Medical Engineering & Physics最新文献

{"title":"Heart rate estimation for U-Net and LSTM models combining multiple attention mechanisms","authors":"Ahui Li ,&nbsp;Jun Cai","doi":"10.1016/j.medengphy.2025.104406","DOIUrl":"10.1016/j.medengphy.2025.104406","url":null,"abstract":"<div><div>Accurate heart rate (HR) monitoring is pivotal in modern medical technology, particularly for disease prevention and health management. This study proposes a novel HR estimation framework that leverages advanced deep learning techniques to accurately extract HR information from noisy photoplethysmography (PPG) signals. The proposed model, termed DRL-Unet, integrates a Denoising Autoencoder (DAE), U-Net architecture, and Long Short-Term Memory (LSTM) networks. To further enhance robustness and precision, the model incorporates a Multi-Head Attention mechanism and Residual Network (ResNet) modules. Comparative experiments demonstrate that DRL-Unet outperforms conventional deep learning models, achieving a Mean Absolute Error (MAE) of 1.69 bpm, Mean Squared Error (MSE) of 3.05 bpm², Root Mean Squared Error (RMSE) of 1.71 bpm, Mean Absolute Percentage Error (MAPE) of 1.15%, and Bias of 0.05 bpm. The model's effectiveness is validated on a public dataset from the IEEE Signal Processing Cup, confirming its superior performance under complex noise conditions. These findings highlight the potential of DRL-Unet to significantly improve the accuracy and reliability of HR estimation, thereby offering valuable advancements for early cardiovascular disease diagnosis and continuous health monitoring.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"145 ","pages":"Article 104406"},"PeriodicalIF":2.3,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830645","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":"Technique for three-dimensional assessment of prosthesis alignment after radial head arthroplasty: A technical note","authors":"Ausberto R. Velasquez Garcia ,&nbsp;Adam J. Wentworth ,&nbsp;Jennifer M. Oettinger ,&nbsp;James S. Fitzsimmons ,&nbsp;Jeffrey F. Marsh Jr. ,&nbsp;Mark E. Morrey ,&nbsp;Shawn W. O'Driscoll","doi":"10.1016/j.medengphy.2025.104413","DOIUrl":"10.1016/j.medengphy.2025.104413","url":null,"abstract":"<div><h3>Objective</h3><div>To present a novel evaluation technique for assessing three-dimensional (3D) prosthesis alignment after radial head arthroplasty (RHA) and to identify potential measurement errors associated with this method.</div></div><div><h3>Materials/Methods</h3><div>Virtual surgical planning of a simulated irreparable fracture of the radial head was performed to select and place optimal implants. Of the six 3D-printed bone models, three were fitted with 3D-printed implants and three with metallic implants. After the procedure, 3D models were derived from 3D scans and dual-energy computed tomography with and without metal artifact reduction. Deviations in rotation and translation from the pre-procedure plan as well as measurement errors were assessed.</div></div><div><h3>Results</h3><div>The technique demonstrated the ability to accurately identify minor deviations in prosthesis alignment post-RHA. Deviations ranged from 0 to 14° in rotation and 0 to 1.3 mm in translation. The method also showed high measurement accuracy against 3D reference models, with mean rotational errors of 0.3–0.5° and translation errors of 0.1–0.3 mm.</div></div><div><h3>Conclusion</h3><div>This technique provides an accurate and precise method for assessing prosthesis alignment in RHA, with minimal measurement errors. Its potential as a valuable clinical tool has substantial implications in improving preoperative planning and postoperative evaluation. Further validation and advancements in reducing operator dependency are necessary for clinical adoption.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"146 ","pages":"Article 104413"},"PeriodicalIF":2.3,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099390","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 adaptive bin-stream network based on frequency decomposition for classifying atrial fibrillation with low SNR data","authors":"Jilin Wang ,&nbsp;Tengqun Shen ,&nbsp;Mengfan Li ,&nbsp;Yijun Ma ,&nbsp;Guozhen Sun ,&nbsp;Yatao Zhang","doi":"10.1016/j.medengphy.2025.104412","DOIUrl":"10.1016/j.medengphy.2025.104412","url":null,"abstract":"<div><div>To detect atrial fibrillation (AF) in ECG signals with low signal-to-noise ratio (SNR), this study introduces the adaptive bin-stream network (ABNet) based on frequency decomposition. The ABNet offers notable advantages: it exhibits high robustness in identifying AF amidst noisy environments, it decomposes the ECG signals into 32-frequency channel recordings to refine frequency ranges for better identifying AF, and it designs an adaptive bin-stream network to gain the optimal results. The method utilizes a 5-level Haar wavelet packet decomposition to decompose the preprocessed ECG signals into their corresponding 32-frequency channel recordings, and the preprocessing signals and the recordings are fed into waveform stream and frequency stream of the bin-stream network, respectively. Finally, an adaptive approach is employed to obtain the optimal classification results. The ABNet was validated for the PhysioNet/Computing in Cardiology Challenge 2017 database (CinC 2017 Db) to classify 4 categories i.e., normal sinus rhythm (N), AF, other abnormal rhythms (O) and noise (P), and it achieved accuracy (<em>acc</em>) 93.08 %, precision (<em>ppv</em>) 78.68 %, sensitivity (<em>sen</em>) 81.84 %, specificity (<em>spec</em>) 94.00 %, and <em>F</em>₁ 0.8382. In addition, it achieved the <em>acc</em> 97.98, <em>ppv</em> 96.40, <em>sen</em> 98.37 %, <em>spec</em> 98.41 %, and <em>F</em>₁ 0.9595 for a synthetic Db consisting of Shandong provincial hospital AF database (SPH AF Db) and CinC 2011 Db for classifying 3 categories i.e., N, AF and P. These results underscore the effectiveness of the ABNet in capturing detailed information about waveform and different frequencies in ECG signals.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"145 ","pages":"Article 104412"},"PeriodicalIF":2.3,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864385","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":"A user-defined element for simulating hydrogel injection into trabecular bone: Numerical simulations and experimental validation","authors":"Georgios F. Samaras ,&nbsp;Vincent Dischl ,&nbsp;Anita Fung ,&nbsp;Vincent A. Stadelmann ,&nbsp;Ulrike Kettenberger ,&nbsp;Stephen J. Ferguson ,&nbsp;Benedikt Helgason","doi":"10.1016/j.medengphy.2025.104411","DOIUrl":"10.1016/j.medengphy.2025.104411","url":null,"abstract":"<div><div>In this study, we present a comprehensive numerical model to simulate the injection of hydrogel into femurs. The model is designed to capture the complex interactions between the hydrogel rheological properties and the biomechanical environment of the femur. The coupled mechanical-flow formulation, based on the Theory of Porous Media, is implemented in an open source Abaqus UEL subroutine, where displacements, pressure and saturation are the unknowns. The rheological properties of the hydrogel were calibrated against experimental augmentations in three femurs and the calibrated model was then applied to three different femurs where the hydrogel patterns were compared to experimental data. Furthermore, the simulations demonstrated the effect of injection flow rate and heterogeneous permeability on the hydrogel patterns and quantified the trabecular matrix's solid strains developed during the injection process. The simulations captured well the volume distribution with an average dice coefficient of 0.75 for the three tested specimens. In addition, the calculated solid strains were below the tensile yield limit for the tested flow rate range. A description of the constitutive equations and the implementation into an Abaqus user element subroutine is provided. Overall, our modeling methodology provides a computational tool that can be used to more accurately model bone augmentation and furthermore plan more safely the treatment of osteoporotic patients.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"145 ","pages":"Article 104411"},"PeriodicalIF":2.3,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841540","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":"Fast geometric deep learning for intraoperative soft tissue deformation estimation: Towards real-time AR guidance in liver surgery","authors":"Zixuan Zhai ,&nbsp;Enpeng Wang ,&nbsp;Xiaojun Chen","doi":"10.1016/j.medengphy.2025.104409","DOIUrl":"10.1016/j.medengphy.2025.104409","url":null,"abstract":"<div><div>The real-time computation of the intraoperative spatial positioning of soft tissues, particularly those not visible within the body, such as blood vessels, is crucial for augmented reality navigation systems. Conventional biomechanical models face challenges in real-time computation and the acquisition of boundary conditions. A novel deep learning framework is proposed, integrating an optimized PointNet++ architecture for modelling liver and vascular deformation. The framework utilizes multi-scale feature extraction, lightweight self-attention mechanisms, and residual feature propagation to predict vascular displacement fields and normal vectors. A hybrid loss function that integrates Chamfer distance and MSE losses improves geometric consistency and deformation accuracy. The proposed approach, utilizing finite element method (FEM)-simulated datasets of liver stretching procedures, exhibits enhanced performance with root mean square errors (RMSE) of 2.78 ± 0.69 mm for hepatic veins and 1.81 ± 0.74 mm for portal veins. This method surpasses conventional techniques by 37.5% in accuracy and reduces inference time to 0.25 seconds. The optimized network exhibits a computation speed that is 83.9% faster than leading non-rigid registration algorithms. Subsequent tumour localization experiments demonstrate a targeting accuracy of 3.2 mm via vascular topology analysis, confirming clinical relevance. This research develops an effective framework for predicting deformation in real-time, providing a significant advancement for navigation in AR-guided hepatobiliary surgery.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"145 ","pages":"Article 104409"},"PeriodicalIF":2.3,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809762","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":"Comparative analysis of Electrospun PLA fibers incorporating bioactive glass nanoparticles: morphological, biological, and osteogenic properties for bone regeneration","authors":"Brunna da Silva Nobrega Souza ,&nbsp;Lilian de Siqueira ,&nbsp;Marina Santos Fernandes ,&nbsp;Joyce Rodrigues de Souza ,&nbsp;Elisa Camargo Kukulka ,&nbsp;Letícia Adrielly Dias Grisante ,&nbsp;Tiago Moreira Bastos Campos ,&nbsp;Luana Marotta Reis de Vasconcellos ,&nbsp;Alexandre Luiz Souto Borges","doi":"10.1016/j.medengphy.2025.104410","DOIUrl":"10.1016/j.medengphy.2025.104410","url":null,"abstract":"<div><div>Polylactic acid (PLA) is widely studied for bone repair due to its biodegradability, biocompatibility, and bioresorbability. However, its limited bioactivity and hydrophobic surface hinder optimal cell interaction and integration. Incorporating bioactive glass (BG) particles into PLA scaffolds via electrospinning and electrospray techniques has emerged as a promising strategy to improve biological performance. This study aimed to fabricate and characterize PLA scaffolds, both with incorporated and surface-coated BG, and to assess their osteogenic potential for tissue engineering applications. Scaffold morphology was evaluated by scanning electron microscopy, and biological performance was assessed through in vitro assays using mesenchymal stem cells derived from Wistar rat bone marrow. Cell viability, total protein content, alkaline phosphatase (ALP) activity, and mineralized nodule formation were analyzed. The scaffolds displayed porous, interconnected structures with fiber diameters influenced by BG incorporation method. All groups demonstrated cytocompatibility, while scaffolds containing BG both incorporated and sprayed—showed significantly higher ALP activity, suggesting enhanced osteogenic differentiation. Mineralization nodules further confirmed the induction of osteogenesis. These findings highlight the potential of PLA/BG composite scaffolds, especially when functionalized via combined electrospinning and electrospray methods, as a promising platform for bone tissue engineering.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"145 ","pages":"Article 104410"},"PeriodicalIF":2.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908034","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":"Reduced model aided fluid-structure interaction design framework for shunt systems","authors":"Elizabeth Hayman ,&nbsp;Van Dung Nguyen ,&nbsp;Ian S. McFarlane ,&nbsp;Juliette Pech ,&nbsp;Jayaratnam Jayamohan ,&nbsp;José-Maria Peña Sánchez ,&nbsp;Sarah Waters ,&nbsp;Antoine Jerusalem","doi":"10.1016/j.medengphy.2025.104403","DOIUrl":"10.1016/j.medengphy.2025.104403","url":null,"abstract":"<div><div>Traditionally, clinical devices are designed, tested and improved through lengthy and expensive laboratory experiments and clinical trials <span><span>[1]</span></span>. More recently, computational methods have allowed for rapid testing, speeding up the design process and enabling far more complete searches of design space. While computational models cannot fully capture the complexities of biological systems, they provide valuable insights into crucial underlying mechanisms, such as the effects of fluid-structure interactions (FSIs). In this paper we present a modular, partitioned, computational FSI pipeline whereby 2D reduced order models guide the 3D design of the problem of interest. This framework is applied to the problem of hydrocephalus shunt occlusion. Hydrocephalus is a medical condition characterised by an excess of cerebrospinal fluid (CSF) in the brain, and is commonly treated with the insertion of a shunt system. This system includes a ventricular catheter component – a hollow tube with inlet holes arranged in the tube wall close to the closed tip – which is positioned in the lateral ventricles of the brain. Despite recent improvements in the catheter material, this treatment still has high failure rates, most often due to the blockage of the catheter by the Choroid Plexus (ChP) tissue. We use an idealised FSI model to compare existing catheter designs by considering the deformation of the ChP under CSF flow in the ventricle environment in an hydrocephalus scenario. To the best of our knowledge, this is the first computational framework to directly incorporate the deformation of the ChP to discriminate between catheter designs. The faster 2D model is used in a comprehensive parameter sweep of the catheter design domain, and motivates a new design, then confirmed to be an improvement when tested in the full 3D domain. This approach demonstrates the success of using reduced order methods to guide the design of a more complex problem.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104403"},"PeriodicalIF":2.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772749","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":"A numerical investigation of the kinematic and fluid dynamic behaviour of an intramuscular autoinjector designed for optimising injection efficiency","authors":"Sudesh Sivarasu ,&nbsp;Ntokozo Magubane ,&nbsp;Chibuike Mbanefo ,&nbsp;Malebogo Ngoepe","doi":"10.1016/j.medengphy.2025.104407","DOIUrl":"10.1016/j.medengphy.2025.104407","url":null,"abstract":"<div><div>The usability and versatility of autoinjectors in managing chronic and autoimmune diseases have made them increasingly attractive in medicine. However, investigations into autoinjector designs require an understanding of the kinematic properties and fluid behaviour during injection. To optimise injection efficiency, this study develops a mathematical and computational fluid dynamics (CFD) model of an IM autoinjector by investigating the effects of viscosity, needle length, needle diameter, and medication volume on the injection process. The model was verified and validated using a comparator experiment and optimised using a parameter sensitivity analysis. The mathematical model results show plunger displacement increases linearly in low viscous fluids (<em>v</em> &lt; 20 cP), allowing faster injections. CFD simulations show that high-viscosity fluids (<em>v</em> &gt; 20 cP) reduce injectability and increase syringeability. Needle gauges below 20 exhibited constant dynamic pressure and negligible shear stress, while gauges between 20 and 25 showed higher shear stress and pressure variability. Longer needles and larger medication volumes increase dynamic pressure and shear stress, prolonging injection time. The mathematical and CFD models matched experimental measurements within a 1.1 % and 4.8 % margin of error, respectively. These findings inform the design of efficient autoinjectors, enhancing drug delivery, patient comfort, and compliance.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104407"},"PeriodicalIF":2.3,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739425","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":"Explainable deep learning framework for brain tumor detection: Integrating LIME, Grad-CAM, and SHAP for enhanced accuracy","authors":"Abdurrahim Akgündoğdu ,&nbsp;Şerife Çelikbaş","doi":"10.1016/j.medengphy.2025.104405","DOIUrl":"10.1016/j.medengphy.2025.104405","url":null,"abstract":"<div><div>Deep learning approaches have improved disease diagnosis efficiency. However, AI-based decision systems lack sufficient transparency and interpretability. This study aims to enhance the explainability and training performance of deep learning models using explainable artificial intelligence (XAI) techniques for brain tumor detection. A two-stage training approach and XAI methods were implemented. The proposed convolutional neural network achieved 97.20% accuracy, 98.00% sensitivity, 96.40% specificity, and 98.90% ROC-AUC on the BRATS2019 dataset. It was analyzed with explainability techniques including Local Interpretable Model-Agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (Grad-CAM), and Shapley Additive Explanations (SHAP). The masks generated from these analyses enhanced the dataset, leading to a higher accuracy of 99.40%, 99.20% sensitivity, 99.60% specificity, 99.60% precision, and 99.90% ROC-AUC in the final stage. The integration of LIME, Grad-CAM, and SHAP showed significant success by increasing the accuracy performance of the model from 97.20% to 99.40%. Furthermore, the model was evaluated for fidelity, stability, and consistency and showed reliable and stable results. The same strategy was applied to the BR35H dataset to test the generalizability of the model, and the accuracy increased from 96.80% to 99.80% on this dataset as well, supporting the effectiveness of the method on different data sources.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104405"},"PeriodicalIF":2.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723271","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":"A machine learning approach to concussive group classification using discrete outcome measures from a low-cost movement-based assessment system","authors":"Jacob M. Thomas ,&nbsp;Jamie B. Hall ,&nbsp;Rebecca Bliss ,&nbsp;Emily Leary ,&nbsp;Stephen P. Sayers ,&nbsp;Praveen Rao ,&nbsp;Trent M. Guess","doi":"10.1016/j.medengphy.2025.104402","DOIUrl":"10.1016/j.medengphy.2025.104402","url":null,"abstract":"<div><div>Measurable neuromotor control deficits during functional task performance could provide objective criteria to aid in concussion diagnosis. However, many tools which measure these constructs are unidimensional and not clinically feasible. The purpose of this study was to assess the classification accuracy of a machine learning model using features measured by a clinically feasible movement-based assessment system (Mizzou Point-of-care Assessment System (MPASS) between athletes with and without concussion. Forty collegiate athletes participated. Twenty (19.40 ± 1.04 yrs., 11 females) suffered concussion within two weeks of data collection (5.40 ± 3.68 days). Twenty (19.85 ± 1.20 yrs.) sex, sport, and position-matched athletes had no concussions in the past year. All participants completed three 30-second static balance trials with eyes closed on foam surface under both single task and cognitive dual task conditions, four trials of gait under normal, head shaking, and dual task conditions, and reaction time tasks. Kinematics, kinetics, and reaction times were recorded by MPASS. Measures were used as features for a XGBoost machine learning model. Five-fold cross-validation yielded mean (across 5-folds): 82.5 % accuracy, 75 % sensitivity, 90 % specificity, 88.2 % positive predictive value, and 78.3 % negative predictive value. Results indicate promise for using movement-based features from a low-cost movement-based assessment system to improve the objectivity of concussion diagnosis decision-making.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104402"},"PeriodicalIF":2.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772750","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}