International Journal of Biomedical Imaging最新文献

{"title":"AI-Powered Early Detection of Retinal Conditions: A Deep Learning Approach for Diabetic Retinopathy and Beyond.","authors":"Ali Basim Mahdi, Zahraa A Mousa Al-Ibraheemi, Zahraa Fadhil Kadhim, Raffef Jabar Abbrahim, Yaqeen Sameer Dhayool, Ghasaq Mankhey Jabbar, Sajjad A Mohammed","doi":"10.1155/ijbi/6154285","DOIUrl":"https://doi.org/10.1155/ijbi/6154285","url":null,"abstract":"<p><p>Various retinal conditions, such as diabetic macular edema (DME) and choroidal neovascularization (CNV), pose significant risks of visual impairment and vision loss. Early detection through automated and accurate and advanced systems can greatly enhance clinical outcomes for patients as well as for medical staff. This study is aimed at developing a deep learning-based model for the early detection of retinal diseases using OCT images. We utilized a publicly available retinal image dataset comprising images with DME, CNV, drusen, and normal cases. The Inception model was trained and validated using various evaluation metrics. Performance metrics, including accuracy, precision, recall, and <i>F</i>1 score, were calculated. The proposed model achieved an accuracy of 94.2%, with precision, recall, and <i>F</i>1 scores exceeding 92% across all classes. Statistical analysis demonstrated the robustness of the model across folds. Our findings highlight the potential of AI-powered systems in improving early detection of retinal conditions, paving the way for integration into clinical workflows. More efforts are needed to utilize it offline by making it available on ophthalmologist mobile devices to facilitate the diagnosis process and provide better service to patients.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"6154285"},"PeriodicalIF":1.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12517976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing TB Bacteria Detection Efficiency: Utilizing RetinaNet-Based Preprocessing Techniques for Small Image Patch Classification.","authors":"Shwetha V, Barnini Banerjee, Vijaya Laxmi, Priya Kamath","doi":"10.1155/ijbi/3559598","DOIUrl":"https://doi.org/10.1155/ijbi/3559598","url":null,"abstract":"<p><p>Tuberculosis (TB), caused by <i>Mycobacterium tuberculosis</i>, is a re-emerging disease that necessitates early and accurate detection. While Ziehl-Neelsen (ZN) staining is effective in highlighting bacterial morphology, automation significantly accelerates the diagnostic workflow. However, detecting TB bacilli-which are typically much smaller than white blood cells (WBCs)-in stained images remains a considerable challenge. This study leverages the ZNSM-iDB dataset, which comprises approximately 2000 publicly available images captured using different staining methods. Notably, 800 images are fully stained with the ZN technique. We propose a novel two-stage pipeline where a RetinaNet-based object detection model functions as a preprocessing step to localize and isolate TB bacilli and WBCs from ZN-stained images. To address the challenges posed by low spatial resolution and background interference, the RetinaNet model is enhanced with dilated convolutional layers to improve fine-grained feature extraction. This approach not only facilitates accurate detection of small objects but also achieves an average precision (AP) of 0.94 for WBCs and 0.97 for TB bacilli. Following detection, a patch-based convolutional neural network (CNN) classifier is employed to classify the extracted regions. The proposed CNN model achieves a remarkable classification accuracy of 93%, outperforming other traditional CNN architectures. This framework demonstrates a robust and scalable solution for automated TB screening using ZN-stained microscopy images.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"3559598"},"PeriodicalIF":1.3,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CAGs-Net: A Novel Adjacent-Context Network With Channel Attention Gate for 3D Brain Tumor Image Segmentation.","authors":"Qianqian Ye, Yuhu Shi, Shunjie Guo","doi":"10.1155/ijbi/6656059","DOIUrl":"10.1155/ijbi/6656059","url":null,"abstract":"<p><p>Accurate brain tumor segmentation is essential for clinical decision-making, yet remains difficult to automate. Key obstacles include the small volume of lesions, their morphological diversity, poorly defined MRI boundaries, and nonuniform intensity profiles. Furthermore, while traditional segmentation approaches often focus on intralayer relevance, they frequently underutilize the rich semantic correlations between features extracted from adjacent network layers. Concurrently, classical attention mechanisms, while effective for highlighting salient regions, often lack explicit mechanisms for directing feature refinement along specific dimensions. To solve these problems, this paper presents CAGs-Net, a novel network that progressively constructs semantic dependencies between neighboring layers in the UNet hierarchy, enabling effective integration of local and global contextual information. Meanwhile, the channel attention gate was embedded within this adjacent-context network. These gates strategically fuse shallow appearance features and deep semantic information, leveraging channel-wise relationships to refine features by recalibrating voxel spatial responses. In addition, the hybrid loss combining generalized dice loss and binary cross-entropy loss was employed to avoid severe class imbalance inherent in lesion segmentation. Therefore, CAGs-Net uniquely combines adjacent-context modeling with channel attention gates to enhance feature refinement, outperforming traditional UNet-based methods, and the experimental results demonstrated that CAGs-Net shows better segmentation performance in comparison with some state-of-the-art methods for brain tumor image segmentation.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"6656059"},"PeriodicalIF":1.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144973775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Lesion Segmentation in Ultrasound Images: The Impact of Targeted Data Augmentation Strategies.","authors":"Xu Wang, Patrice Monkam, Bonan Zhao, Shouliang Qi, He Ma, Long Huang, Wei Qian","doi":"10.1155/ijbi/3309822","DOIUrl":"10.1155/ijbi/3309822","url":null,"abstract":"<p><p>Automated lesion segmentation in ultrasound (US) images based on deep learning (DL) approaches plays a crucial role in disease diagnosis and treatment. However, the successful implementation of these approaches is conditioned by large-scale and diverse annotated datasets whose obtention is tedious and expertise demanding. Although methods like generative adversarial networks (GANs) can help address sample scarcity, they are often associated with complex training processes and high computational demands, which can limit their practicality and feasibility, especially in resource-constrained scenarios. Therefore, this study is aimed at exploring new solutions to address the challenge of limited annotated samples in automated lesion delineation in US images. Specifically, we propose five distinct mixed sample augmentation strategies and assess their effectiveness using four deep segmentation models for the delineation of two lesion types: breast and thyroid lesions. Extensive experimental analyses indicate that the effectiveness of these augmentation strategies is strongly influenced by both the lesion type and the model architecture. When appropriately selected, these strategies result in substantial performance improvements, with the Dice and Jaccard indices increasing by up to 37.95% and 36.32% for breast lesions and 14.59% and 13.01% for thyroid lesions, respectively. These improvements highlight the potential of the proposed strategies as a reliable solution to address data scarcity in automated lesion segmentation tasks. Furthermore, the study emphasizes the critical importance of carefully selecting data augmentation approaches, offering valuable insights into how their strategic application can significantly enhance the performance of DL models.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"3309822"},"PeriodicalIF":1.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification.","authors":"Rafiqul Islam, Sazzad Hossain","doi":"10.1155/ijbi/2149042","DOIUrl":"10.1155/ijbi/2149042","url":null,"abstract":"<p><p>Brain tumors are complex clinical lesions with diverse morphological characteristics, making accurate segmentation from MRI scans a challenging task. Manual segmentation by radiologists is time-consuming and susceptible to human error. Consequently, automated approaches are anticipated to accurately delineate tumor boundaries and quantify tumor burden, addressing these challenges efficiently. The presented work integrates a convolutional block attention module (CBAM) into a deep learning architecture to enhance the accuracy of MRI-based brain tumor segmentation. The deep learning network is built upon a VGG19-based U-Net model, augmented with depthwise and pointwise convolutions to improve feature extraction and processing efficiency during brain tumor segmentation. Furthermore, the proposed framework enhances segmentation precision while simultaneously incorporating tumor area measurement, making it a comprehensive tool for early-stage tumor analysis. Several qualitative assessments are used to assess the performance of the model in terms of tumor segmentation analysis. The qualitative metrics typically analyze the overlap between predicted tumor masks and ground truth annotations, providing information on the segmentation algorithms' accuracy and dependability. Following segmentation, a new approach is used to compute the extent of segmented tumor areas in MRI scans. This involves counting the number of pixels within the segmented tumor masks and multiplying by their area or volume. The computed tumor areas offer quantifiable data for future investigation and clinical interpretation. In general, the proposed methodology is projected to improve segmentation accuracy, efficiency, and clinical relevance compared to existing methods, resulting in better diagnosis, treatment planning, and monitoring of patients with brain tumors.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"2149042"},"PeriodicalIF":1.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12334286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144817900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Deep Learning-Based Subabdominal MR Image Segmentation During Rectal Cancer Treatment: Exploiting Multiscale Feature Pyramid Network and Bidirectional Cross-Attention Mechanism.","authors":"Yu Xiao, Xin Yang, Sijuan Huang, Lihua Guo","doi":"10.1155/ijbi/7560099","DOIUrl":"10.1155/ijbi/7560099","url":null,"abstract":"<p><p><b>Background:</b> This study is aimed at solving the misalignment and semantic gap caused by multiple convolutional and pooling operations in U-Net while segmenting subabdominal MR images during rectal cancer treatment. <b>Methods:</b> We propose a new approach for MR Image Segmentation based on a multiscale feature pyramid network and a bidirectional cross-attention mechanism. Our approach comprises two innovative modules: (1) We use dilated convolution and a multiscale feature pyramid network in the encoding phase to mitigate the semantic gap, and (2) we implement a bidirectional cross-attention mechanism to preserve spatial information in U-Net and reduce misalignment. <b>Results:</b> Experimental results on a subabdominal MR image dataset demonstrate that our proposed method outperforms existing methods. <b>Conclusion:</b> A multiscale feature pyramid network effectively reduces the semantic gap, and the bidirectional cross-attention mechanism facilitates feature alignment between the encoding and decoding stages.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"7560099"},"PeriodicalIF":1.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12313379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144761710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model.","authors":"S Trisheela, Roshan Fernandes, Anisha P Rodrigues, S Supreeth, B J Ambika, Piyush Kumar Pareek, Rakesh Kumar Godi, G Shruthi","doi":"10.1155/ijbi/5535505","DOIUrl":"10.1155/ijbi/5535505","url":null,"abstract":"<p><p>The objective of AI research and development is to create intelligent systems capable of performing tasks and reasoning like humans. Artificial intelligence extends beyond pattern recognition, planning, and problem-solving, particularly in the realm of machine learning, where deep learning frameworks play a pivotal role. This study focuses on enhancing brain tumour detection in MRI scans using deep learning techniques. Malignant brain tumours result from abnormal cell growth, leading to severe neurological complications and high mortality rates. Early diagnosis is essential for effective treatment, and our research aims to improve detection accuracy through advanced AI methodologies. We propose a modified DarkNet-53 architecture, optimized with invasive weed optimization (IWO), to extract critical features from preprocessed MRI images. The model's presentation is assessed using accuracy, recall, loss, and AUC, achieving a 95% success rate on a dataset of 3264 MRI scans. The results demonstrate that our approach surpasses existing methods in accurately identifying a wide range of brain tumours at an early stage, contributing to improved diagnostic precision and patient outcomes.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"5535505"},"PeriodicalIF":3.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the Effect of Antenna-to-Head Distance for Microwave Brain Imaging Applications.","authors":"Farhana Parveen, Parveen Wahid","doi":"10.1155/ijbi/8872566","DOIUrl":"https://doi.org/10.1155/ijbi/8872566","url":null,"abstract":"<p><p>Wideband antennas are extensively used in many medical applications, which require the placement of the antenna on or near a human body. The performance of the antenna should remain compliant with the requirements of the target application when placed in front of the subject under investigation. Since the performance of an antenna varies when the distance from the subject is changed, the effect of varying the distance of a miniaturized wideband antipodal Vivaldi antenna from a numerical head model is analyzed in this work. The analyses can demonstrate whether the antenna performance and its effect on the head aptly comply with the requirements for the intended application of microwave brain imaging. It is observed that, when the antenna-head distance is increased, the background noise in the received signal is enhanced, whereas when the distance is reduced, the radiation-safety consideration on the head is affected. Hence, the optimum distance should provide a good compromise in terms of both signal receptibility by the antenna and radiation safety on the head. As the optimum antenna-to-head distance may vary with the change in antenna, measurement system, and the surrounding medium, this work presents a basic analysis procedure to find the appropriate antenna distance for the intended application.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"8872566"},"PeriodicalIF":3.3,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12066184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessments of Medical Student's Knowledge About Radiation Protection and Different Imaging Modalities in Jeddah, Saudi Arabia.","authors":"Raghad Aljondi, Rahaf Alem, Rowa Aljondi, Abdulrahman Tajaldeen, Salem Saeed Alghamdi, Mohammed Majdi Toras","doi":"10.1155/ijbi/1528291","DOIUrl":"https://doi.org/10.1155/ijbi/1528291","url":null,"abstract":"<p><p><b>Introduction:</b> Doctors can play a significant role in attributing to patient safety concerning exposure to ionizing radiation. Therefore, healthcare professionals should have adequate knowledge about radiation risk and protection of different medical imaging examinations. This study aims to evaluate the knowledge about radiation protection (RP) and applications of different imaging modalities (IMs) among medical students in their clinical years and intern, in Jeddah, Saudi Arabia. <b>Materials and Methods:</b> A cross-sectional study based on an online questionnaire was performed in Jeddah, Saudi Arabia, on 170 medical students during January 2024; the study participants included clinical years medical students (from Years 4 to 6) and interns of both gender and basic year medical students, and specialists and consultants were excluded. For each participant, the percentage of correct answers was calculated for the knowledge RP and knowledge in IMs separately, and each participant will have two scores, RP knowledge score (RPKS) and IM knowledge score (IMKS). <b>Results:</b> A total of 170 medical students responded and completed the questionnaire. The overall levels of awareness and knowledge of the students was determined through calculations of their scores in answering the questionnaire; students in this study group have low average knowledge score in RP, which is 43, while they have moderate-high knowledge score in IMs, which is 68. Regarding the knowledge score, for the RPKS, the best participant scored 82, while the worst scored 0, whereas for IMKS, the best participant score 100, while the worst scored 0. However, according to the SD, participants generally differ between each other by 19 in RPKS and 31 in IMKS. <b>Conclusions:</b> The assessments of medical students' knowledge regarding radiation exposure in diagnostic modalities reveal a low level of confidence in their knowledge of ionizing radiation dose parameters. Furthermore, the mean scores on overall knowledge assessments indicate a need for improvement in RP knowledge for medical students. To address this gap, a comprehensive modification of the undergraduate medical curriculum's radiology component is required by enhancing active learning approaches and integrating radiation safety courses early in the medical curriculum. Medical education institutions could implement ongoing workshops, online modules, and certification programs to reinforce radiation safety principles.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"1528291"},"PeriodicalIF":3.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12045688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143989135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Anatomical and Indication-Based Diagnostic Reference Levels (DRLs) in Head CT Imaging: Implications for Radiation Dose Management.","authors":"Benard Ohene-Botwe, Samuel Anim-Sampong, Robert Saizi","doi":"10.1155/ijbi/6464273","DOIUrl":"10.1155/ijbi/6464273","url":null,"abstract":"<p><p><b>Introduction:</b> Many diagnostic reference levels (DRLs) in computed tomography (CT) imaging are based mainly on anatomical locations and often overlook variations in radiation exposure due to different clinical indications. While indication-based DRLs, derived from dose descriptors like volume-weighted CT dose index (CTDI_vol) and dose length product (DLP), are recommended for optimising patient radiation exposure, many studies still use anatomical-based DRL values. This study is aimed at quantifying the differences between anatomical and indication-based DRL values in head CT imaging and assessing its implications for radiation dose management. This will support the narrative when explaining the distinction between indication-based DRLs and anatomical DRLs for patients' dose management. <b>Methods:</b> Employing a retrospective quantitative study design, we developed and compared anatomical and common indication-based DRL values using a dataset of head CT scans with similar characteristics. The indications included in the study were brain tumor/intracranial space-occupying lesion (ISOL), head injury/trauma, stroke, and anatomical examinations. Data analysis was conducted using SPSS Version 29. <b>Results:</b> The findings suggest that using anatomical-based DLP DRL values for CT head examinations leads to underestimations in the median, 25th percentile, and 75th percentile values of head injury/trauma by 20.2%, 30.0%, and 14.5% in single-phase CT head procedures. Conversely, for the entire examination, using anatomical-based DLP DRL as a benchmark for CT stroke DRL overestimates median, 25th percentile, and 75th percentile values by 18.3%, 23.9%, and 13.5%. Brain tumor/ISOL DL<i>P</i> values are underestimated by 62.6%, 60.4%, and 71.8%, respectively. <b>Conclusion:</b>The study highlights that using anatomical DLP DRL values for specific indications in head CT scans can lead to underestimated or overestimated DL<i>P</i> values, making them less reliable for radiation management compared to indication-based DRLs. Therefore, it is imperative to promote the establishment and use of indication-based DRLs for more accurate dose management in CT imaging.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2025 ","pages":"6464273"},"PeriodicalIF":3.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11944678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143721830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}