Journal of Biomedical Informatics最新文献

{"title":"From image to report: automating lung cancer screening interpretation and reporting with vision-language models.","authors":"Tien-Yu Chang, Qinglin Gou, Leyi Zhao, Tiancheng Zhou, Hongyu Chen, Dong Yang, Huiwen Ju, Kaleb E Smith, Chengkun Sun, Jinqian Pan, Yu Huang, Xing He, Xuhong Zhang, Daguang Xu, Jie Xu, Jiang Bian, Aokun Chen","doi":"10.1016/j.jbi.2025.104931","DOIUrl":"https://doi.org/10.1016/j.jbi.2025.104931","url":null,"abstract":"<p><strong>Objective: </strong>Lung cancer is the most prevalent cancer and the leading cause of cancer-related death in the United States. Lung cancer screening with low-dose computed tomography (LDCT) helps identify lung cancer at an early stage and thus improves overall survival. The growing adoption of LDCT screening has increased radiologists' workload and demands specialized training to accurately interpret LDCT images and report findings. Advances in artificial intelligence (AI), including large language models (LLMs) and vision models, could help reduce this burden and improve accuracy.</p><p><strong>Methods: </strong>We devised LUMEN (Lung cancer screening with Unified Multimodal Evaluation and Navigation), a multimodal AI framework that mimics the radiologist's workflow by identifying nodules in LDCT images, generating their characteristics, and drafting corresponding radiology reports in accordance with reporting guidelines. LUMEN integrates computer vision, vision-language models (VLMs), and LLMs. To assess our system, we developed a benchmarking framework to evaluate the lung cancer screening reports generated based on the findings and management criteria outlined in the Lung Imaging Reporting and Data System (Lung-RADS). It extracts them from radiology reports and measures clinical accuracy-focusing on information that is clinically important for lung cancer screening-independently of report format.</p><p><strong>Results: </strong>This complement exists LLM/VLM in semantic accuracy metrics and provides a more comprehensive view of system performance. Our lung cancer screening report generation system achieved unparalleled performance compared to contemporary VLM systems, including M3D, CT2Report and MedM3DVLM. Furthermore, compared to standard LLM metrics, the clinical metrics we designed for lung cancer screening more accurately reflect the clinical utility of the generated reports.</p><p><strong>Conclusion: </strong>LUMEN demonstrates the feasibility of generating clinically accurate lung nodule reports from LDCT images through a nodule-centric VQA approach, highlighting the potential of integrating VLMs and LLMs to support radiologists in lung cancer screening workflows. Our findings also underscore the importance of applying clinically meaningful evaluation metrics in developing medical AI systems.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104931"},"PeriodicalIF":4.5,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286192","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":"MF-DTA: Predicting drug–target affinity with multi-modal feature fusion model","authors":"Yanlei Kang ,&nbsp;Haoyu Zhuang ,&nbsp;Yunliang Jiang ,&nbsp;Zhong Li","doi":"10.1016/j.jbi.2025.104926","DOIUrl":"10.1016/j.jbi.2025.104926","url":null,"abstract":"<div><div>The prediction of drug–target interactions (DTIs) and binding affinities (DTAs) plays a pivotal role in drug discovery and design. However, most existing methods fail to fully exploit the rich multimodal information inherent in molecular structures. In this study, we propose a multimodal feature fusion model, MF-DTA. On the representational level, MF-DTA introduces the molecular fragment graph, generated via BRICS-based decomposition, as a novel modality. This representation enables a more intuitive capture of the structural characteristics and pharmacophore-related information of drug molecules. In terms of model architecture, a deformable convolutional layer is applied for the protein residue–residue contact map (hereafter referred to as contact map) to flexibly adjust the distribution of sampling points and enhance the representational capability. To effectively integrate the multimodal information from both drug and target branches, a mixture-of-experts (MoE)-based multihead attention mechanism is employed for local fusion, while a dual-decoder architecture facilitates cross-modal interaction between drug and target features. The final output yields a high-quality prediction of binding affinity. Cross-validation experiments conducted on several benchmark datasets demonstrate that MF-DTA consistently outperforms state-of-the-art methods. Specifically, it achieves CI improvements of 0.1%, 0.5%, and 0.3% over the best-performing baseline models in the Davis, KIBA and BindingDB datasets, respectively, and exceeds traditional models by 1% to 2% on average. The model also ranks among the best performers in terms of the MSE and R_m <span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> metrics. Model visualization further supports its interpretability, confirming that it successfully learns meaningful drug–target interaction patterns.To further assess the practical utility of the proposed model, we apply it to screen potential candidate compounds from a natural product library targeting tubulin. In summary, MF-DTA offers not only accurate and robust binding affinity prediction capabilities but also strong interpretability, making it a powerful and practical tool for drug design and target identification.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104926"},"PeriodicalIF":4.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145280336","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":"Multi-feature machine learning for enhanced drug–drug interaction prediction","authors":"Qiuyang Feng ,&nbsp;Xiao Huang","doi":"10.1016/j.jbi.2025.104923","DOIUrl":"10.1016/j.jbi.2025.104923","url":null,"abstract":"<div><div>Drug–drug interactions are a major concern in healthcare, as concurrent drug use can cause severe adverse effects. Existing machine learning methods often neglect data imbalance and DDI directionality, limiting clinical reliability. To overcome these issues, we employed GPT-4o Large Language Model to convert free-text DDI descriptions into structured triplets for directionality analysis and applied SMOTE to alleviate class imbalance. Using four key drug features (molecular fingerprints, enzymes, pathways, targets), our Deep Neural Networks (DNN) achieved 88.9% accuracy and showed an average AUPR gain of 0.68 for minority classes attributable to SMOTE. By applying attention-based feature importance analysis, we demonstrated that the most influential feature in the DNN model was supported by pharmacological evidence. These results demonstrate the effectiveness of our framework for accurate and robust DDI prediction. The source code and data are available at <span><span>https://github.com/FrankFengF/Drug-drug-interaction-prediction-</span><svg><path></path></svg></span></div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104923"},"PeriodicalIF":4.5,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145258203","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 REDCap advanced randomization module to meet the needs of modern trials","authors":"Luke Stevens ,&nbsp;Nan Kennedy ,&nbsp;Rob J. Taylor ,&nbsp;Adam Lewis ,&nbsp;Frank E. Harrell Jr ,&nbsp;Matthew S. Shotwell ,&nbsp;Emily S. Serdoz ,&nbsp;Gordon R. Bernard ,&nbsp;Wesley H. Self ,&nbsp;Christopher J. Lindsell ,&nbsp;Paul A. Harris ,&nbsp;Jonathan D. Casey","doi":"10.1016/j.jbi.2025.104925","DOIUrl":"10.1016/j.jbi.2025.104925","url":null,"abstract":"<div><h3>Objective</h3><div>Since 2012, the electronic data capture platform REDCap has included an embedded randomization module allowing a single randomization per study record with the ability to stratify by variables such as study site and participant sex at birth. In recent years, platform, adaptive, decentralized, and pragmatic trials have gained popularity. These trial designs often require approaches to randomization not supported by the original REDCap randomization module, including randomizing patients into multiple domains or at multiple points in time, changing allocation tables to add or drop study groups, or adaptively changing allocation ratios based on data from previously enrolled participants. Our team aimed to develop new randomization functions to address these issues.</div></div><div><h3>Methods</h3><div>A collaborative process facilitated by the NIH-funded Trial Innovation Network was initiated to modernize the randomization module in REDCap, incorporating feedback from clinical trialists, biostatisticians, technologists, and other experts.</div></div><div><h3>Results</h3><div>This effort led to the development of an advanced randomization module within the REDCap platform. In addition to supporting platform, adaptive, decentralized, and pragmatic trials, the new module introduces several new features, such as improved support for blinded randomization, additional randomization metadata capture (e.g., user identity and timestamp), additional tools allowing REDCap administrators to support investigators using the randomization module, and the ability for clinicians participating in pragmatic or decentralized trials to perform randomization through a survey without needing log-in access to the study database. As of June 19, 2025, multiple randomizations have been used in 211 projects from 55 institutions, randomizations with real-time trigger logic in 108 projects from 64 institutions, and blinded group allocation in 24 projects from 17 institutions.</div></div><div><h3>Conclusion</h3><div>The new randomization module aims to streamline the randomization process, improve trial efficiency, and ensure robust data integrity, thereby supporting the conduct of more sophisticated and adaptive clinical trials.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104925"},"PeriodicalIF":4.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238683","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":"Accelerating probabilistic privacy-preserving medical record linkage: A three-party MPC approach","authors":"Şeyma Selcan Mağara,&nbsp;Noah Dietrich,&nbsp;Ali Burak Ünal,&nbsp;Mete Akgün","doi":"10.1016/j.jbi.2025.104920","DOIUrl":"10.1016/j.jbi.2025.104920","url":null,"abstract":"<div><h3>Objective:</h3><div>Record linkage is essential for integrating data from multiple sources with diverse applications in real-world healthcare and research. Probabilistic Privacy-Preserving Record Linkage (PPRL) enables this integration occurs, while protecting sensitive information from unauthorized access, especially when datasets lack exact identifiers. As privacy regulations evolve and multi-institutional collaborations expand globally, there is a growing demand for methods that effectively balance security, accuracy, and efficiency. However, ensuring both privacy and scalability in large-scale record linkage remains a key challenge.</div></div><div><h3>Method:</h3><div>This paper presents a novel and efficient PPRL method based on a secure 3-party computation (MPC) framework. Our approach allows multiple parties to compute linkage results without exposing their private inputs and significantly improves the speed of linkage process compared to existing PPRL solutions.</div></div><div><h3>Result:</h3><div>Our method preserves the linkage quality of a state-of-the-art (SOTA) MPC-based PPRL method while achieving up to 14 times faster performance. For example, linking a record against a database of 10,000 records takes just 8.74 s in a realistic network with 700 Mbps bandwidth and 60 ms latency, compared to 92.32 s with the SOTA method. Even on a slower internet connection with 100 Mbps bandwidth and 60 ms latency, the linkage completes in 28 s, where as the SOTA method requires 287.96 s. These results demonstrate the significant scalability and efficiency improvements of our approach.</div></div><div><h3>Conclusion:</h3><div>Our novel PPRL method, based on secure 3-party computation, offers an efficient and scalable solution for large-scale record linkage while ensuring privacy protection. The approach demonstrates significant performance improvements, making it a promising tool for secure data integration in privacy-sensitive sectors.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104920"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223419","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":"GraphFusion: Integrative prediction of drug synergy using multi-scale graph representations and cell line contexts","authors":"Biyang Zeng,&nbsp;Shikui Tu,&nbsp;Lei Xu","doi":"10.1016/j.jbi.2025.104921","DOIUrl":"10.1016/j.jbi.2025.104921","url":null,"abstract":"<div><div>Predicting the synergy of drug combinations is crucial for cancer treatment and drug development. Accurate prediction requires the integration of multiple types of data, including molecular structures of individual drugs, available synergy scores between drugs, and gene expression information from different cancer cell lines. The first two types contain multi-scale information within or between drugs, while the cell lines serve as the contextual background for drug interactions. Existing machine learning methods fail to fully utilize and integrate these information, leading to suboptimal performance. To address this issue, we introduce GraphFusion, an innovative approach that combines molecular graphs and drug synergy graphs with cell line contextual information. By employing novel GCN and Graphormer modules capable of accepting and utilizing external information, GraphFusion integrates these two levels of graph information. Specifically, the molecular graphs pass fine-grained structural information to the synergy graphs, while the synergy graphs convey global drug interaction data to the molecular graphs. Additionally, cell line information is incorporated as contextual background. This comprehensive integration enables GraphFusion to achieve state-of-the-art results on the O’Neil and NCI-ALMANAC datasets.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104921"},"PeriodicalIF":4.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145212771","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":"Definitions to data flow: Operationalizing MIABIS in HL7 FHIR","authors":"Radovan Tomášik ,&nbsp;Šimon Koňár ,&nbsp;Niina Eklund ,&nbsp;Cäcilia Engels ,&nbsp;Zdenka Dudova ,&nbsp;Radoslava Kacová ,&nbsp;Roman Hrstka ,&nbsp;Petr Holub","doi":"10.1016/j.jbi.2025.104919","DOIUrl":"10.1016/j.jbi.2025.104919","url":null,"abstract":"<div><h3>Objective</h3><div>Biobanks and biomolecular resources are increasingly central to data-driven biomedical research, encompassing not only metadata but also granular, sample-related data from diverse sources such as healthcare systems, national registries, and research outputs. However, the lack of a standardised, machine-readable format for representing such data limits interoperability, data reuse and integration into clinical and research environments. While MIABIS provides a conceptual model for biobank data, its abstract nature and reliance on heterogeneous implementations create barriers to practical, scalable adoption. This study presents a pragmatic, operational implementation of MIABIS focused on enabling real-world exchange and integration of sample-level data.</div></div><div><h3>Methods</h3><div>We systematically evaluated established data exchange standards, comparing HL7 FHIR and OMOP CDM with respect to their suitability for structuring sample-related data in a semantically robust and machine-readable form. Based on this analysis, we developed a FHIR-based representation of MIABIS that supports complex biobank structures and enables integration with federated data infrastructures. Supporting tools, including a Python library and an implementation guide, were created to ensure usability across diverse research and clinical contexts.</div></div><div><h3>Results</h3><div>We <em>created nine interoperable FHIR profiles</em> covering core MIABIS entities, ensuring consistency with FHIR standards. To support adoption, we <em>developed an open-source Python library</em> that abstracts FHIR interactions and provides schema validation for MIABIS-compliant data. The <em>library was integrated into an ETL tool</em> in operation at Czech Node of BBMRI-ERIC, European Biobanking and Biomolecular Resources Research Infrastructure, to demonstrate usability with real-world sample-related data. Separately, we validated the representation of MIABIS entities at the organisational level by converting the data structures of BBMRI-ERIC Directory into FHIR, demonstrating compatibility with federated data infrastructures.</div></div><div><h3>Conclusion</h3><div>This work delivers a machine-readable, interoperable implementation of MIABIS, enabling the exchange of both organisational and sample-level data across biobanks and health information systems. By integrating MIABIS with HL7 FHIR, we provide a host of reusable tools and mechanisms for further evolution of the data model. Combined, these benefits can help with the integration into clinical and research workflows, supporting data discoverability, reuse, and cross-institutional collaboration in biomedical research.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104919"},"PeriodicalIF":4.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191636","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":"Review of tools to support Target Trial Emulation","authors":"Christina A. van Hal ,&nbsp;Elmer V. Bernstam ,&nbsp;Todd R. Johnson","doi":"10.1016/j.jbi.2025.104897","DOIUrl":"10.1016/j.jbi.2025.104897","url":null,"abstract":"<div><h3>Objective:</h3><div>Randomized Controlled Trials (RCTs) are the gold standard for clinical evidence, but ethical and practical constraints sometimes necessitate or warrant the use of observational data. The aim of this study is to identify informatics tools that support the design and conduct of Target Trial Emulations (TTEs), a framework for designing observational studies that closely emulate RCTs so as to minimize biases that often arise when using real-world evidence (RWE) to estimate causal effects.</div></div><div><h3>Methods:</h3><div>We divided the process of conducting TTEs into three phases and seven steps. We then systematically reviewed the literature to identify currently available tools that support one or more of the seven steps required to conduct a TTE. For each tool, we noted which step or steps the tool supports.</div></div><div><h3>Results:</h3><div>7625 papers were included in the initial review, with 76 meeting our inclusion criteria. Our review identified 24 distinct tools applicable to the three phases of TTE. Specifically, 3 tools support the Design Phase, 5 support the Implementation Phase, and 19 support the Analysis Phase, with some tools applicable to multiple phases.</div></div><div><h3>Conclusion:</h3><div>This review revealed significant gaps in tool support for the Design Phase of TTEs, while support for the Implementation and Analysis phases was highly variable. No single tool currently supports all aspects of TTEs from start to finish and few tools are interoperable, meaning they cannot be easily integrated into a unified workflow. The results highlight the need for further development of informatics tools for supporting TTEs.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104897"},"PeriodicalIF":4.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145185991","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":"Drug repositioning with metapath guidance and adaptive negative sampling enhancement","authors":"Yaozheng Zhou ,&nbsp;Xingyu Shi ,&nbsp;Lingfeng Wang ,&nbsp;Jin Xu ,&nbsp;Demin Li ,&nbsp;Congzhou Chen","doi":"10.1016/j.jbi.2025.104916","DOIUrl":"10.1016/j.jbi.2025.104916","url":null,"abstract":"<div><h3>Objective:</h3><div>Drug repositioning plays a pivotal role in expediting the drug discovery pipeline. The rapid development of computational methods has opened new avenues for predicting drug-disease associations (DDAs). Despite advancements in existing methodologies, challenges such as insufficient exploration of diverse relationships in heterogeneous biological networks and inadequate quality of negative samples have persisted.</div></div><div><h3>Methods:</h3><div>In this study, we introduce DRMGNE, a novel drug repositioning framework that harnesses metapath-guided learning and adaptive negative enhancement for DDA prediction. DRMGNE initiates with an autoencoder to extract semantic features based on similarity matrices. Subsequently, a comprehensive set of metapaths is designed to generate subgraphs, and graph convolutional networks are utilized to extract enriched node representations reflecting topological structures. Furthermore, the adaptive negative enhancement strategy is employed to improve the quality of negative samples, ensuring balanced learning.</div></div><div><h3>Results:</h3><div>Experimental evaluations demonstrate that DRMGNE outperforms state-of-the-art algorithms across three benchmark datasets. Additionally, case studies and molecular docking validations further underscore its potential in facilitating drug discovery and accelerating drug repurposing efforts.</div></div><div><h3>Conclusion:</h3><div>DRMGNE is a novel framework for DDA prediction that leverages metapath-based guidance and adaptive negative enhancement. Experiments on benchmark datasets show superior performance over existing methods, underscoring its potential impact in drug discovery.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"171 ","pages":"Article 104916"},"PeriodicalIF":4.5,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182165","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 crisis of biomedical foundation models.","authors":"Fei Wang","doi":"10.1016/j.jbi.2025.104917","DOIUrl":"10.1016/j.jbi.2025.104917","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104917"},"PeriodicalIF":4.5,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182104","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}