Medical Journal Armed Forces India最新文献_第9页

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2025-10-31 DOI: 10.1016/j.mjafi.2025.09.001

Rakesh Datta

引用次数: 0

Adoption of artificial intelligence technologies in health care: A cross-sectional survey on insights and perspectives of healthcare professionals 在医疗保健中采用人工智能技术：对医疗保健专业人员的见解和观点的横断面调查

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2025-06-19 DOI: 10.1016/j.mjafi.2025.04.012

Poonam Raj , Anubhav Singh , Kamal Preet Singh , Rakesh Datta

{"title":"Adoption of artificial intelligence technologies in health care: A cross-sectional survey on insights and perspectives of healthcare professionals","authors":"Poonam Raj , Anubhav Singh , Kamal Preet Singh , Rakesh Datta","doi":"10.1016/j.mjafi.2025.04.012","DOIUrl":"10.1016/j.mjafi.2025.04.012","url":null,"abstract":"<div><h3>Background</h3><div>The successful implementation of artificial intelligence (AI)–enabled technologies in health care requires a thorough understanding of the needs and expectations of healthcare professionals (HCPs). This study evaluated the acceptability, expectations, needs, and concerns of HCPs regarding the adoption of AI technologies.</div></div><div><h3>Methods</h3><div>A cross-sectional survey of 572 HCPs was conducted using an online survey questionnaire. The survey responses were aggregated, and proportions of agreement were computed to assess the familiarity, perception, expectations, and attitudes towards the adoption of AI technologies in health care. The data were further analysed as per the broad specialities of respondents. Thematic analysis was conducted to analyse the responses to open-ended questions.</div></div><div><h3>Results</h3><div>The survey found significant gaps in technical knowledge and expertise for implementation of AI-enabled healthcare technologies, with potential scope for improvement. Whilst 73.33% of respondents rated their knowledge of computers as mediocre, 42.31% were familiar with the concepts of AI, and only 36.89% were familiar with applications of AI in their speciality. Medicine and allied specialities had the least agreement regarding the adoption of AI. The respondents noted strong optimism regarding the potential of AI in improving efficiency and clinical outcomes. However, various technical, legal, and regulatory challenges remain to be addressed before full-scale implementation of AI technologies in health care.</div></div><div><h3>Conclusion</h3><div>The study provides valuable insights into the perspectives of HCPs regarding the integration of AI-assisted technologies, highlighting the importance of training in AI, development of robust technologies, and addressing the needs and concerns to ensure the optimal utilisation of AI in health care.</div></div>","PeriodicalId":39387,"journal":{"name":"Medical Journal Armed Forces India","volume":"81 6","pages":"Pages 680-688"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Significance of increased 18F-FDG uptake in determining pathological status in post-chemotherapy lymphoma cases involving head-neck region on PET-CT 18F-FDG摄取增加在PET-CT上判断头颈部化疗后淋巴瘤病理状态的意义

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2024-11-06 DOI: 10.1016/j.mjafi.2024.09.009

D.K. Gupta , Sanjeeva Bharadwaja , M. Vigneshwaran , A.G. Pandit

{"title":"Significance of increased 18F-FDG uptake in determining pathological status in post-chemotherapy lymphoma cases involving head-neck region on PET-CT","authors":"D.K. Gupta , Sanjeeva Bharadwaja , M. Vigneshwaran , A.G. Pandit","doi":"10.1016/j.mjafi.2024.09.009","DOIUrl":"10.1016/j.mjafi.2024.09.009","url":null,"abstract":"<div><h3>Background</h3><div>Currently, all follow-up cases of lymphoma showing fluorodeoxyglucose (FDG) avid uptake on positron emission tomography/computed tomography (PET-CT) in head-neck region undergo biopsy for tissue confirmation of diagnosis. There are no consensus or practice guidelines in literature pertaining, whether to biopsy all such FDG avid lesions post-chemotherapy. Hence, this study was conducted to determine the significance of PET-CT (SUVmax 5.0 or above) in determining remission or pathological status in these patients.</div></div><div><h3>Methods</h3><div><span><span><span>A retrospective cohort analysis conducted between July 2019 and May 2023 at our institute of 40 follow-up cases of lymphoma post-chemotherapy (Group A) showing FDG Uptake with SUVmax 5.0 or above on PET-CT in </span>Waldeyer's Ring (WR) or neck, and 40 non- lymphoma cases (Group B) showing FDG Uptake with similar SUVmax in WR or neck. All cases in group A underwent </span>tonsillectomy and results of </span>histopathology were correlated with PET-CT findings. All cases in group B underwent thorough clinical correlation with respect to PET-CT findings. A Receiver Operating Characteristic (ROC) curve for SUVmax was plotted to statistically analyse this data.</div></div><div><h3>Results</h3><div>Statistical analysis of our observations by plotting a ROC curve for SUVmax revealed a sensitivity of 90% with 10% specificity and an insignificant p-value of 0.733.</div></div><div><h3>Conclusion</h3><div>We propose that unnecessary biopsy in all cases with FDG avid uptake must not be encouraged unless accompanied by a suspicious holistic clinical picture of asymmetry, significant lymph node involvement with FDG avid uptake and counterpart finding on CT. A tissue confirmation in all such cases may result in unnecessary risk and increase the cost of therapy.</div></div>","PeriodicalId":39387,"journal":{"name":"Medical Journal Armed Forces India","volume":"81 6","pages":"Pages 728-734"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and validation of an artificial intelligence algorithm for cervical vertebral maturation staging using lateral cephalograms 开发和验证人工智能算法的颈椎成熟分期使用侧位脑电图

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2025-09-04 DOI: 10.1016/j.mjafi.2025.08.012

Ramnarayan BK , Sindhu P , Preeti Patil , Arjun Krishnamurthy , Mahesh DR , Darshana S

{"title":"Development and validation of an artificial intelligence algorithm for cervical vertebral maturation staging using lateral cephalograms","authors":"Ramnarayan BK , Sindhu P , Preeti Patil , Arjun Krishnamurthy , Mahesh DR , Darshana S","doi":"10.1016/j.mjafi.2025.08.012","DOIUrl":"10.1016/j.mjafi.2025.08.012","url":null,"abstract":"<div><h3>Background</h3><div>Cervical vertebral maturation (CVM) assessment using lateral cephalograms offers a reliable method for evaluating skeletal maturity without additional radiation exposure. However, traditional manual analysis is time-intensive and prone to variability. With the growing role of artificial intelligence in medical imaging, this study aimed to develop and validate a deep learning algorithm capable of automatically determining CVM stages from lateral cephalometric radiographs, improving diagnostic efficiency and accuracy.</div></div><div><h3>Methods</h3><div>In total, 525 lateral cephalograms from individuals aged 7–17 years (249 males and 276 females; mean age, 12.67 years) were analyzed. An artificial intelligence-powered annotation platform, developed using PLAINSIGHT, was employed to identify 19 anatomical landmarks and perform 20 linear measurements on the C2, C3, and C4 vertebrae. The VGG19 convolutional neural network model was trained using 1300 augmented images generated from 420 original cephalograms. Model validation was performed on an independent dataset comprising 105 cephalograms.</div></div><div><h3>Results</h3><div>The trained VGG19 model achieved an overall accuracy of 86% in CVM staging, with optimal performance observed between 80 and 100 training epochs. Confusion matrix analysis indicated the highest classification accuracy in CVS stages 4, 5, and 6. The model demonstrated an overall F1 score of 0.85, with the highest score in CVS6 (0.93) and the lowest in CVS1 (0.79), reflecting robust predictive capability across multiple maturation stages.</div></div><div><h3>Conclusion</h3><div>The VGG19-based deep learning model showed strong potential for automating CVM assessment using lateral cephalograms. Its high accuracy and reproducibility suggest its utility as a clinical decision-support tool for evaluating skeletal development in growing individuals.</div></div>","PeriodicalId":39387,"journal":{"name":"Medical Journal Armed Forces India","volume":"81 6","pages":"Pages 672-679"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Role of artificial intelligence-enabled hand-held fundus camera for community-based diabetic retinopathy screening 人工智能手持式眼底相机在社区糖尿病视网膜病变筛查中的作用

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2024-11-19 DOI: 10.1016/j.mjafi.2024.09.008

Vijay K. Sharma , Srishti Khullar , Prabhjot Singh , Vikas Ambiya , Ashok Kumar , Anuroop N , Gaurav Kapoor , Preeti RK

{"title":"Role of artificial intelligence-enabled hand-held fundus camera for community-based diabetic retinopathy screening","authors":"Vijay K. Sharma , Srishti Khullar , Prabhjot Singh , Vikas Ambiya , Ashok Kumar , Anuroop N , Gaurav Kapoor , Preeti RK","doi":"10.1016/j.mjafi.2024.09.008","DOIUrl":"10.1016/j.mjafi.2024.09.008","url":null,"abstract":"<div><h3>Background</h3><div>This study aimed to assess the diagnostic accuracy of an artificial intelligence (AI) system integrated with a portable handheld fundus camera for the detection of diabetic retinopathy (DR) in a community-based screening program.</div></div><div><h3>Methods</h3><div>A DR screening camp was organized at a tertiary care hospital in India. A cohort of 261 patients with diabetes was screened using a nonmydriatic handheld fundus camera. Retinal images were graded by specialists and compared with the AI system's output. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC-ROC) were calculated. Subgroup analyses based on image quality was performed.</div></div><div><h3>Results</h3><div>Of the 261 patients screened, 253 had available retinal images, and 243 had gradable images. The AI system achieved a sensitivity of 85.29%, specificity of 99.04%, PPV of 93.55%, and NPV of 97.64% for detecting referable DR. The AUC-ROC was 0.93. The AI system's performance remained robust across all image-quality categories. The AI system showed strong agreement with human graders (κ = 0.86). However, it failed to identify certain non-DR pathologies detected by human graders.</div></div><div><h3>Conclusions</h3><div>The AI system integrated with a portable handheld fundus camera demonstrated high diagnostic accuracy for referable DR detection in a community-based screening setting. This technology shows promise for expanding DR-screening coverage in resource-limited settings.</div></div>","PeriodicalId":39387,"journal":{"name":"Medical Journal Armed Forces India","volume":"81 6","pages":"Pages 665-671"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence in simulation-based training for Health Professions Education: Navigating the rabbit hole 人工智能在卫生专业教育模拟培训中的应用：探索兔子洞

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2025-09-02 DOI: 10.1016/j.mjafi.2025.08.010

Rakhi Negi , Deepti Chopra , Komal Maheshwari , Anushi Mahajan , Dinesh Badyal , Padmini Venkataramani

{"title":"Artificial intelligence in simulation-based training for Health Professions Education: Navigating the rabbit hole","authors":"Rakhi Negi , Deepti Chopra , Komal Maheshwari , Anushi Mahajan , Dinesh Badyal , Padmini Venkataramani","doi":"10.1016/j.mjafi.2025.08.010","DOIUrl":"10.1016/j.mjafi.2025.08.010","url":null,"abstract":"<div><div>Simulation-based training (SBT) for health professions education has seen an evolution from low-fidelity trainers to technology-integrated high-fidelity trainers, which has opened doors to newer and promising prospects of integration of artificial intelligence (AI) into SBT. This review provides insights into the use of AI to augment and transform various elements of SBT like complex scenario designing, realism, feedback, student engagement, etc. This is exemplified through the successful application of AI in various SBT platforms, which have increased the efficacy of simulations. However, several challenges and barriers have been perceived in the use of AI in SBT, which include bias in AI algorithms originating due to skewed training datasets leading to inaccurate decisions, errors due to black box, cost factors, need for constant update, and ethical, legal, and cultural issues. Despite these challenges, the railroads of AI are fast-tracking with increased interest and collaborative ventures between various stakeholders like healthcare professionals, educators, technological experts, and policymakers. This article attempts to provide a comprehensive overview of the role of AI in SBT, challenges, and the way forward to amalgamating it with SBT in an optimal manner.</div></div>","PeriodicalId":39387,"journal":{"name":"Medical Journal Armed Forces India","volume":"81 6","pages":"Pages 637-643"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence driven diagnostic model for detecting paranasal sinus opacification in computed tomography images: Development and evaluation 人工智能驱动的诊断模型用于检测计算机断层图像中的鼻窦混浊：开发和评估

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2025-04-07 DOI: 10.1016/j.mjafi.2025.02.007

Anubhav Singh , Kamal Deep Joshi , Sachin Girdhar , Dharamendra Kumar Singh , Rakesh Datta , Abhipsa Hota , Poonam Raj , Suraj Thapa

{"title":"Artificial intelligence driven diagnostic model for detecting paranasal sinus opacification in computed tomography images: Development and evaluation","authors":"Anubhav Singh , Kamal Deep Joshi , Sachin Girdhar , Dharamendra Kumar Singh , Rakesh Datta , Abhipsa Hota , Poonam Raj , Suraj Thapa","doi":"10.1016/j.mjafi.2025.02.007","DOIUrl":"10.1016/j.mjafi.2025.02.007","url":null,"abstract":"<div><h3>Background</h3><div><span>Visual analysis of paranasal sinuses (PNS) on computed </span>tomography (CT) images requires interpretation and reporting of sinus involvement and other anatomical factors. This is time consuming, labour intensive and subjective. Artificial intelligence (AI)–based machine learning (ML) tools are under development for analysis of radiological images. The scope of this study was to develop and evaluate a coding–free ML model for automated identification of PNS on CT images.</div></div><div><h3>Methods</h3><div>A total of 19,119 anonymous coronal images retrieved from 90 CT studies were included. All images were annotated with locations, names and opacification status of the sinuses. The images were divided into training, validation and testing datasets. The ML model was trained for 2000 iterations using YOLOv2 algorithm, and its accuracy was evaluated using F1 score and Intersection over Union (IoU) metrics.</div></div><div><h3>Results</h3><div><span>An ML model was developed using “Create ML” application on an Apple MacBook computer. A mean F1 score of 0.89 and a mean IoU50 of 79% was achieved during evaluation of the model on the testing dataset. The highest accuracy was seen in the detection of normal sphenoid sinus, and the lowest in the detection of opacified </span>frontal sinus.</div></div><div><h3>Conclusion</h3><div>The study demonstrates the utility of AI and ML in automating the interpretation of PNS CT images. From the results of our study, it can be concluded that a coding–free ML model can be developed and deployed for automated identification of PNS on CT images with accuracy similar to custom–coded ML models.</div></div>","PeriodicalId":39387,"journal":{"name":"Medical Journal Armed Forces India","volume":"81 6","pages":"Pages 649-657"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large urinary bladder leiomyoma mimicking malignancy: Using diffusion-weighted imaging as a problem solving tool 模拟恶性肿瘤的大膀胱平滑肌瘤：使用扩散加权成像作为解决问题的工具

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2023-08-14 DOI: 10.1016/j.mjafi.2023.06.004

Pooja Pande , Satyendra N. Singh , M.V. Manikandan

引用次数: 0

To study the efficacy of case based learning on prescription practices in post graduate students on geriatric patients attending medical OPD 目的：研究基于案例学习的研究生处方实践对老年门诊患者的影响

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2024-05-27 DOI: 10.1016/j.mjafi.2024.04.005

Vivek Aggarwal , Pradeep Behal , Vishal Sharma , A.K. Yadav , Uday Yanamandra

{"title":"To study the efficacy of case based learning on prescription practices in post graduate students on geriatric patients attending medical OPD","authors":"Vivek Aggarwal , Pradeep Behal , Vishal Sharma , A.K. Yadav , Uday Yanamandra","doi":"10.1016/j.mjafi.2024.04.005","DOIUrl":"10.1016/j.mjafi.2024.04.005","url":null,"abstract":"<div><h3>Background</h3><div>Rationalizing drugs and deprescribing potentially inappropriate medicines in elderly patients is a major challenge faced by doctors in today’s era due to the ever-increasing number of diseases, drugs, changing guidelines, and indications. The objective of this study was to study the efficacy of case-based learning on prescription practices in postgraduate students on geriatric patients attending medical outpatient department (OPD).</div></div><div><h3>Methods</h3><div>Observation cross-sectional study done on postgraduate medicine residents working in medicine/geriatric OPD. Prescriptions were analyzed for quality by prescription quality index (PQI) score. The baseline PQI score was generated. An interactive workshop and training session on case-based learning on appropriate prescribing for the elderly was conducted. The prescribing habits and PQI scores were reassessed and compared to assess the change in PQI scores along with satisfaction levels and faculty feedback.</div></div><div><h3>Results</h3><div>A total of 60 prescriptions from 24 medicine residents were initially assessed for baseline PQI. A total of 41 residents participated in the workshop. Sixty fresh prescriptions were reassessed after one month of the workshop. The mean baseline PQI was 27.15 (±3.70) which increased to 31.81 (±3.60) [p < 0.001. The majority of the faculty (10/12) felt improvement in prescribing practices. Two third of residents (28/41) had a very good and excellent level of satisfaction with the case-based learning.</div></div><div><h3>Conclusion</h3><div>Case-based learning is an effective tool for enhancing the prescribing skills of postgraduates, especially in geriatric patients. There was a significant improvement in PQI score after the case-based learning workshop in the prescriptions of the postgraduate students with a p-value of < 0.001.</div></div>","PeriodicalId":39387,"journal":{"name":"Medical Journal Armed Forces India","volume":"81 6","pages":"Pages 723-727"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enumeration of Ki67 proliferation index by an algorithm-based digital image analysis in breast carcinoma 基于算法的乳腺癌数字图像分析中Ki67增殖指数的计数

Medical Journal Armed Forces India Pub Date : 2025-11-01 Epub Date: 2025-09-01 DOI: 10.1016/j.mjafi.2025.07.008

Ankit Dhaka , Jeevantika Rana , Brajesh Singh , Manoj Gopal Madakshira

{"title":"Enumeration of Ki67 proliferation index by an algorithm-based digital image analysis in breast carcinoma","authors":"Ankit Dhaka , Jeevantika Rana , Brajesh Singh , Manoj Gopal Madakshira","doi":"10.1016/j.mjafi.2025.07.008","DOIUrl":"10.1016/j.mjafi.2025.07.008","url":null,"abstract":"<div><h3>Background</h3><div>Ki67 is a prognostic factor in breast carcinoma (BC). We evaluate algorithm-based digital image analysis for Ki67 proliferation index (KPI).</div></div><div><h3>Methods</h3><div>A retrospective study was conducted on 81 BC cases. KPI (by eyeballing, EB) and tubule scores were obtained from records. A region of interest within the hotspot on the scanned slide was annotated and analysed using ImageJ (Reference standard) and Optrascan algorithm (OA). Statistical analysis included Pearson correlation (PC) included Pearson Correlation (PC) and Bland - Altman plots (BAP).</div></div><div><h3>Results</h3><div>EB and OA correlated with ImageJ (p < 0.001). OA had a stronger correlation (r: 0.9913) than EB. BAP revealed larger bias with EB (bias: 1.309) than OA. EB showed significant bias in tumours with >10% tubule formation (bias: 7.268) compared with OA.</div></div><div><h3>Conclusion</h3><div>OA is a reasonable alternative for KPI. EB tends to overestimate in solid tumours and underestimate in differentiated tumours. OA overestimates with increased nuclear pleomorphism and underestimates in increased intra-tumoural lymphocytes or fibroblasts.</div></div>","PeriodicalId":39387,"journal":{"name":"Medical Journal Armed Forces India","volume":"81 6","pages":"Pages 707-713"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0