","doi":"10.1109/TBME.2025.3580446","DOIUrl":"https://doi.org/10.1109/TBME.2025.3580446","url":null,"abstract":"<p><p>Peripheral arterial disease (PAD) can lead to severe foot problems, including claudication and amputation in extreme cases. Currently, clinical diagnosis primarily relies on costly and cumbersome methods like spectral Doppler ultrasound and Ankle-Brachial index (ABI). This highlights the urgent need for a low-cost and convenient screening approach. The lower extremity arterial stenosis caused by PAD leads to a delay in pulse wave transmission from the heart to the feet. This study proposes a novel PAD screening method, the bipedal plantar pulse transit time difference (PTTD), calculated as the time difference between photoplethysmographic (PPG) signals extracted from RGB videos of the feet. A simulation experiment was conducted on 19 healthy adult subjects, in which five different vascular obstruction conditions (i.e., PAD degrees) were simulated by applying varying pressures to the calf. The experimental results show that PTTD achieved 90.53% accuracy in PAD-simulation detection and 80.00% in five-class PAD-simulation grading, offering improvements of 10.53% and 28.42% over the baseline perfusion index (PI)-based detection and grading models, respectively. Additionally, we collected plantar video recordings from 10 PAD patients at the Department of Ultrasound in a hospital, demonstrating the feasibility in real clinical settings. This indicates that PTTD measured between bipedal plantars exhibits high sensitivity to vascular obstruction and holds promise as an efficient tool for PAD screening.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144325537","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":"EEG-fNIRS multilayer brain network analysis revealed functional neural reorganization of rTMS with motor training in stroke.","authors":"Jianeng Lin, Shuxiao Jin, Yugen You, Jinrui Liu, Jiewei Lu, Zhilin Shu, Yuxin Feng, Yaru Zhang, Hui Xiao, Ying Zhang, Jing Wang, Xintong Zhao, Chunfang Wang, Jianda Han, Ningbo Yu","doi":"10.1109/TBME.2025.3580943","DOIUrl":"https://doi.org/10.1109/TBME.2025.3580943","url":null,"abstract":"<p><strong>Objective: </strong>Repetitive transcranial magnetic stimulation (rTMS) is an effective non-invasive neuromodulation technique promoting motor function recovery in stroke patients. Our study aimed to reveal the functional neural reorganization of rTMS with motor training in stroke from a comprehensive multimodal perspective.</p><p><strong>Methods: </strong>This study proposed a novel EEG-fNIRS multilayer brain network analysis method to investigate the hemisphere activation and neuroplasticity changes and conducted clinical study. Specifically, the EEG-fNIRS signals were first reconstructed and aligned in the unified cortical source space. Then, the neurovascular coupling strength was quantified by subject-specific estimation of the hemodynamic response function and utilized to build the interlayer edges. Subsequently, the unimodal intra-layer edge and bimodal inter-layer edge were combined to construct the multilayer brain network, of which features were extracted. 27 stroke patients and 13 healthy controls were recruited in the clinical experiment.</p><p><strong>Results: </strong>We found that the rTMS group showed significant improvement in the neurovascular coupling levels and multiplex clustering coefficients compared with the sham group. Moreover, these neural changes were significantly correlated with the motor function improvements (R = 0.600 and 0.618). The proposed method reduces the prediction error for rehabilitation outcomes by an average of 20.36% compared to unimodal approaches.</p><p><strong>Conclusion: </strong>The results indicated that our method effectively reveals the functional neural reorganization of rTMS with motor training in stroke.</p><p><strong>Significance: </strong>This work provides a novel method to empower neuroelectric-hemodynamic analysis and a unique insight into the mechanisms of stroke recovery and the therapeutic potential of rTMS in combination with motor training.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144325538","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":"Modeling Latent Dynamics of the Autonomic Nervous System in Response to Trauma Recall and Non-Invasive Vagus Nerve Stimulation.","authors":"Asim H Gazi, Sungtae An, Jesus Antonio Sanchez-Perez, Michael Chan, Mohammad Nikbakht, David J Lin, Shlok Natarajan, Kyle A Johnsen, J Douglas Bremner, Jin-Oh Hahn, Omer T Inan, Christopher J Rozell","doi":"10.1109/TBME.2025.3580051","DOIUrl":"https://doi.org/10.1109/TBME.2025.3580051","url":null,"abstract":"<p><strong>Objective: </strong>To develop a person-specific dynamic modeling approach to quantify the time course of multiple peripheral markers of the autonomic nervous system (ANS) in response to acute stressors and potential stress-reducing interventions.</p><p><strong>Methods: </strong>We curated data (N=50 participants) from a double-blind, randomized, sham-controlled trial of non-invasive vagus nerve stimulation (nVNS) for posttraumatic stress disorder (PTSD). For each participant, a multi-input, multi-output, linear state space model (SSM) was trained on $sim$3500 s of cardiovascular and respiratory marker time series data and tested for predictive validity on $sim$2000 s of held-out data. The inputs to each SSM indicated the presence or absence of potential stressors (e.g., traumatic memories) and interventions (e.g., nVNS or sham stimulation). We analyzed the SSMs' step responses to each input and compared the responses to real data. We then simulated the effects of just-in-time nVNS delivered during a traumatic memory.</p><p><strong>Results: </strong>The SSMs outperformed baseline forecasting methods on held-out data (P$< $.05). Responses to nVNS were in the opposite direction of responses to traumatic memories, with neutral conditions (included for comparison) remaining in between. For participants with PTSD, just-in-time nVNS attenuated-and briefly reversed-the response to traumatic memories along the principal axis of variance, which explained $sim$50% or more variance and mirrored expected ANS changes. Just-in-time sham stimulation produced no attenuation.</p><p><strong>Conclusion: </strong>Our methods capture latent ANS dynamics during increasing and decreasing stress levels associated with traumatic memories and nVNS, respectively.</p><p><strong>Significance: </strong>Traumatic memories can cause pathological stress responses during daily life that just-in-time non-invasive neuromodulation may potentially help mitigate.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309900","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":"Partial Domain Adaptation for Stable Neural Decoding in Disentangled Latent Subspaces.","authors":"Puli Wang, Yu Qi, Gang Pan","doi":"10.1109/TBME.2025.3577222","DOIUrl":"https://doi.org/10.1109/TBME.2025.3577222","url":null,"abstract":"<p><strong>Objective: </strong>Brain-Computer Interfaces (BCI) have demonstrated significant potential in neural rehabilitation. However, the variability of non-stationary neural signals often leads to instabilities of behavioral decoding, posing critical obstacles to chronic applications. Domain adaptation technique offers a promising solution by obtaining the invariant neural representation against non-stationary signals through distribution alignment. Here, we demonstrate domain adaptation that directly applied to neural data may lead to unstable performance, mostly due to the common presence of task-irrelevant components within neural signals. To address this, we aim to identify task-relevant components to achieve more stable neural alignment.</p><p><strong>Methods: </strong>In this work, we propose a novel partial domain adaptation (PDA) framework that performs neural alignment within the task-relevant latent subspace. With pre-aligned short-time windows as input, the proposed latent space is constructed based on a causal dynamical system, enabling more flexible neural decoding. Within this latent space, task-relevant dynamical features are disentangled from task-irrelevant components through VAE-based representation learning and adversarial alignment. The aligned task-relevant features are then employed for neural decoding across domains.</p><p><strong>Results: </strong>Using Lyapunov theory, we analytically validated the improved stability of late our neural representations through alignment. Experiments with various neural datasets verified that PDA significantly enhanced the cross-session decoding performance.</p><p><strong>Conclusion: </strong>PDA successfully achieved stable neural representations across different experimental days, enabling reliable long-term decoding.</p><p><strong>Significance: </strong>Our approach provides a novel aspect for addressing the challenge of chronic reliability in real-world BCI deployments.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309901","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":"ECG-Based Detection of Acute Myocardial Infarction using a Wrist-Worn Device.","authors":"Karolina Janciuleviciute, Daivaras Sokas, Justinas Bacevicius, Leif Sornmo, Andrius Petrenas","doi":"10.1109/TBME.2025.3580154","DOIUrl":"https://doi.org/10.1109/TBME.2025.3580154","url":null,"abstract":"<p><strong>Background: </strong>A wrist-worn wearable device for acquiring limb and chest ECG leads (wECG) may constitute a promising approach to detection of acute myocardial infarction (AMI). However, it remains to be demonstrated whether the information conveyed by the wECG is sufficient for AMI detection.</p><p><strong>Objective: </strong>To explore explainable machine learning models for detecting AMI using the wECG.</p><p><strong>Methods: </strong>Two types of machine learning models are explored: a convolutional neural network (CNN) using the raw ECG as input and a gradient-boosting decision tree (GBDT) using clinically informative features. 123 participants were included, divided into patients with AMI, patients with other cardiovascular diseases, and healthy individuals. A wristworn device equipped with three biopotential electrodes was used to acquire two ECG leads with a single touch: limb lead I and another lead involving a specific body site, i.e., either the V3 or V5 electrode positions, or the abdomen.</p><p><strong>Results: </strong>The best performance on the test dataset is obtained using models that incorporate all four leads. The CNN model performs slightly better than the GBDT model, with a sensitivity of 0.77 and specificity of 0.75 compared to 0.77 and 0.72, respectively. When distinguishing between AMI and healthy participants, the specificity increases to 0.94 for the CNN model and 0.90 for the GBDT model. Feature importance analysis shows that the GBDT model primarily relies on the J point, while the CNN model primarily relies on the QRS complex.</p><p><strong>Conclusions: </strong>wECG-based AMI detection shows considerable promise in out-of-hospital settings. However, caution is needed as CNN explanations rarely agree with the ECG intervals typically analyzed in clinical practice.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309899","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":"Ultra-low-field Balanced Steady-state Free Precession MRI at 0.05 Tesla.","authors":"Ye Ding, Varut Vardhanabhuti, Fan Huang, Linfang Xiao, Shi Su, Jiahao Hu, Junhao Zhang, Vick Lau, Christopher Man, Yujiao Zhao, Alex T L Leong, Ed X Wu","doi":"10.1109/TBME.2025.3580111","DOIUrl":"https://doi.org/10.1109/TBME.2025.3580111","url":null,"abstract":"<p><strong>Objective: </strong>The high cost and limited accessibility of MRI scanners remain significant barriers to their broader use in clinical settings. This study aims to demonstrate the feasibility of balanced steady-state free precession (bSSFP) imaging at ultra-low-field (ULF) on a highly simplified and low-cost 0.05 Tesla whole-body MRI scanner.</p><p><strong>Methods: </strong>Experiments were conducted using a newly developed 0.05 Tesla MRI scanner that employed a permanent magnet without the need for magnetic or radiofrequency shielding. We optimized the bSSFP protocol for imaging the brain, spine, chest, abdomen, pelvis, and knee in healthy volunteers. We also examined the dependency of tissue contrast on the excitation flip angle.</p><p><strong>Results: </strong>The bSSFP protocols demonstrated reasonable image quality at 0.05 Tesla, allowing visualization of various anatomical structures. The protocols provided a spatial resolution of 2×2×6 mm3 with approximately 5 minutes of scan time per protocol. Good soft tissue contrasts were shown, facilitating the identification of major tissue types within each structure. Although bSSFP exhibited predominantly T2/T1 contrast, it could be adjusted to some extent by varying the flip angle.</p><p><strong>Conclusion: </strong>The bSSFP sequence is particularly effective for imaging at ULF due to the substantially decreased tissue T1 values. This study demonstrates that imaging various anatomical structures with bSSFP at 0.05 Tesla is efficient and feasible.</p><p><strong>Significance: </strong>Such bSSFP protocol benefits from ULF and can provide superior soft tissue contrasts compared to CT and ultrasound. This ULF bSSFP approach may offer a cost-effective alternative for imaging soft tissues in clinical settings lacking traditional MRI access.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309902","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}