Medical & Biological Engineering & Computing最新文献

{"title":"Improving practical steerability of a bidirectional steerable catheter for ERCP by non-straight wire routing.","authors":"Atsushi Yamada, Wataru Yonemichi, Ikuo Tooyama, Osamu Inatomi","doi":"10.1007/s11517-026-03670-5","DOIUrl":"https://doi.org/10.1007/s11517-026-03670-5","url":null,"abstract":"<p><p>Endoscopic retrograde cholangiopancreatography (ERCP) requires reliable bile duct cannulation, yet steerability of wire-driven steerable catheters (SCs) can deteriorate when the shaft is constrained, partly because actuation may induce unintended shaft-side deformation and transmission loss. This study evaluated whether non-straight wire-routing architectures could improve practical steerability of a bidirectional SC for ERCP. Three prototypes were developed: a conventional straight-wiring design (SC#0), a non-straight, non-crossed design (SC#1), and a non-straight, crossed design (SC#2). Steerability was assessed by incremental wire-pulling tests under straight-shaft and loose-shaft conditions and by a mock ERCP study using an actual duodenoscope. Outcomes included the handle displacement and pulling force required to reach 85° distal bending (HD₈₅, HPF₈₅) and target-angle reachability. In the straight-shaft condition, SC#1 and SC#2 reduced HD₈₅ and HPF₈₅ by 54-63% and 63-65%, respectively, compared with SC#0. Under the loose-shaft condition, SC#0 reached 85° in only 1/5 trials, whereas SC#1 and SC#2 reached 85° in all trials. In the mock ERCP study, the proposed designs reduced HD₈₅ and HPF₈₅ by 57-71% and 57-62%, respectively, in both bending directions. These findings indicate that non-straight wire-routing architectures can be practically implemented in a bidirectional SC for ERCP and may improve steerability under ERCP-relevant constraints.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876012","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":"Improving cellular nuclei detection in histological images by combining super-resolution techniques and deep learning-based object detectors.","authors":"Eduardo Díaz-Gaxiola, Ivan García-Aguilar, Arturo Yee-Rendón, Ines F Vega-López, Ezequiel López-Rubio, Rafael Marcos Luque-Baena","doi":"10.1007/s11517-026-03604-1","DOIUrl":"10.1007/s11517-026-03604-1","url":null,"abstract":"<p><p>Digital pathology has enabled large-scale analysis of histological images. However, accurate detection of cellular nuclei remains challenging due to variability in morphology, staining, and especially image resolution. Existing object detection approaches often degrade when applied to low-resolution images, and current solutions typically address either multi-scale detection or image enhancement independently. In this work, we propose a hybrid framework that integrates super-resolution with a dual-branch detection strategy combining full-image and patch-based inference. This design leverages both global contextual information and localized high-detail analysis to improve detection robustness. The outputs of both branches are fused through a confidence-weighted mechanism followed by non-maximum suppression and clustering-based refinement. The proposed method was evaluated on the NuCLS dataset, demonstrating consistent improvements over baseline detection approaches. In particular, the combined workflow achieved up to a 20% increase in mAP@0.5-0.95 at higher confidence thresholds, achieving competitive performance compared to state-of-the-art methods while maintaining a lightweight architecture. These results highlight the effectiveness of integrating super-resolution and multi-scale detection strategies for improving nuclei detection in histopathological images.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3543-3556"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13635039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148723961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation study of exoskeleton-musculoskeletal coupling for exoskeleton assistance via multi-agent reinforcement learning.","authors":"Lei Sun, Hui Li, Aofei Deng, Wei Wang","doi":"10.1007/s11517-026-03634-9","DOIUrl":"10.1007/s11517-026-03634-9","url":null,"abstract":"<p><p>To address the wearable exoskeletons' core bottlenecks, difficulty in personalized adaptation, over-reliance on iterative human experiments, and poor generalization across multiple working conditions. This study proposes a musculoskeletal-exoskeleton coupled assistance framework based on heterogeneous multi-agent reinforcement learning (MARL), with deep integration of biomechanical principles. A high-fidelity OpenSim simulation environment is constructed, incorporating a bilaterally symmetric 7 degrees of freedom (7-DOFs) lower-limb musculoskeletal model and a hip-assist exoskeleton, which are modeled as heterogeneous agents and trained under the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm's \"centralized training and distributed execution\" (CTDE) paradigm. Desired kinematic trajectories from healthy human gait data are embedded as biomechanical priors; a multi-objective reward function constrains joint tracking errors, posture instability, core muscle activation (aligning with \"core-auxiliary muscle\" load division law), and abrupt exoskeleton actions, while an Actor network action smoothness term suppresses control fluctuations to match human musculoskeletal dynamic response characteristics. Simulation results (0.86 m/s, 1.2 m/s and 1.5 m/s) demonstrate high-precision symmetric hip/knee kinematic tracking; the iliopsoas peak force reduced from > 3000 N to < 1600 N; and by 10-25% lower ground reaction force (GRF) bimodal peaks. This work verifies that biomechanics-integrated heterogeneous MARL can learn robust, speed-adaptive assistance policies in simulation, offering a scalable pathway to accelerate exoskeleton controller development and address long-standing industry challenges.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3231-3247"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148551215","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":"AV-NeXt: a topology-aware framework with interaction modeling and competitive gating for retinal artery/vein segmentation.","authors":"Mingyang Sun, Xiaoyang He","doi":"10.1007/s11517-026-03633-w","DOIUrl":"10.1007/s11517-026-03633-w","url":null,"abstract":"<p><p>Automated segmentation and classification of retinal arteries and veins (A/V) are pivotal for the precise morphological characterization required in the early diagnosis of systemic cardiovascular and ophthalmic pathologies. However, existing deep learning paradigms exhibit limitations in resolving complex structural entanglement at A/V crossings, frequently resulting in vascular disconnection and classification ambiguity due to the insufficient modeling of local artery-vein interactions. To address these challenges, AV-NeXt, a topology-aware framework, is proposed to improve crossing-region recognition by integrating anatomically motivated A/V interaction modeling into deep feature representations. Constructed upon a ConvNeXt backbone, the framework incorporates two pivotal components: (1) an Interaction-conditioned Crossing Head, which exploits feature-level co-activation cues between artery and vein streams to assist crossing prediction; and (2) a Competitive Gating Mechanism, designed to dynamically reduce crossing-induced classification ambiguity through soft gating. Extensive experiments on Fundus-AVSeg demonstrated that AV-NeXt achieved the best overall performance among the compared methods, with an mDice of 76.72%, an mIoU of 63.64%, and a Crossing IoU of 44.85%. Paired statistical analysis further supported the reliability of the crossing-region gains, while zero-shot evaluation on RITE showed competitive crossing recognition under domain shift, with a Crossing IoU of 28.52%.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3249-3263"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148594421","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":"Prospective motion correction with data reacquisition based on fat navigators for T1-weighted brain MRI at 3T.","authors":"Minqiang Jia, Bingbing Zhao, Tuo Yu, Jiayu Zhu, Xijing Zhang, Haohao Peng, Xin Ye, Yichen Zhou, Rongrong Xie, Xinju Zhang, Han Zhang, Xiaopeng Zong","doi":"10.1007/s11517-026-03622-z","DOIUrl":"10.1007/s11517-026-03622-z","url":null,"abstract":"<p><p>T1-weighted (T1w) sequence is widely employed in 3T clinical and research MRI. However, its image quality is vulnerable to motion artifacts. We aim to develop a robust prospective motion correction (PMC) method for T1w sequence based on fat navigators (FatNav). Since residual artifacts are often present after PMC, we also evaluated the effectiveness of data reacquisition in reducing such artifacts. Twenty healthy adults and six pediatric participants were scanned with PMC on and off while performing controlled or random head motion. To achieve PMC, the field of view was adjusted in real time based on motion parameters derived from fat navigators. Data acquired between two neighboring navigators that had relative motion score above a certain threshold were reacquired. Image quality was assessed using tissue boundary blurriness, SSIM, PSNR, and cortical and white matter volumes. For images acquired with controlled head movement, PMC with reacquisition significantly increased visual assessment scores from 1.42 ± 0.69 to 3.67 ± 0.76, indicating reduced visible artifacts, and significantly reduced blurriness (p = 0.001) with a mean reduction of 18.6% compared with PMC-off images. In parallel, cortical volume, SSIM, and PSNR significantly increased after PMC (p = 0.012, p = 0.027, and p = 0.043, respectively). Blurriness and cortical volume were fitted against the number of reacquired TRs using a linear model, which suggested an approximately linear trend under the tested conditions. However, the slope of the linear fit was significantly different (p = 0.019 and p = 0.030, respectively) from zero only when the motion scores of the reacquired TR were above a certain threshold. For images acquired with random head motion, visible artifact levels were also reduced with PMC and reacquisition compared to those with PMC off. PMC based on FatNav effectively reduces the impact of motion for T1-weighted MRI at 3 T. Reacquiring data with motion scores above a certain threshold can further improve image quality.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3265-3282"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148594419","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":"An integrated fluid-dynamic and structural pipeline to assess atrial hemodynamics before and after left atrial appendage occluder deployment in a patient-specific anatomy.","authors":"Benigno Marco Fanni, Francesca Danielli, Chiara Bonfanti, Ilaria Verdirame, Francesca Berti, Emanuele Gasparotti, Sergio Berti, Giancarlo Pennati, Lorenza Petrini, Simona Celi","doi":"10.1007/s11517-026-03655-4","DOIUrl":"10.1007/s11517-026-03655-4","url":null,"abstract":"<p><p>Atrial fibrillation is associated with a high risk of thromboembolic events, often mitigated by left atrial appendage occlusion (LAAO) when anticoagulation is contraindicated. This study presents a patient-informed computational workflow integrating pre-operative computational fluid dynamics (CFD), finite element (FE) modeling of device deployment, and post-operative CFD informed by the FE-deformed configuration. A proof-of-concept case was analyzed considering three atrial wall stiffness values to explore biomechanical variability. Pre-implant CFD simulations identified regions of blood stasis within the left atrial appendage, highlighting areas of thrombotic risk. FE simulations of device deployment demonstrated that wall stiffness influenced device positioning, orientation, and contact with the atrial anatomy. Post-implant CFD analyses, incorporating both the FE-informed device configuration and two fabric reconstruction strategies (conformal vs. detached), revealed flow redirection and changes in wall shear stress consistent with effective occlusion and the impact of fabric variability. Overall, the proposed workflow enabled a coherent assessment of mechanical and hemodynamic outcomes, providing insights into how anatomical and biomechanical factors affect post-operative LA hemodynamics. This multiphysics approach offers a step toward a patient-informed computational framework for assessing device deployment and thrombogenic risk under complete occlusion conditions.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3605-3622"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800785","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":"An interactive EMG-EMS system for assisted exercise monitoring: a user-centered evaluation.","authors":"Javier Jimenez-Honrado, Felipe Costa-Tebar, Gabriel Sebastián-Rivera, Jose A Gallud","doi":"10.1007/s11517-026-03646-5","DOIUrl":"10.1007/s11517-026-03646-5","url":null,"abstract":"<p><p>Surface electromyography (sEMG) and electrical muscle stimulation (EMS) are increasingly used in rehabilitation and training. However, turning EMG-EMS setups into usable end-to-end interactive experiences remains challenging due to real-time constraints, safety, and user-facing workflow complexity. We investigate an interactive EMG-EMS exercise workflow that supports exercise execution and post-session reflection, and we evaluate it from an interaction-centered perspective focusing on usability, learnability, perceived usefulness, and comfort. The system integrates real-time sEMG visualization with session history and session comparison for reflective analysis. During the study, EMS assistance was delivered in a manual, operator-mediated manner, in which the application provided real-time stimulation cues that guided manual adjustments of a commercial EMS device. A small exploratory pilot study with healthy participants ([Formula: see text]) examined the full interaction workflow in two sequential phases (sEMG-only followed by EMS-assisted), using think-aloud, task-level performance logging, and questionnaires (SUS and domain-specific). The evaluation showed positive perceived usability and user experience (SUS mean: 78.95/100), with participants particularly valuing session history and comparison as tools for post-exercise reflection. Real-time sEMG feedback was delivered with stable behavior and an effective processing delay on the order of tens of milliseconds. At the same time, the study revealed recurring challenges inherent to EMG-EMS interaction, including sensor onboarding, electrode placement, and users' interpretation of sEMG-derived feedback, which point toward important directions for future system design. Findings provide preliminary support for the feasibility of an interactive EMG-EMS exercise experience with positive perceived usability and comfort in a healthy, technologically literate pilot sample. However, the results should be interpreted as exploratory because of the small sample, sequential study design, and operator-mediated EMS delivery. These findings motivate HCI-focused iterations to improve onboarding, interpretability, and future evaluation with more reliable and automated hardware.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3441-3451"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680776","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":"Monitoring changes in mixed venous oxygen saturation using photoplethysmography on the sternum - a proof of concept study.","authors":"Erik Näslund, Gunnar Strandberg, Jacob Karlsson, Stephanie Franzén, Robert Frithiof","doi":"10.1007/s11517-026-03648-3","DOIUrl":"10.1007/s11517-026-03648-3","url":null,"abstract":"<p><p>Mixed venous oxygen saturation (SvO₂) is a valuable indicator of the balance between oxygen delivery and consumption in the body. However, the invasiveness and risk of complications limit its use. This study aimed to test the feasibility of continuously monitoring variations in SvO₂ from photoplethysmography (PPG) measurements of sternal intramedullary oxygen saturation. In an animal model, including anesthetized pigs (n = 15, mean 33.1 (SD 2.0) kg), alterations in SvO₂ were instituted by separate sessions: incremental decrease in arterial oxygen saturation using hypoxic gas mixtures (N₂/air), and hemorrhage of 20% plus 20% of estimated blood volume. Estimations of non-invasive sternal oxygen saturation (nSsO₂) were derived from calibrating PPG measurements against SvO₂ sampled from the pulmonary artery. Individual linear correlations were found between SvO₂ and PPG measurements. nSsO₂ and SvO₂ co-varied, except in severe hypovolemia. A concordance rate between nSsO₂ and SvO₂ was determined to 87% (95% CI 79.1-95.0). In this study, we demonstrated linear correlations between SvO₂ and PPG-measurements on the sternum, enabling estimation of nSsO₂, which trends with changes in SvO₂. The results support further technical development of non-invasive monitoring of SvO₂ from sternal measurements.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3325-3335"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13635036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148621872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-stage deep learning framework for the restoration of incomplete-ring PET images.","authors":"Yeqi Fang, Rong Zhou","doi":"10.1007/s11517-026-03641-w","DOIUrl":"10.1007/s11517-026-03641-w","url":null,"abstract":"<p><p>Positron Emission Tomography (PET) is an important molecular imaging tool widely used in medicine. Traditional PET systems rely on complete detector rings for full angular coverage and reliable data collection. However, incomplete-ring PET scanners have emerged due to hardware failures, cost constraints, or specific clinical needs. Standard reconstruction algorithms often suffer from performance degradation with these systems because of reduced data completeness and geometric inconsistencies. We present a two-stage deep-learning framework that, without incorporating any time-of-flight (TOF) information, restores high-quality images from data with about 50% missing coincidences-double the loss levels previously addressed by CNN-based methods. The pipeline operates in two stages: a projection-domain Attention U-Net first predicts the missing sections of the sinogram by leveraging spatial context from neighbouring slices, after which the completed data are reconstructed with OSEM algorithm and passed to a cascaded U-Net & warm-start diffusion model for image refinement. This module starts the reverse diffusion process from the U-Net coarse prediction rather than pure Gaussian noise. Using 613 simulated brain volumes from real scans (196 healthy brain samples, 217 Alzheimer's disease samples, and 200 Mild Cognitive Impairment samples), the results show that our model successfully preserves most anatomical structures and tracer distribution features with PSNR of 38.18 to 38.59 dB and SSIM of 0.9904 to 0.9925. Our two-stage deep-learning framework effectively restores high-quality PET images from over 50 % incomplete-ring data, achieving near-complete anatomical fidelity and robust performance without requiring TOF information.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3413-3425"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680792","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":"MDAGCN: A multimodal dynamic adaptive graph convolutional network for sleep staging.","authors":"Dongsheng Fang, Chu Li, Haoxuan Li, Xiaodong Yang","doi":"10.1007/s11517-026-03639-4","DOIUrl":"10.1007/s11517-026-03639-4","url":null,"abstract":"<p><p>Existing deep-learning-based sleep staging frameworks frequently rely on shared multimodal feature extractors, which may overlook modality-specific discriminative traits and insufficiently capture dynamic cross-modal dependencies. This study aims to improve automated sleep stage classification by proposing the Multimodal Dynamic Adaptive Graph Convolutional Network (MDAGCN) for heterogeneous polysomnography (PSG) analysis. MDAGCN employs specialized branches to capture the spatio-temporal characteristics of EEG, EOG, EMG, and ECG signals, and integrates multi-scale convolutions, lead-wise attention, and adaptive graph learning to model complementary multimodal interactions within a unified framework. Extensive evaluations on ISRUC-S3 and Sleep-EDF-78 demonstrate competitive performance. On ISRUC-S3, MDAGCN achieves an accuracy of 0.841, an F1-score of 0.825, and a Cohen's kappa of 0.795, matching the best reported accuracy and kappa and obtaining the highest F1-scores for the W, N2, and N3 stages. On Sleep-EDF-78, MDAGCN achieves the highest accuracy of 0.828 and Cohen's kappa of 0.766, improving over the strongest baseline by 0.3 and 0.6 percentage points, respectively. These results demonstrate the effectiveness of MDAGCN for multimodal sleep staging and highlight the importance of jointly modeling modality-specific characteristics and cross-modal physiological dependencies.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3623-3638"},"PeriodicalIF":3.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800806","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}