IEEE Transactions on Biomedical Engineering最新文献

{"title":"Training an Unconstrained 6 DOF Biomimetic Robotic Eye With Deep Reinforcement Learning","authors":"Miguel E. Teixeira;A. John van Opstal;Reza Javanmard Alitappeh;Alexandre Bernardino","doi":"10.1109/TBME.2026.3705372","DOIUrl":"10.1109/TBME.2026.3705372","url":null,"abstract":"Understanding how the brain controls rapid eye movements, known as saccades, remains an open challenge. In this work, we develop a biologically plausible computational model of a biomimetic eye, with six degrees-of-freedom (DOFs), to explore the control signals that drive these rapid ocular movements, and further compare their movement characteristics with known human behaviour. We hypothesise that saccade generation is governed by optimising a total reward, penalising costs such as inaccuracy, duration, energy and tendon tension, and train a model-free deep reinforcement learning algorithm under this constraint. Our results show that the emerging control strategies approximate human-like saccadic characteristics, including the nonlinear <italic>main sequence</i> relationships, compliance with Listing’s and Donders’ Laws, straight oblique trajectories, normometric pulse-step-like controls, and the antagonistic pairing of extraocular muscles, without explicitly enforcing these behaviours. We further analysed the evolution of the different costs during learning, and the impact of noise on the resulting control strategies and corresponding motions.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2843-2852"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148277269","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":"Stimulus Optimization Using the Artificial Bee Colony Algorithm for Visual Neuroprostheses","authors":"Ariastity Pratiwi;Jorge Jara-Balsera;Hipólito Guzmán-Miranda;Gregg Suaning;Alejandro Barriga-Rivera","doi":"10.1109/TBME.2026.3706302","DOIUrl":"10.1109/TBME.2026.3706302","url":null,"abstract":"<italic>Objective:</i> Optimization of electrical stimulation patterns can enhance the performance of visual neuroprosthetics by replicating the selective and temporally precise neural responses characteristic of natural vision. <italic>Methods:</i> An in silico approach employing the Artificial Bee Colony (ABC) algorithm was developed to optimize electrical stimulus waveforms that evoke physiologically realistic responses in retinal ganglion cells (RGCs). The optimization was guided by a computational model of ON and OFF RGCs, with the goal of matching recorded neural responses from the rat dorsolateral geniculate nucleus (dLGN). The ABC algorithm iteratively modified the amplitude envelope of charge-balanced, biphasic pulse trains to maximize similarity between simulated and recorded responses. <italic>Results:</i> The ABC algorithm consistently achieved <inline-formula><tex-math>$geq$</tex-math></inline-formula>80% cross-correlation with target neural responses within 2000 iterations. It also identified single optimized waveforms capable of eliciting distinct responses in neighboring neurons, indicating potential for simultaneous physiological responses with electrical stimulations. Under constrained computational conditions, ABC demonstrated superior accuracy compared to alternative optimization algorithms. <italic>Conclusion:</i> The proposed ABC-based optimization approach effectively generates neural activation patterns closely resembling natural responses, supporting its use for real-time and adaptive stimulation control in visual neuroprosthetic systems. <italic>Significance:</i> This study introduces a biologically inspired optimization framework that advances the development of intelligent stimulation strategies to enhance the functionality of next-generation visual neuroprostheses.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2829-2842"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11574610","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148301553","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":"Neural Style Transfer-Based Denoising of Seismocardiogram Signals Under Dynamic Conditions","authors":"Dunya Moradi;Berke Kizir;Beren Semiz","doi":"10.1109/TBME.2026.3688416","DOIUrl":"10.1109/TBME.2026.3688416","url":null,"abstract":"Seismocardiogram (SCG) signals capture the mechanical dynamics of cardiac activity, but their clinical utility is severely limited by motion artifacts during ambulatory monitoring. To overcome this challenge, we propose a neural style transfer (NST)–based denoising framework that converts motion-contaminated SCG recordings into morphology-preserving, rest-like representations. Our method leverages time–frequency spectrograms obtained from continuous wavelet transforms and a pre-trained convolutional neural network (VGG19) to suppress exercise-induced distortions while maintaining physiologically relevant timing and morphology. Across 20 participants, the proposed approach substantially enhanced signal fidelity, improving signal-to-noise ratio and peak signal-to-noise ratio by 176.7% and 152.1%, respectively, and reducing mean squared error and mean absolute error by 95.1% and 83.6%. Structural similarity increased by 70.3%, and correlation with the resting reference more than doubled. Heart rate estimated from denoised signals showed excellent agreement with electrocardiogram measurements, yielding an average error of only 0.89 beats per minute (0.73%). Furthermore, comparative evaluation demonstrated that the proposed restyling and denoising approach consistently outperformed state-of-the-art denoising techniques—including empirical mode decomposition variants, variational mode decomposition, Savitzky–Golay filtering, moving-average filtering, and wavelet-based reconstruction—achieving the lowest heart rate estimation error (0.89 bpm RMSE). Additionally, a controlled simulation confirmed the framework's restyling capability under known ground-truth conditions, yielding a heart rate estimation error of only 0.15 bpm relative to the true reference. These results demonstrate that neural style transfer enables physiology-consistent reconstruction of cardiac mechanical signals in dynamic conditions and represents a highly promising direction toward motion-resilient wearable cardiac monitoring.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2682-2694"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147814533","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":"Investigating High-Order Behaviors in Multivariate Cardiovascular Interactions via Nonlinear Prediction and Information-Theoretic Tools","authors":"Chiara Barà;Yuri Antonacci;Helder Pinto;Laura Sparacino;Michal Javorka;Sebastiano Stramaglia;Luca Faes","doi":"10.1109/TBME.2026.3689598","DOIUrl":"10.1109/TBME.2026.3689598","url":null,"abstract":"<italic>Objective:</i> Assessing the synergistic high-order behaviors (HOBs) that emerge from underlying structural mechanisms is crucial to characterize complex systems. This work leverages the combined use of predictability and information-theoretic measures to detect and quantify HOBs in synthetic and physiological network systems. <italic>Methods:</i> After providing formal definitions of mechanisms and behaviors in a complex system, measures of statistical synergy are defined as the whole-minus-sum (WMS) excess of mutual predictability (<inline-formula><tex-math>$Delta _text {MP}$</tex-math></inline-formula>) or mutual information (<inline-formula><tex-math>$Delta _text {MI}$</tex-math></inline-formula>) observed when considering the system as a whole rather than as a combination of its units. The two measures are computed using model-free methods based on nonlinear prediction and entropy estimation. <italic>Results:</i> The application to simulated linear Gaussian systems and nonlinear deterministic and stochastic dynamic systems shows that <inline-formula><tex-math>$Delta _text {MP}$</tex-math></inline-formula> tends to vanish for target variables influenced by additive effects of single independent source variables and is positive in the presence of group interactions between sources, while <inline-formula><tex-math>$Delta _text {MI}$</tex-math></inline-formula> exhibits a higher propensity to display positive values. Then, the analysis of physiological variables shows significant values of <inline-formula><tex-math>$Delta _text {MI}$</tex-math></inline-formula> when investigating the additive effect of systolic and diastolic arterial pressure on mean arterial pressure, and of both <inline-formula><tex-math>$Delta _text {MP}$</tex-math></inline-formula> and <inline-formula><tex-math>$Delta _text {MI}$</tex-math></inline-formula> when assessing how diastolic pressure is modulated by pre-ejection period and left-ventricular ejection time. <italic>Conclusion:</i> HOBs can be more clearly identified by information-theoretic WMS measures, while prediction WMS measures appear more sensitive to synergy arising from the governing rules of the system analyzed rather than from pure statistical dependencies. <italic>Significance:</i> Quantifying HOBs through WMS measures sensitive to complex structural mechanisms can provide new biomarkers to assess physio-pathological alterations of cardiovascular networks.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2736-2745"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11503394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147814589","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":"Advancing Electrolaryngeal Speech Enhancement Through Speech–Text Representation Learning","authors":"Ding Ma;Jinyi Mi;Fengji Li;Lester Phillip Violeta;Jiajun He;Wenchin Huang;Kazuhiro Kobayashi;Tomoki Toda","doi":"10.1109/TBME.2026.3694703","DOIUrl":"10.1109/TBME.2026.3694703","url":null,"abstract":"<italic>Objective:</i> laryngectomees depend on an electromechanical device to generate electrolaryngeal (EL) speech for verbal communication. Compared with normal speech, EL speech suffers from severe distortion, limited phonetic variation, unnatural prosody, and temporal shifts, degrading naturalness and intelligibility. Although sequence-to-sequence (seq2seq) voice conversion (VC) based EL-speech-to-normal-speech conversion (EL2SP) is promising, substantial mismatches between EL and normal speech inevitably cause cumulative mapping errors that limit performance. To address this, we describe a novel representation learning framework integrating speech and text representations to improve mapping and reconstruction quality within a seq2seq VC model. <italic>Methods:</i> our methodology comprises two main stages: 1) representation integration and learning, and 2) reconstruction training. A network capable of incorporating auxiliary text information is first constructed with pretrained modules to learn speech–text-based integrated representations. Then, an autoencoder-style reconstruction strategy finalizes EL2SP model to inherit these representations without increasing model complexity. Additional optimization designs are performed across these stages. We introduce three fusion strategies including middle-, input-, and hybrid-level fusion strategies that progressively enhance learning. Moreover, besides standard seq2seq VC objectives, an additional reconstruction loss on the integrated representation is introduced to refine representation transfer. <italic>Results:</i> experiments under different EL2SP datasets consistently demonstrate that our methods, combined with data augmentations, outperform baselines relying solely on speech representations regarding both conversion quality and intelligibility. Furthermore, progressive improvements with system design depth validate the effectiveness of our methods. <italic>Significance:</i> the proposed methods provide an extensible and practical methodology for EL speech enhancement and assistive communication technologies.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2861-2875"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147972237","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 Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework","authors":"Yasuyuki Kataoka;Kazunori Uemura;Mitsuji Sampei;Yukiko Fukuda;Jon Peterson;Takuya Nishikawa;Keita Saku;Joe Alexander;Kenji Sunagawa","doi":"10.1109/TBME.2026.3706753","DOIUrl":"10.1109/TBME.2026.3706753","url":null,"abstract":"<italic>Objectives:</i> This study develops a model-based control framework integrating a left ventricular assist device (LVAD) with drug therapy to regulate multidimensional hemodynamics (mean arterial pressure, cardiac output, and left and right atrial pressures) while minimizing myocardial oxygen consumption (<inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula>) in acute heart afailure (AHF). By modeling the hemodynamic and <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> effects of partial and total LVAD support, the framework enables combined drugs–LVAD therapy for severe AHF cases where drug therapy alone is not effective. <italic>Methods:</i> A previously developed hemodynamic control framework was extended to incorporate LVAD support. Using circulatory equilibrium models under LVAD support, an analytical <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> model encompassing both LVAD modes was derived from the relationship between <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> and the ventricular pressure-volume area. An optimal controller explicitly accounts for transitions between partial and total LVAD support. In our cardiovascular simulator, we validated the <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> model and evaluated the framework’s multidimensional hemodynamic regulation and <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> minimization capabilities. <italic>Results:</i> The <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> model accurately predicted <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> across the two LVAD modes. In a severe AHF scenario, drug therapy or LVAD support alone failed, whereas the combined drugs–LVAD controller succeeded in achieving both hemodynamic regulation and <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> minimization. Compared with drug therapy alone, drugs–LVAD control using the proposed <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> model halved <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> while achieving multidimensional regulation. <italic>Conclusions:</i> Our drugs–LVAD framework may enable multidimensional hemodynamic regulation while reducing <inline-formula><tex-math>$MVO_{2}$</tex-math></inline-formula> in severe AHF. Modeling both partial and total LVAD support may be essential for hemodynamic management. <italic>Significance:</i> This work provides a theoretical basis for integrated drugs–LVAD control to achieve target hemodynamics while reducing cardiac workload.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2695-2708"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11592159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148368351","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":"IEEE Transactions on Biomedical Engineering Handling Editors Information","authors":"","doi":"10.1109/TBME.2026.3707115","DOIUrl":"https://doi.org/10.1109/TBME.2026.3707115","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"C4-C4"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11647107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148699531","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":"A Progressive Gaussian Splatting Framework for Monocular-Only High-Fidelity Surgical Scene Reconstruction","authors":"Yuchao Zheng;Jianing Zhang;Yutong Wu;Hongen Liao;Guochen Ning","doi":"10.1109/TBME.2026.3701768","DOIUrl":"10.1109/TBME.2026.3701768","url":null,"abstract":"Accurate and reliable 3D scene reconstruction is a key component of intelligent surgery, enabling enhanced spatial understanding and data-driven analysis in minimally invasive surgery (MIS). However, existing clinical systems are often bulky and workflow-incompatible, while vision-based Structure-from-Motion methods struggle with sparse textures and specularities, leading to unstable pose estimation and high computational cost. To address these limitations, we present SurGSplat++, a progressive, pose-free Gaussian splatting framework for monocular surgical scene reconstruction that requires no auxiliary hardware or pre-computed camera poses. Experiments show that SurGSplat++ achieves improved geometric stability, reduced pose drift, and superior novel-view synthesis compared with existing approaches. By producing accurate and consistent 3D reconstructions, the proposed method provides a practical solution for post-operative analysis, pre-operative planning, and data-driven surgical modeling in clinical environments.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2911-2921"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148210696","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":"Physiology-Aware Temporal Attention and Frequency Band Selection for Robust Respiratory Sound Analysis","authors":"Nouhaila Fraihi;Ouassim Karrakchou;Mounir Ghogho","doi":"10.1109/TBME.2026.3701932","DOIUrl":"10.1109/TBME.2026.3701932","url":null,"abstract":"<italic>Objective:</i> We present a physiology-aware and efficient framework for respiratory sound classification. <italic>Methods:</i> The framework combines a CNN backbone for local feature extraction with Phase-Informed Saliency Temporal Attention (PISTA), which integrates respiratory-phase cues, acoustic salience, and temporal locality to focus attention on diagnostically relevant time regions. An importance-guided Frequency Band Selection (FBS) module removes non-relevant spectral bands. Multi-axis Group Distributionally Robust Optimization (GroupDRO) improves robustness to class, demographic, and recording-condition imbalance. Evaluation is conducted on ICBHI 2017 and SPRSound 2022/2023, with real-time profiling on a Raspberry Pi 3. <italic>Results:</i> The method achieves state-of-the-art performance on SPRSound 2022/2023 and competitive results on ICBHI 2017, while FBS reduces spectral dimensionality and computation by up to 50%. Integrating the proposed modules into transformer baselines yields new state-of-the-art results on ICBHI. Real-time inference on Raspberry Pi 3 confirms edge feasibility. <italic>Conclusion:</i> The proposed system enables accurate and efficient respiratory sound classification. <italic>Significance:</i> The framework supports reliable, real-time auscultation in low-resource and point-of-care settings.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2758-2770"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148210783","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":"Non-Invasive BP Estimation via Carotid-Brachial-Radial Coupling: A Physics-Informed Asymmetric Cycle Network Approach","authors":"Yuxuan Lin;Yihong Yu;Hui Zhang;Xiaorong Ding","doi":"10.1109/TBME.2026.3704348","DOIUrl":"10.1109/TBME.2026.3704348","url":null,"abstract":"Photoplethysmogram (PPG) based cuffless blood pressure (BP) monitoring holds significant potential to revolutionize hypertension management. However, its widespread adoption is hampered by limited accuracy and poor interpretability, primarily due to the lack of central arterial information and physical constraints. To address the inability of PPG to capture central hemodynamic drivers, we proposed a novel multimodal approach that leveraged multi-site physiological coupling from the carotid, brachial, and radial arteries, integrated into a physics-informed deep learning framework called the PIAC-Net. Our method integrated carotid blood flow velocity – measured using an ultrasound sensor – as a key central physiological signal, thereby supplementing the peripheral information provided by PPG alone. The PIAC-Net incorporated a set of hybrid physical loss functions that integrated 1D Navier-Stokes equations with 0D Windkessel boundary conditions, thereby enforcing strict hemodynamic conservation constraints on the latent brachial waveform used as the BP reference. Five-fold cross-validation showed superior accuracy than compared methods. The mean absolute errors of systolic/diastolic BP on the synthetic (N = 4,374) and in-house (N = 1,765) datasets were 1.82/1.51 mmHg and 7.08/6.38 mmHg, respectively. Ablation studies demonstrated that integrating carotid information reduced estimation errors by over 50%, serving as the primary accuracy factor, while the physics-based constraint mechanism also exhibited consistent high-performance estimation under small-sample conditions. This dual-fusion paradigm of “central driver-peripheral response” and “data-physics” integration offers a clinically viable solution for high-precision and interpretable BP monitoring.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"73 8","pages":"2674-2681"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148301610","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}