Bioengineering最新文献

{"title":"Dementia and Heart Failure Classification Using Optimized Weighted Objective Distance and Blood Biomarker-Based Features.","authors":"Veerasak Noonpan, Supansa Chaising, Georgi Hristov, Punnarumol Temdee","doi":"10.3390/bioengineering12090980","DOIUrl":"10.3390/bioengineering12090980","url":null,"abstract":"<p><p>Dementia and heart failure are growing global health issues, exacerbated by aging populations and disparities in care access. Diagnosing these conditions often requires advanced equipment or tests with limited availability. A reliable tool distinguishing between the two conditions is essential, enabling more accurate diagnoses and reducing misclassifications and inappropriate referrals. This study proposes a novel measurement, the optimized weighted objective distance (OWOD), a modified version of the weighted objective distance, for the classification of dementia and heart failure. The OWOD is designed to enhance model generalization through a data-driven approach. By enhancing objective class generalization, applying multi-feature distance normalization, and identifying the most significant features for classification-together with newly integrated blood biomarker features-the OWOD could strengthen the classification of dementia and heart failure. A combination of risk factors and proposed blood biomarkers (derived from 10,000 electronic health records at Chiang Rai Prachanukroh Hospital, Chiang Rai, Thailand), comprising 20 features, demonstrated the best OWOD classification performance. For model evaluation, the proposed OWOD-based classification method attained an accuracy of 95.45%, a precision of 96.14%, a recall of 94.70%, an F1-score of 95.42%, and an area under the receiver operating characteristic curve of 97.10%, surpassing the results obtained using other machine learning-based classification models (gradient boosting, decision tree, neural network, and support vector machine).</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-Year Clinical Performance of Ultra-Thin No-Prep Veneers from 5Y-TZP Zirconia: A Retrospective Study.","authors":"Katarzyna Taraszkiewicz-Sulik, Patryk Wiśniewski, Edyta Cywoniuk, Teresa Sierpińska","doi":"10.3390/bioengineering12090976","DOIUrl":"10.3390/bioengineering12090976","url":null,"abstract":"<p><strong>Objective: </strong>This retrospective study aimed to evaluate the two-year clinical performance of ultra-thin, no-prep Prettau^® Skin zirconia veneers placed in the anterior region of the maxilla and mandible.</p><p><strong>Materials and methods: </strong>This single-cohort retrospective series did not include a conventional control group. A total of 201 veneers (Prettau^® Skin, 5Y-TZP zirconia) were placed in the anterior maxilla and mandible. Veneers were air-abraded with 50 µm Al₂O₃ (0.25 MPa, ~10 mm, 20 s) and bonded using an MDP-containing adhesive (Tokuyama Bond, Tokuyama Dental, Japan) and dual-cure resin cement (Estecem II, Tokuyama Dental, Japan) following enamel etching with 37% H₃PO₄ (Etching Gel, Cerkamed, Poland). Clinical performance was assessed using the modified FDI criteria after two years.</p><p><strong>Results: </strong>At 24 months, no debonding events were recorded. The survival rate was 99.5% (95% CI: 97.3-99.9). Fracture rate was 0.5% (95% CI: 0.1-2.8). Most veneers received \"very good\" scores for surface luster (81.6%, 95% CI: 75.6-86.4), color match (96.0%, 95% CI: 92.0-98.0), marginal adaptation (84.1%, 95% CI: 78.3-88.6), and anatomical form (100%, 95% CI: 98.1-100). Periodontal response was rated as \"very good\" or \"good\" in 90.0% (95% CI: 85.4-93.4) of cases. Patient satisfaction remained consistently high (100%, 95% CI: 98.1-100).</p><p><strong>Conclusions: </strong>Ultra-thin, no-prep Prettau^® Skin zirconia veneers show favorable short-term clinical outcomes, offering excellent esthetic results, mechanical stability, and biological compatibility. These findings support their use as a minimally invasive option in anterior restorative dentistry. However, further long-term studies are needed to confirm their durability and compare outcomes with conventional veneer techniques.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Assisted Fusion Technique for Orthodontic Diagnosis Between Cone-Beam Computed Tomography and Face Scan Data.","authors":"Than Trong Khanh Dat, Jang-Hoon Ahn, Hyunkyo Lim, Jonghun Yoon","doi":"10.3390/bioengineering12090975","DOIUrl":"10.3390/bioengineering12090975","url":null,"abstract":"<p><p>This study presents a deep learning-based approach that integrates cone-beam computed tomography (CBCT) with facial scan data, aiming to enhance diagnostic accuracy and treatment planning in medical imaging, particularly in cosmetic surgery and orthodontics. The method combines facial mesh detection with the iterative closest point (ICP) algorithm to address common challenges such as differences in data acquisition times and extraneous details in facial scans. By leveraging a deep learning model, the system achieves more precise facial mesh detection, thereby enabling highly accurate initial alignment. Experimental results demonstrate average registration errors of approximately 0.3 mm (inlier RMSE), even when CBCT and facial scans are acquired independently. These results should be regarded as preliminary, representing a feasibility study rather than conclusive evidence of clinical accuracy. Nevertheless, the approach demonstrates consistent performance across different scan orientations, suggesting potential for future clinical application. Furthermore, the deep learning framework effectively handles diverse and complex facial geometries, thereby improving the reliability of the alignment process. This integration not only enhances the precision of 3D facial recognition but also improves the efficiency of clinical workflows. Future developments will aim to reduce processing time and enable simultaneous data capture to further improve accuracy and operational efficiency. Overall, this approach provides a powerful tool for practitioners, contributing to improved diagnostic outcomes and optimized treatment strategies in medical imaging.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The ASC Module: A GPU Memory-Efficient, Physiology-Aware Approach for Improving Segmentation Accuracy on Poorly Contrast-Enhanced CT Scans-A Preliminary Study.","authors":"Zuoyuan Zhao, Toru Higaki, Yanlei Gu, Bisser Raytchev","doi":"10.3390/bioengineering12090974","DOIUrl":"10.3390/bioengineering12090974","url":null,"abstract":"<p><p>At present, some aging populations, such as those in Japan, face an underlying risk of inadequate medical resources. Using neural networks to assist doctors in locating the aorta in patients via computed tomography (CT) before surgery is a task with practical value. While UNet and some of its derived models are efficient for the semantic segmentation of optimally contrast-enhanced CT images, their segmentation accuracy on poorly or non-contrasted CT images is too low to provide usable results. To solve this problem, we propose a data-processing module based on the physical-spatial structure and anatomical properties of the aorta, which we call the Automatic Spatial Contrast Module. In an experiment using UNet, Attention UNet, TransUNet, and Swin-UNet as baselines, modified versions of these models using the proposed Automatic Spatial Contrast (ASC) Module showed improvements of up to 24.84% in the Intersection-over-Union (IoU) and 28.13% in the Dice Similarity Coefficient (DSC). Furthermore, the proposed approach entails only a small increase in GPU memory when compared with the baseline models.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proof-of-Concept Study: Hyperspectral Imaging for Quantification of DKK-3 Expression in Oropharyngeal Carcinoma.","authors":"Theresa Mittermair, Andrea Brunner, Bettina Zelger, Rohit Arora, Christian Wolfgang Huck, Johannes Dominikus Pallua","doi":"10.3390/bioengineering12090971","DOIUrl":"10.3390/bioengineering12090971","url":null,"abstract":"<p><strong>Introduction: </strong>Oral squamous cell carcinoma (OSCC) is one of the most common tumours worldwide. This study investigated the suitability of visible and near-infrared hyperspectral imaging compared to visual assessment and conventional digital image analysis for quantifying immunohistochemical staining on the example of Dickkopf-3 (DKK-3) in OSCC.</p><p><strong>Materials and methods: </strong>A retrospective analysis of TMAs containing DKK-3 stained OSCC of 50 patients was retrieved from the archives at the Institute of Pathology, Medical University of Innsbruck. TMAs were first evaluated visually, followed by digital image analysis using QuPath (version 0.3.2, open-source software). For hyperspectral imaging, six exemplary cases were selected (three cases with strong expression and three cases with weak expression) and evaluated. The collected hyperspectral images were visualised using TIVITA (Tissue Imaging System). The resulting true-colour images and the classified HSI images were then assessed using the QuPath software. The Allred score and the H-score were used for all analyses.</p><p><strong>Results: </strong>97 tissue cores were used for visual and digital image analysis. No significant difference was found between the evaluations of visual and digital image analysis using the H-score (pWilcoxon = 0.278), and both H-scores correlated significantly with each other (p^Spearman < 0.001). Similar results were also found using the Allred score. The kappa value was 0.67, which represents a \"substantial\" correlation. Finally, the H-scores and Allred scores were compared for visual, digital, and HSI imaging. No significant differences were found between the three groups concerning the H-score (pWilcoxon > 0.1). Using Cohen's Kappa, a \"fair\" to \"moderate\" correlation was observed between the three evaluations.</p><p><strong>Conclusion: </strong>Visible and near-infrared hyperspectral imaging (VIS-NIR-HSI) is a promising complementary tool for digital pathology workflows. This proof-of-concept study suggests that HSI offers the potential for more objective quantification of DKK-3 expression in oropharyngeal squamous cell carcinoma, particularly in cases with weak staining. However, given the small sample size and exploratory design, the findings should be regarded as hypothesis-generating. Future studies with larger, clinically annotated cohorts and standardised workflows are needed before any consideration of routine clinical application.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics for Detecting Metaplastic Histology in Triple-Negative Breast Cancer: A Step Towards Personalized Therapy.","authors":"Rana Gunoz Comert, Gorkem Durak, Ravza Yilmaz, Halil Ertugrul Aktas, Zeynep Tuz, Hongyi Pan, Jun Zeng, Aysel Bayram, Baran Mollavelioglu, Sukru Mehmet Erturk, Ulas Bagci","doi":"10.3390/bioengineering12090973","DOIUrl":"10.3390/bioengineering12090973","url":null,"abstract":"<p><p>This study aims to develop and validate a multisequence MRI-based radiomics approach for distinguishing metaplastic breast cancer (MBC) from non-metaplastic triple-negative breast cancer (TNBC) at the initial diagnosis, which could facilitate optimal treatment selection. In this retrospective study, we analyzed 105 patients (27 MBC, 78 non-metaplastic TNBC) who underwent standardized breast magnetic resonance imaging (MRI), which included T1-weighted contrast-enhanced (T1W-CE) and short-tau inversion recovery (STIR) sequences. Two radiologists performed ground truth lesion segmentation, verified by a senior radiologist. We extracted 214 radiomic features (using PyRadiomics) and used least absolute shrinkage and selection operator (LASSO) regression for feature selection. Seven machine learning classifiers were thoroughly evaluated using five-fold cross-validation, with performance assessed through ROC analysis and accuracy metrics. The combined T1W-CE and STIR analysis demonstrated superior diagnostic performance for distinguishing MBC from non-metaplastic TNBC (AUC = 0.845; accuracy = 81%) compared with either sequence alone (T1W only AUC = 0.805; accuracy = 80%; STIR only AUC:0.768; accuracy = 77%). Multisequence MRI radiomics can reliably distinguish between MBC and TNBC at the time of initial diagnosis. This could potentially facilitate the selection of more appropriate treatments and help avoid ineffective chemotherapy for MBC patients.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid Screening of Anticoagulation Compounds for Biological Target-Associated Adverse Effects Using a Deep-Learning Framework in the Management of Atrial Fibrillation.","authors":"Tim Dong, Rhys Llewellyn, Melanie Hezzell, Gianni D Angelini","doi":"10.3390/bioengineering12090972","DOIUrl":"10.3390/bioengineering12090972","url":null,"abstract":"<p><p><b>Background:</b> Deep learning methods may be useful for drug compound interaction and discovery analysis. However, there has been limited research on their use for screening biologically related adverse effects. <b>Objectives:</b> This study aims to pre-emptively screen for likely drug use persistence or success in clinical trials. <b>Methods:</b> This shall be achieved through the extension, application, and evaluation of a deep learning-based framework. Specifically, it shall be considered in the discovery of novel candidates and mechanisms underlying AF management-related adverse effects. The targets were linked to their adverse effects specified in two previous studies, their corresponding protein sequences, and the organs affected. <b>Results:</b> The new model showed good performance when compared to existing approaches in the Side Effect Resource (SIDER) and Food and Drug Administration Adverse Event Reporting System (FAERS) external validation datasets. A precision of 0.879 was obtained for enoxaparin, along with a recall of 0.746 for rivaroxaban. Stronger bleeding-related adverse effects were found for edoxaban compared with apixaban and enoxaparin. The binding and safety profiles of sequoiaflavone were very similar to those of rivaroxaban. <b>Conclusions:</b> This study presents a framework that could be used to pre-emptively screen for adverse effects. In doing so, it considers the biological basis for guiding optimal drug selection.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ro(a)d to New Functional Materials: Sustainable Isolation of High-Aspect-Ratio β-Chitin Microrods from Marine Algae.","authors":"Jan Ludwig, Florian Kauffmann, Sabine Laschat, Ingrid M Weiss","doi":"10.3390/bioengineering12090969","DOIUrl":"10.3390/bioengineering12090969","url":null,"abstract":"<p><p>High-aspect-ratio rod-shaped chitins such as chitin whiskers or chitin nano- and microfibers are particularly promising for a wide range of applications, including electrorheological suspensions, lightweight reinforcement material for biocomposites, biomedical scaffolds, and food packaging. Here, we report the first mild water-based mechanical extraction protocol to isolate β-chitin microrods from the marine algal species <i>Thalassiosira rotula</i> while preserving their structural integrity throughout the process. The resulting microrods could be distributed into two populations based on the fultoportulae from which they are extruded. The rods exhibit typical dimensions of 12.6 ± 4.0 µm in length and 75 ± 21 nm in diameter (outer fultoportulae) or 17.5 ± 4.7 µm in length and 170 ± 39 nm in diameter (central fultoportulae), yielding high aspect ratios of ~168 and ~103 on average, respectively. Due to this environmentally friendly extraction, the high purity of the synthesized chitin, and the renewable algal source, this work introduces a sustainable route to produce pure biogenic β-chitin microrods.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Therapeutic Scope of Orofacial Mesenchymal Stem Cells.","authors":"Bharath Chandra Vaddaram, Akhilesh Kumar Shakya, Brandon R Zadeh, Diariza M Lopez, Jon Wagner, Todd Parco, Umadevi Kandalam","doi":"10.3390/bioengineering12090970","DOIUrl":"10.3390/bioengineering12090970","url":null,"abstract":"<p><p>Orofacial Mesenchymal Stem Cells (OMSCs) are an attractive and promising tool for tissue regeneration, with their potential for craniofacial bone repair being a primary focus of research. A key advantage driving their clinical interest is their accessibility from tissues that are often discarded, such as exfoliated deciduous teeth, which circumvents the ethical concerns and donor site morbidity associated with other stem cell sources. The high proliferation ability and multi-differentiation capacity of OMSCs make them a unique resource for tissue engineering. Recently, OMSCs have been explored in the restoration of the heart and skin, treatment of oral mucosal lesions, and regeneration of hard connective tissues such as cartilage. Beyond their direct regenerative capabilities, OMSCs possess potent immunomodulatory functions, enabling them to regulate the immune system in various inflammatory disorders through the secretion of cytokines. This review offers an in-depth update regarding the therapeutic possibilities of OMSCs, highlighting their roles in the regeneration of bone and various tissues, outlining their immunomodulatory capabilities, and examining the essential technologies necessary for their clinical application.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanoparticle-Based Delivery Systems for Synergistic Therapy in Lung Cancers.","authors":"Zicheng Deng, Ali Al Siraj, Isabella Lowry, Ellen Ruan, Rohan Patel, Wen Gao, Tanya V Kalin, Vladimir V Kalinichenko","doi":"10.3390/bioengineering12090968","DOIUrl":"10.3390/bioengineering12090968","url":null,"abstract":"<p><p>Lung cancer remains the leading cause of cancer-related mortality worldwide, with conventional treatments often limited by systemic toxicity, different tumor sensitivity to the drugs, and the emergence of multidrug resistance. To address these challenges, nanoparticle-based delivery systems have emerged as an innovative strategy, enabling the simultaneous transport of multiple agents, including chemotherapeutic drugs and expression vectors, to enhance treatment efficacy and overcome tumor resistance. This review explores various nanocarrier platforms, such as liposomes, solid lipid nanoparticles, polymeric micelles, and inorganic nanoparticles, specifically designed for lung cancer therapy. Synergistic effects and physicochemical properties of therapeutic agents must be carefully considered in the design of nanoparticle-based co-delivery systems for lung cancer therapy. We highlight the applications of these nanoparticle systems in drug-drug, gene-gene, and drug-gene co-delivery approaches. By addressing the limitations of traditional therapies, nanoparticle-based systems offer a promising avenue to improve outcomes in patients with lung cancers.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}