Computers in biology and medicine最新文献

{"title":"The impact of different material types on ergonomics in lower extremity exoskeleton construction","authors":"İsmail Çalıkuşu ,&nbsp;Ugur Fidan","doi":"10.1016/j.compbiomed.2025.110403","DOIUrl":"10.1016/j.compbiomed.2025.110403","url":null,"abstract":"<div><div>This study examines the effects of materials such as A Glass Fiber, Aluminum Alloy, Stainless Steel, S Glass Fiber, C Graphite, Hexcel, and Thornel on biomechanical performance in the design of lower extremity exoskeletons. Exoskeleton models created using Computer-Aided Modeling software were integrated into the AnyBody Modeling System and combined with a full-body human model to conduct walking simulations. In these simulations, femur and tibia segments were also incorporated into the model to analyze the impacts of the exoskeleton on human movement dynamics in detail. The results reveal that material selection significantly influences joint reaction forces and moments, ground reaction forces, and contact forces. Flexible materials were found to provide greater comfort to the user but demonstrated lower durability performance. Conversely, more durable materials improved overall efficiency by reducing load transfer. These findings emphasize that material selection in exoskeleton design plays a critical role not only in durability and performance but also in meeting ergonomic requirements. This research offers a valuable foundation for developing exoskeleton designs tailored to different user groups and highlights the need to customize material selection to optimize biomechanical performance. The study serves as a guide for enhancing the compatibility of exoskeletons with human movement dynamics and improving user comfort.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110403"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116247","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":"CancerNet: A comprehensive deep learning framework for precise and intelligible cancer identification","authors":"S.M. Nuruzzaman Nobel ,&nbsp;Shirin Sultana ,&nbsp;Md All Moon Tasir ,&nbsp;M.F. Mridha ,&nbsp;Zeyar Aung","doi":"10.1016/j.compbiomed.2025.110339","DOIUrl":"10.1016/j.compbiomed.2025.110339","url":null,"abstract":"<div><div>The medical community continually seeks innovative solutions to address healthcare challenges, particularly in cancer detection. A promising approach involves the use of Artificial Intelligence (AI) techniques, specifically Deep Learning (DL) models. This research introduces CancerNet, incorporating convolutional, involutional, and transformer components to extract hierarchical features and capture long-range dependencies from medical imaging data across the channel and spatial domains. CancerNet was trained and evaluated on an extensive dataset of histopathological images (HI) of tumor tissues and validated on the DeepHisto dataset, which comprises whole slide images (WSI) of various subtypes of glioma. CancerNet surpasses other comparative models and, achieves a higher accuracy on both datasets. CancerNet exhibits robustness across various imaging conditions, thereby ensuring reliable performance in various clinical scenarios. By integrating Explainable AI (XAI) techniques, CancerNet enhances transparency in its decision-making process, improves understanding and fosters trust in clinical adoption. CancerNet achieved an accuracy of 98.77% on the Histopathological Image dataset and 97.83% on the DeepHisto validation dataset, proving to be more effective than previous. Furthermore, transparency in AI models is crucial as it enhances healthcare professionals ability to understand and trust the model’s decision-making process, facilitating their adoption in clinical settings.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110339"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A rule-based clinical decision support system for detection of acute kidney injury after pediatric cardiac surgery","authors":"Janice Wachenbrunner ,&nbsp;Marcel Mast ,&nbsp;Julia Böhnke ,&nbsp;Nicole Rübsamen ,&nbsp;Louisa Bode ,&nbsp;André Karch ,&nbsp;Henning Rathert ,&nbsp;Alexander Horke ,&nbsp;Philipp Beerbaum ,&nbsp;Michael Marschollek ,&nbsp;Thomas Jack ,&nbsp;Martin Böhne","doi":"10.1016/j.compbiomed.2025.110382","DOIUrl":"10.1016/j.compbiomed.2025.110382","url":null,"abstract":"<div><h3>Background</h3><div>Acute kidney injury (AKI) is common in children with congenital heart disease following open-heart surgery with cardiopulmonary bypass (CPB). Early AKI detection in critically ill children requires clinician expertise to compile various data from different sources within a stressful and time-sensitive environment. However, as electronic health records provide data in a machine-readable format, this process could be supported by computerized systems. Therefore, we developed a time-aware, rule-based clinical decision support system (CDSS) to detect, stage, and track temporal AKI progression in children.</div></div><div><h3>Methods</h3><div>We integrated retrospective clinical routine data from n = 290 randomly selected cases (n = 263 patients, aged 0–17 years) who underwent cardiac surgery with CPB into a dataset. We adapted <em>Kidney Disease: Improving Global Outcome</em> (KDIGO) criteria, including serum creatinine, urine output, and estimated glomerular filtration rate, and translated them into computable rules for the CDSS. As a reference standard, patients were manually assessed by blinded clinical experts.</div></div><div><h3>Results</h3><div>The AKI incidence, according to the reference standard, was n = 146 cases for stage 1, n = 58 for stage 2, and n = 20 for stage 3. The CDSS achieved sensitivities of 92.2 % (95 % CI: 86.8–95.5 %) for AKI stage 1, 88.1 % (95 % CI: 77.2–94.2 %) for stage 2, and 95 % (95 % CI: 70.5–99.3 %) for stage 3. The specificities were 97.0 % (95 % CI: 94.4–98.4 %), 98.5 % (95 % CI: 96.5–99.4 %), and 99.3 % (95 % CI: 97.3–99.8 %), respectively.</div></div><div><h3>Conclusions</h3><div>We demonstrated that a CDSS is able to perform a complex AKI detection and staging process, including 11 criteria across three stages. For accurate automated AKI detection, standardized machine-readable data of high data quality are required. CDSS with high diagnostic accuracy, like presented, can support clinical management and be used for surveillance and quality management. The prototypical use for surveillance and further studies, such as the development of prediction models, should demonstrate the system's benefits in the future.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110382"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107111","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":"Transformation trees — Documentation of multimodal image registration","authors":"Agnieszka Anna Tomaka,&nbsp;Dariusz Pojda,&nbsp;Michał Tarnawski,&nbsp;Leszek Luchowski","doi":"10.1016/j.compbiomed.2025.110311","DOIUrl":"10.1016/j.compbiomed.2025.110311","url":null,"abstract":"<div><div>Multimodal image registration plays a key role in creating digital patient models by combining data from different imaging techniques into a single coordinate system. This process often involves multiple sequential and interconnected transformations, which must be well-documented to ensure transparency and reproducibility. In this paper, we propose the use of transformation trees as a method for structured recording and management of these transformations. This approach has been implemented in the dpVision software and uses a dedicated .dpw file format to store hierarchical relationships between images, transformations, and motion data. Transformation trees allow precise tracking of all image processing steps, reduce the need to store multiple copies of the same data, and enable the indirect registration of images that do not share common reference points. This improves the reproducibility of the analyses and facilitates later processing and integration of images from different sources. The practical application of this method is demonstrated with examples from orthodontics, including the integration of 3D face scans, intraoral scans, and CBCT images, as well as the documentation of mandibular motion. Beyond orthodontics, this method can be applied in other fields that require systematic management of image registration processes, such as maxillofacial surgery, oncology, and biomechanical analysis. Maintaining long-term data consistency is essential for both scientific research and clinical practice. It enables easier comparison of results in longitudinal studies, improves retrospective analysis, and supports the development of artificial intelligence algorithms by providing standardized and well-documented datasets. The proposed approach enhances data organization, allows for efficient analysis, and facilitates the reuse of information in future studies and diagnostic procedures.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110311"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107112","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":"Mutational and expression analysis of classical protein tyrosine phosphatase genes in pancreatic ductal adenocarcinoma","authors":"Maryam Naeem,&nbsp;Khursheed Ahmed,&nbsp;Aneesa Sultan","doi":"10.1016/j.compbiomed.2025.110319","DOIUrl":"10.1016/j.compbiomed.2025.110319","url":null,"abstract":"<div><h3>Background</h3><div>Pancreatic cancer is a highly lethal and aggressive malignancy with a minimal five-year survival rate (5 %) and a high mortality rate. The most common and fast-growing type of pancreatic cancer is PDAC, which constitutes 90 % of all cases.</div></div><div><h3>Objective</h3><div>Numerous signaling pathways are disrupted in PDAC. We explored the mutational status and expression profiling of classical protein tyrosine phosphatase (PTP) genes, which are vital regulators of multiple significant signaling pathways.</div></div><div><h3>Method</h3><div>Through whole exome sequencing, we identified potentially pathogenic non-synonymous variants that were subsequently analyzed <em>in-silico</em>. To validate these findings, quantitative real-time PCR was performed on blood samples from PDAC patients to assess the expression of deleterious genes.</div></div><div><h3>Results</h3><div>All the potential pathogenic variants were localized within the phosphatase domain 1, fibronectin type III domain, and the FERM domain of classical PTPs regions crucial for the proper functioning of the respective proteins. Among the analyzed genes, PTPN3, PTPN12, PTPRK, and PTPRZ1 were found statistically significant (<em>p</em> &lt; 0.05), highlighting their potential as novel prognostic biomarkers and therapeutic targets for PDAC.</div></div><div><h3>Conclusion</h3><div>These findings hold particular relevance for the Pakistani population, offering valuable insights into the genetic landscape of this aggressive cancer.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110319"},"PeriodicalIF":7.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation","authors":"Seokhwan Ko ,&nbsp;Yu Ando ,&nbsp;Moonsik Kim ,&nbsp;Nora Jee-Young Park ,&nbsp;Hyungsoo Han ,&nbsp;Ji Young Park ,&nbsp;Junghwan Cho","doi":"10.1016/j.compbiomed.2025.110353","DOIUrl":"10.1016/j.compbiomed.2025.110353","url":null,"abstract":"<div><h3>Background:</h3><div>Histopathological diagnosis involves examining abnormal architectural patterns and cellular-level changes. Whole slide images (WSIs) provide comprehensive digital representations of tissue samples, enabling detailed analysis and interpretation. Annotating the giga-pixel images remains labor-intensive, requiring experts to label abnormal patterns and cellular changes. To address this, Multiple Instance Learning (MIL), a promising weakly supervised approach, enables models to learn from limited annotations while preserving key histopathological features.</div></div><div><h3>Method:</h3><div>However, existing MIL-based methods may overlook potential semantic features, limiting their effectiveness. To overcome this limitation, we propose a novel Cluster-Aware Attention-based MIL (CAAMIL) architecture. This approach employs an advanced attention-based module integrated with a clustering method to enhance the interpretability of heterogeneous features. Our approach clusters architectural or cytologic features, making the groups interpretable at the cluster level and reflective of histopathological grades or prognostic indicators.</div></div><div><h3>Results:</h3><div>We demonstrated the efficacy of our model in both slide-level and patch-level classification as well as in interpreting tumor and mutation predictions. Experimental results show that our model achieves an AUC score of 0.96 for tumor detection at slide-level and 0.85 at patch-level, outperforming other state-of-the-art MIL-based methods.</div></div><div><h3>Conclusion:</h3><div>Our proposed CAAMIL architecture overcomes the limitations of existing MIL methods by effectively clustering features and providing interpretable results. The high accuracy and interpretability of our model make it a promising tool for histopathological diagnosis and tumor detection.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110353"},"PeriodicalIF":7.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106716","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":"Agitation-sedation models for critical care: Insights into endogenous agitation reduction and stimulus","authors":"Ryan O’Sullivan,&nbsp;Isaac Flett,&nbsp;Chris Pretty,&nbsp;J. Geoffrey Chase","doi":"10.1016/j.compbiomed.2025.110323","DOIUrl":"10.1016/j.compbiomed.2025.110323","url":null,"abstract":"<div><h3>Background:</h3><div>Sedation and agitation management are core treatments in the intensive care unit. This study uses pharmacokinetic–pharmacodynamic (PKPD) models to capture the endogenous agitation response. The identification and validation of these models allow for a better understanding of agitation-sedation dynamics and improves the clinical implementation.</div></div><div><h3>Methods:</h3><div>A cohort of healthy volunteers (N=25) was exposed to a controlled psychological stimulus, with agitation levels quantitatively measured using heart rate-derived metrics. Endogenous agitation reduction (EAR) coefficients were determined from the post-stimulus decay. Using these parameters and a priori information about the experienced stimulus, the model was validated against the measured agitation data.</div></div><div><h3>Results:</h3><div>The model demonstrated a good fit between measured and modelled agitation. EAR parameters were identified with 45% of the cohort ranging between 0.003–0.004 <span><math><msup><mrow><mi>s</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span>. Using a population value for EAR still resulted in a good fit to measured data. Minimal differences were observed between female and male participants.</div></div><div><h3>Conclusion:</h3><div>This study provides further development of PKPD models of agitation-sedation dynamics. The identified EAR parameter can be used in future studies and in the clinical application of these models.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110323"},"PeriodicalIF":7.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107110","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":"Automatic transformer-based grading of multiple retinal inflammatory signs in uveitis on fluorescein angiography","authors":"Victor Amiot ,&nbsp;Oscar Jimenez–del–Toro ,&nbsp;Yan Guex–Crosier ,&nbsp;Muriel Ott ,&nbsp;Teodora-Elena Bogaciu ,&nbsp;Shalini Banerjee ,&nbsp;Jeremy Howell ,&nbsp;Christoph Amstutz ,&nbsp;Christophe Chiquet ,&nbsp;Ciara Bergin ,&nbsp;Ilenia Meloni ,&nbsp;Mattia Tomasoni ,&nbsp;Florence Hoogewoud ,&nbsp;André Anjos","doi":"10.1016/j.compbiomed.2025.110327","DOIUrl":"10.1016/j.compbiomed.2025.110327","url":null,"abstract":"<div><h3>Background</h3><div>Grading fluorescein angiography (FA) for uveitis is complex, often leading to the oversight of retinal inflammation in clinical studies. This study aims to develop an automated method for grading retinal inflammation.</div></div><div><h3>Methods</h3><div>Patients from Jules-Gonin Eye Hospital with active or resolved uveitis who underwent FA between 2018 and 2021 were included. FAs were acquired using a standardized protocol, anonymized, and annotated following the Angiography Scoring for Uveitis Working Group criteria, for four inflammatory signs of the posterior pole. Intergrader agreement was assessed by four independent graders. Four deep learning transformer models were developed, and performance was evaluated using the Ordinal Classification Index, accuracy, F1 scores, and Kappa scores. Saliency analysis was employed to visualize model predictions.</div></div><div><h3>Findings</h3><div>A total of 543 patients (1042 eyes, 40987 images) were included in the study. The models closely matched expert graders in detecting vascular leakage (F1-score = 0·87, 1-OCI = 0·89), capillary leakage (F1-score = 0·86, 1-OCI = 0·89), macular edema (F1-score = 0·82, 1-OCI = 0·86), and optic disc hyperfluorescence (F1-score = 0·72, 1-OCI = 0·85). Saliency analysis confirmed that the models focused on relevant retinal structures. The mean intergrader agreement across all inflammatory signs was F1-score = 0·79 and 1-OCI = 0·83.</div></div><div><h3>Interpretation</h3><div>We developed a vision transformer-based model for the automatic grading of retinal inflammation in uveitis, utilizing the largest dataset of FAs in uveitis to date. This approach provides significant clinical benefits for the evaluation of uveitis and paves the way for future advancements, including the identification of novel biomarkers through the integration of clinical data and other modalities.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110327"},"PeriodicalIF":7.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106739","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":"Large medical image database impact on generalizability of synthetic CT scan generation","authors":"Claudia Boily ,&nbsp;Jean-Paul Mazellier ,&nbsp;Philippe Meyer","doi":"10.1016/j.compbiomed.2025.110303","DOIUrl":"10.1016/j.compbiomed.2025.110303","url":null,"abstract":"<div><div>This study systematically examines the impact of training database size and the generalizability of deep learning models for synthetic medical image generation. Specifically, we employ a Cycle-Consistency Generative Adversarial Network (CycleGAN) with softly paired data to synthesize kilovoltage computed tomography (kVCT) images from megavoltage computed tomography (MVCT) scans. Unlike previous works, which were constrained by limited data availability, our study uses an extensive database comprising 4,000 patient CT scans, an order of magnitude larger than prior research, allowing for a more rigorous assessment of database size in medical image translation.</div><div>We quantitatively evaluate the fidelity of the generated synthetic images using established image similarity metrics, including Mean Absolute Error (MAE) and Structural Similarity Index Measure (SSIM). Beyond assessing image quality, we investigate the model’s capacity for generalization by analyzing its performance across diverse patient subgroups, considering factors such as sex, age, and anatomical region. This approach enables a more granular understanding of how dataset composition influences model robustness.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110303"},"PeriodicalIF":7.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106717","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":"The role of mathematical models in prediction of osteoarthritis development","authors":"Marek Pawlikowski,&nbsp;Szymon Sikora,&nbsp;Gustaw Ostrowski","doi":"10.1016/j.compbiomed.2025.110407","DOIUrl":"10.1016/j.compbiomed.2025.110407","url":null,"abstract":"<div><div>In the paper we presented the review of mathematical and numerical models of osteoarthritis (OA). As angiogenesis seems to be the most principal factor in OA mathematical and numerical modelling, we focused on the models that consider the process. The spectrum of the presented models is wide. They were divided in the scale of the simulated phenomena, i.e., micro- or macro-scale. A part of them considers only damage of tissue without paying attention to its remodeling. Others consider loss of tissue, new tissue formulation and remodeling of bone, both in micro- and macro-scale. What is worth mentioning is that most of the models were confirmed by comparing results to data available in literature. Only a few of them were experimentally validated. As the conclusion, we stated that the most accurate models are those that take into consideration mechanical stimulation, biological signaling and their nonlocal effects. Also, an important feature of an OA model is the ability to adapt it to various cases to be able to simulate OA in any joint.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110407"},"PeriodicalIF":7.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106738","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}