Frontiers in Artificial Intelligence最新文献

{"title":"AI in business operations: driving urban growth and societal sustainability.","authors":"Sharareh Shahidi Hamedani, Sarfraz Aslam, Shervin Shahidi Hamedani","doi":"10.3389/frai.2025.1568210","DOIUrl":"https://doi.org/10.3389/frai.2025.1568210","url":null,"abstract":"","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1568210"},"PeriodicalIF":3.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11973334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804429","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":"Traditional vs. AI-generated meteorological risks for emergency predictions.","authors":"Naoufal Sirri, Christophe Guyeux","doi":"10.3389/frai.2025.1545851","DOIUrl":"https://doi.org/10.3389/frai.2025.1545851","url":null,"abstract":"<p><p>This study aims to analyze and examine in-depth the feature selection process using Large Language Models (LLMs) to optimize firefighter prediction performance. Although features from reliable sources are known to significantly aid predictions, their accuracy may be limited in critical situations requiring rigorous prioritization. Therefore, the focus was placed on meteorological risks for a comparative diagnosis between their extraction from Météo France and those generated by LLMs across various dimensions. Given the crucial role of meteorological risks as key informational sources for decision-making, this study explores the impact of feature extraction methods related to these risks on predicting firefighter interventions over nine years, from 2015 to 2024. Annual reports on firefighter activities in France highlight the growing influence of weather-related risks, underscoring the urgent need for precise and actionable meteorological information to support rapid and effective emergency response strategies. The methodology implemented involved comprehensive data preparation, an in-depth analysis of feature extraction through different approaches, and their evaluation from multiple perspectives. This required leveraging machine learning models such as XGBoost, Random Forest, and Support Vector Machines (SVM) to assess and analyze prediction results based on two feature spaces: F1 (including general features and meteorological risks extracted from Météo France) and F2 (including general features and meteorological risks generated by LLMs). The results revealed that models trained with the F2 feature space consistently demonstrated superior performance. Notably, annual improvements were observed, particularly for high and very high intervention activities. However, the use of the F2 space proved less effective for low intervention activities and underperformed compared to F1 during the summer season. In conclusion, this work presents a concrete methodology for forecasting and enhancing resource management, accelerating firefighter response times, and ultimately contributing to life preservation by reducing the risk of failure during critical incidents.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1545851"},"PeriodicalIF":3.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11973294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804433","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":"Data augmentation via diffusion model to enhance AI fairness.","authors":"Christina Hastings Blow, Lijun Qian, Camille Gibson, Pamela Obiomon, Xishuang Dong","doi":"10.3389/frai.2025.1530397","DOIUrl":"https://doi.org/10.3389/frai.2025.1530397","url":null,"abstract":"<p><strong>Introduction: </strong>AI fairness seeks to improve the transparency and explainability of AI systems by ensuring that their outcomes genuinely reflect the best interests of users. Data augmentation, which involves generating synthetic data from existing datasets, has gained significant attention as a solution to data scarcity. In particular, diffusion models have become a powerful technique for generating synthetic data, especially in fields like computer vision.</p><p><strong>Methods: </strong>This paper explores the potential of diffusion models to generate synthetic tabular data to improve AI fairness. The Tabular Denoising Diffusion Probabilistic Model (Tab-DDPM), a diffusion model adaptable to any tabular dataset and capable of handling various feature types, was utilized with different amounts of generated data for data augmentation. Additionally, reweighting samples from AIF360 was employed to further enhance AI fairness. Five traditional machine learning models-Decision Tree (DT), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)-were used to validate the proposed approach.</p><p><strong>Results and discussion: </strong>Experimental results demonstrate that the synthetic data generated by Tab-DDPM improves fairness in binary classification.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1530397"},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774511","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":"Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea).","authors":"Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo","doi":"10.3389/frai.2025.1446876","DOIUrl":"10.3389/frai.2025.1446876","url":null,"abstract":"<p><strong>Background: </strong>Viral load (VL) suppression is key to ending the global HIV epidemic, and predicting it is critical for healthcare providers and people living with HIV (PLHIV). Traditional research has focused on statistical analysis, but machine learning (ML) is gradually influencing HIV clinical care. While ML has been used in various settings, there's a lack of research supporting antiretroviral therapy (ART) programs, especially in resource-limited settings like Guinea. This study aims to identify the most predictive variables of VL suppression and develop ML models for PLHIV in Conakry (Guinea).</p><p><strong>Methods: </strong>Anonymized data from HIV patients in eight Conakry health facilities were pre-processed, including variable recoding, record removal, missing value imputation, grouping small categories, creating dummy variables, and oversampling the smallest target class. Support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and four stacked models were developed. Optimal parameters were determined through two cross-validation loops using a grid search approach. Sensitivity, specificity, predictive positive value (PPV), predictive negative value (PNV), <i>F</i>-score, and area under the curve (AUC) were computed on unseen data to assess model performance. RF was used to determine the most predictive variables.</p><p><strong>Results: </strong>RF (94% <i>F</i>-score, 82% AUC) and NB (89% <i>F</i>-score, 82% AUC) were the most optimal models to detect VL suppression and non-suppression when applied to unseen data. The optimal parameters for RF were 1,000 estimators and no maximum depth (Random state = 40), and it identified Regimen schedule_6-Month, Duration on ART (months), Last ART CD4, Regimen schedule_Regular, and Last Pre-ART CD4 as top predictors for VL suppression.</p><p><strong>Conclusion: </strong>This study demonstrated the capability to predict VL suppression but has some limitations. The results are dependent on the quality of the data and are specific to the Guinea context and thus, there may be limitations with generalizability. Future studies may be to conduct a similar study in a different context and develop the most optimal model into an application that can be tested in a clinical context.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1446876"},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143773440","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":"Testing an inverse modeling approach with gradient boosting regression for stroke volume estimation using patient thermodilution data.","authors":"Vasiliki Vicky Bikia, Dionysios Adamopoulos, Marco Roffi, Georgios Rovas, Stéphane Noble, François Mach, Nikolaos Stergiopulos","doi":"10.3389/frai.2025.1530453","DOIUrl":"10.3389/frai.2025.1530453","url":null,"abstract":"<p><p>Stroke volume (SV) is a major indicator of cardiovascular function, providing essential information about heart performance and blood flow adequacy. Accurate SV measurement is particularly important for assessing patients with heart failure, managing patients undergoing major surgeries, and delivering optimal care in critical settings. Traditional methods for estimating SV, such as thermodilution, are invasive and unsuitable for routine diagnostics. Non-invasive techniques, although safer and more accessible, often lack the precision and user-friendliness needed for continuous bedside monitoring. We developed a modified method for SV estimation that combines a validated 1-D model of the systemic circulation with machine learning. Our approach replaces the traditional optimization process developed in our previous work, with a regression method, utilizing an in silico-generated dataset of various hemodynamic profiles to create a gradient boosting regression-enabled SV estimator. This dataset accurately mimics the dynamic characteristics of the 1-D model, allowing for precise SV predictions without resource-intensive parameter adjustments. We evaluated our method against SV values derived from the gold standard thermodilution method in 24 patients. The results demonstrated that our approach provides a satisfactory agreement between the predicted and reference data, with a MAE of 16 mL, a normalized RMSE of 21%, a bias of -9.2 mL, and limits of agreement (LoA) of [-47, 28] mL. A correlation coefficient of <i>r</i> = 0.7 (<i>p</i> < 0.05) was reported, with the predicted SV slightly underestimated (68 ± 23 mL) in comparison to the reference SV (77 ± 26 mL). The significant reduction in computational time of our method for SV assessment should make it suitable for real-time clinical applications.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1530453"},"PeriodicalIF":3.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11959070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765007","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":"Accurate V2X traffic prediction with deep learning architectures.","authors":"Ali R Abdellah, Ahmed Abdelmoaty, Abdelhamied A Ateya, Ahmed A Abd El-Latif, Ammar Muthanna, Andrey Koucheryavy","doi":"10.3389/frai.2025.1565287","DOIUrl":"10.3389/frai.2025.1565287","url":null,"abstract":"<p><p>Vehicle-to-Everything (V2X) communication promises to revolutionize road safety and efficiency. However, challenges in data sharing and network reliability impede its full realization. This paper addresses these challenges by proposing a novel Deep Learning (DL) approach for traffic prediction in V2X environments. We employ Bidirectional Long Short-Term Memory (BiLSTM) networks and compare their performance against other prominent DL architectures, including unidirectional LSTM and Gated Recurrent Unit (GRU). Our findings demonstrate that the BiLSTM model exhibits superior accuracy in predicting traffic patterns. This enhanced prediction capability enables more efficient resource allocation, improved network performance, and enhanced safety for all road users, reducing fuel consumption, decreased emissions, and a more sustainable transportation system.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1565287"},"PeriodicalIF":3.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11962783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774506","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":"Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental.","authors":"Roberto Balestri","doi":"10.3389/frai.2025.1558696","DOIUrl":"10.3389/frai.2025.1558696","url":null,"abstract":"<p><p>This study evaluates the biases in Gemini 2.0 Flash Experimental, a state-of-the-art large language model (LLM) developed by Google, focusing on content moderation and gender disparities. By comparing its performance to ChatGPT-4o, examined in a previous work of the author, the analysis highlights some differences in ethical moderation practices. Gemini 2.0 demonstrates reduced gender bias, notably with female-specific prompts achieving a substantial rise in acceptance rates compared to results obtained by ChatGPT-4o. It adopts a more permissive stance toward sexual content and maintains relatively high acceptance rates for violent prompts (including gender-specific cases). Despite these changes, whether they constitute an improvement is debatable. While gender bias has been reduced, this reduction comes at the cost of permitting more violent content toward both males and females, potentially normalizing violence rather than mitigating harm. Male-specific prompts still generally receive higher acceptance rates than female-specific ones. These findings underscore the complexities of aligning AI systems with ethical standards, highlighting progress in reducing certain biases while raising concerns about the broader implications of the model's permissiveness. Ongoing refinements are essential to achieve moderation practices that ensure transparency, fairness, and inclusivity without amplifying harmful content.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1558696"},"PeriodicalIF":3.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11958708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765040","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":"Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease.","authors":"Hsin-Yueh Liang, Kai-Cheng Hsu, Shang-Yu Chien, Chen-Yu Yeh, Ting-Hsuan Sun, Meng-Hsuan Liu, Kee Koon Ng","doi":"10.3389/frai.2025.1496109","DOIUrl":"10.3389/frai.2025.1496109","url":null,"abstract":"<p><strong>Background: </strong>The diagnostic power of exercise stress electrocardiography (ExECG) remains limited. We aimed to construct an artificial intelligence (AI)-based method to enhance ExECG performance to identify patients with significant coronary artery disease (CAD).</p><p><strong>Methods: </strong>We retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months. The mean age was 57.0 ± 10.1 years, and 614 (75%) were male patients. Significant coronary artery disease was seen in 369 (43.8%) CAG reports. We also included 197 individuals with normal ExECG and low risk of CAD. A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. We also investigated the optimal number of inputted ECG signal slices and features and the weighting of features for model performance.</p><p><strong>Results: </strong>Using the data of patients undergoing CAG for training and test sets, our algorithm had an area under the curve, sensitivity, and specificity of 0.74, 0.86, and 0.47, respectively, which increased to 0.83, 0.89, and 0.60, respectively, after enrolling 197 subjects with low risk of CAD. Three ECG signal slices and 12 features yielded optimal performance metrics. The principal predictive feature variables were sex, maximum heart rate, and ST/HR index. Our model generated results within one minute after completing ExECG.</p><p><strong>Conclusion: </strong>The multimodal AI algorithm, leveraging deep learning techniques, efficiently and accurately identifies patients with significant CAD using ExECG data, aiding clinical screening in both symptomatic and asymptomatic patients. Nevertheless, the specificity remains moderate (0.60), suggesting a potential for false positives and highlighting the need for further investigation.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1496109"},"PeriodicalIF":3.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754829","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":"LLM services in the management of social communications.","authors":"Yuriy Dyachenko, Oleksandra Humenna, Oleg Soloviov, Inna Skarga-Bandurova, Nayden Nenkov","doi":"10.3389/frai.2025.1474017","DOIUrl":"10.3389/frai.2025.1474017","url":null,"abstract":"<p><p>This paper proposes enhancing social communication management with a behavioral economics approach through artificial intelligence instruments. The research aims to explore the influence of social communication on citizens' behavior using large language model services and assess its effectiveness. The paper builds on Daniel Kahneman's dual-process theory, highlighting the intuitive system (System 1) and the rational system (System 2) in decision-making. The author introduces a third system, System 3, representing rooted in identity socially conditioned behavior influenced by societal norms and self-awareness. On this theoretical basis, the paper emphasizes automating communication management through large language model services, freeing up citizens' potential for self-determination and self-organization. By leveraging these services, messages can be crafted to support social transformation while respecting historical, cultural, and political contexts. Based on the preconditions and restrictions described above, we use GPT-4 model to generate messages based on these narratives. The experiment will use an observational study design with virtual persons. To compare the impact of original and modified messages according to the addressee's mentality, we used the Claude 3.5 Sonnet model. We can see that the potential activity of respondents after perceiving the changed message does not change much, and the original message is perceived. Modifying messages by LLM services crafted to support social transformation while respecting historical, cultural, and political contexts cause attitudes to become substantially more negative (2.5 units downward shift in median); the intentions showed a slight positive increase (0.2 units upward change in median).</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1474017"},"PeriodicalIF":3.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143731926","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":"Applied machine learning in intelligent systems: knowledge graph-enhanced ophthalmic contrastive learning with \"clinical profile\" prompts.","authors":"Mini Han Wang, Jiazheng Cui, Simon Ming-Yuen Lee, Zhiyuan Lin, Peijin Zeng, Xinyue Li, Haoyang Liu, Yunxiao Liu, Yang Xu, Yapeng Wang, José Lopes Camilo Da Costa Alves, Guanghui Hou, Junbin Fang, Xiangrong Yu, Kelvin Kam-Lung Chong, Yi Pan","doi":"10.3389/frai.2025.1527010","DOIUrl":"10.3389/frai.2025.1527010","url":null,"abstract":"<p><strong>Introduction: </strong>The integration of artificial intelligence (AI) into ophthalmic diagnostics has the potential to significantly enhance diagnostic accuracy and interpretability, thereby supporting clinical decision-making. However, a major challenge in AI-driven medical applications is the lack of transparency, which limits clinicians' trust in automated recommendations. This study investigates the application of machine learning techniques by integrating knowledge graphs with contrastive learning and utilizing \"clinical profile\" prompts to refine the performance of the ophthalmology-specific large language model, MeEYE, which is built on the CHATGLM3-6B architecture. This approach aims to improve the model's ability to capture clinically relevant features while enhancing both the accuracy and explainability of diagnostic predictions.</p><p><strong>Methods: </strong>This study employs a novel methodological framework that incorporates domain-specific knowledge through knowledge graphs and enhances feature representation using contrastive learning. The MeEYE model is fine-tuned with structured clinical knowledge, enabling it to better distinguish subtle yet significant ophthalmic features. Additionally, \"clinical profile\" prompts are incorporated to further improve contextual understanding and diagnostic precision. The proposed method is evaluated through comprehensive performance benchmarking, including quantitative assessments and clinical case studies, to ensure its efficacy in real-world ophthalmic diagnosis.</p><p><strong>Results: </strong>The experimental findings demonstrate that integrating knowledge graphs and contrastive learning into the MeEYE model significantly improves both diagnostic accuracy and model interpretability. Comparative analyses against baseline models reveal that the proposed approach enhances the identification of ophthalmic conditions with higher precision and clarity. Furthermore, the model's ability to generate transparent and clinically relevant AI recommendations is substantiated through rigorous evaluation, highlighting its potential for real-world clinical implementation.</p><p><strong>Discussion: </strong>The results underscore the importance of explainable AI in medical diagnostics, particularly in ophthalmology, where model transparency is critical for clinical acceptance and utility. By incorporating domain-specific knowledge with advanced machine learning techniques, the proposed approach not only enhances model performance but also ensures that AI-generated insights are interpretable and reliable for clinical decision-making. These findings suggest that integrating structured medical knowledge with machine learning frameworks can address key challenges in AI-driven diagnostics, ultimately contributing to improved patient outcomes. Future research should explore the adaptability of this approach across various medical domains to further advance AI-assisted diagnostic systems.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1527010"},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754826","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}