Technology and Health Care最新文献

{"title":"Uncovering potential molecular markers and pathological mechanisms of Parkinson's disease and myocardial infarction based on bioinformatics analysis.","authors":"Jian Liu, Lu Xing, Tianye Lan, Qiang Wang, Yitong Wang, Xuenan Chen, Weimin Zhao, Liwei Sun","doi":"10.1177/09287329241307805","DOIUrl":"10.1177/09287329241307805","url":null,"abstract":"<p><p>BackgroundThe direct association between Parkinson's disease (PD) and Myocardial infarction (MI) has been the subject of relatively limited research.ObjectiveThe purpose of this study was to identify the genes most associated with PD and MI to explore their common pathogenesis.MethodsThe gene expression profiles of PD and MI were downloaded from GEO database. Differential expression analysis was performed to identify the common differential expression genes (DEGs) of PD and MI, followed by functional annotation. Subsequently, protein-protein interaction network were constructed, and hub DEGs were identified based on CytoHubba plugin and LASSO regression analysis. To explore the potential molecular mechanism of hub DEGs, GSEA analysis, immune correlation analysis, drug prediction and molecular docking were performed, and transcription factors (TF) and lncRNA-miRNA-mRNA (ceRNA) regulatory networks were constructed.ResultsA total of 48 DEGs with the same expression trend were identified in the MI vs. normal control (NC) and PD vs. NC groups. Functional annotation results showed that the common DEGs were significantly enriched in immune and inflammation-related pathways. RPS4Y1 and UTY were the most relevant hub DEGs for PD and MI, and may be involved in the HALLMARK_MYC_TARGETS_V1 and HALLMARK_PROTEIN_SECRETION pathways. TP63 was a common TF of RPS4Y1 and UTY. The PVT1/KCNQ1OT1-hsa-miR-31-5p-RPS4Y1 and KCNQ1OT1-hsa-let-7a-5p/hsa-miR-19b-3p-UTY axes may play an important role in regulating PD and MI. CYCLOHEXIMIDE and ATALAREN may be potential drugs for the treatment of PD and MI comorbidity. In addition, PD and MI exhibit different patterns of immune cell infiltration and immune function status, which may be related to the specific pathological processes of the disease.ConclusionsThis study revealed for the first time that RPS4Y1 and UTY may be common biomarkers of PD and MI and may be potential therapeutic targets. This study provides new perspective on the common molecular mechanisms between PD and MI.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1878-1894"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship of influenza virus to inflammatory factors and immune function in elderly patients with COPD: A retrospective analysis.","authors":"Yanrong Chen, Yun Shi, Liyang Dou, Yan Liu, Jing Zhang","doi":"10.1177/09287329251317307","DOIUrl":"10.1177/09287329251317307","url":null,"abstract":"<p><p>ObjectiveThe aim of this retrospective study was to investigate the relevance of influenza A virus (IAV) in acutely exacerbating airway inflammatory response and disrupting immune function in elderly COPD patients.MethodsThe group conducted a pre-test: using multiplex combined real-time PCR detection kits Multiple real⁃time PCR was used to detect twenty-four pathogens, 385 patients clinically diagnosed with COPD were tested for viral nucleic acid in throat swabs. At the same time, peripheral blood leukapheresis was collected from both groups of patients, and their IL-6, IL-8, IL-1β, and TNF-α levels were detected, along with the levels of T-cell differentiation markers CD4 and CD8, to assess the influence of influenza virus on the immune function of elderly COPD patients and its relevance to the acute exacerbation of airway inflammatory response in elderly COPD patients.ResultsResults showed that the expression of inflammatory cytokines IL-6, IL-8, IL-1β and TNF-α was significantly higher in the viral group compared with the non-infected group (P < 0.05, P < 0.01). The levels of T cell differentiation type markers CD4 and CD8 were significantly lower in the infected group compared with the uninfected group.ConclusionInfluenza virus further exacerbated airway inflammatory response and decreased the immune function of T cells by activating intrinsic immune molecules such as IL-6, IL-8, IL-1β and TNF-α.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1988-1998"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technological integration in timing of endoscopy: Predictive modeling for acute upper gastrointestinal bleeding outcomes.","authors":"Yangde Miao, Ajun Gu, Guang Yu, Binbin Tang","doi":"10.1177/09287329251316050","DOIUrl":"10.1177/09287329251316050","url":null,"abstract":"<p><p>BackgroundTechnological advancements have revolutionized the management of acute upper gastrointestinal bleeding (AUGIB). However, the impact of endoscopic timing on treatment outcomes remains a critical area of exploration.ObjectiveThis study evaluated the role of endoscopic timing in improving treatment outcomes for AUGIB and introduces a predictive model incorporating clinical and technological insights.MethodsA retrospective analysis of AUGIB patients treated between December 2020 and December 2023 was conducted. Logistic regression identified significant predictors of outcomes, and receiver operating characteristic (ROC) analysis evaluated their predictive value. A predictive model was developed based on these findings.ResultsAmong 145 patients, 35 (24.1%) experienced rebleeding. Key predictors included endoscopic timing, active bleeding, shock on admission, and bleeding volume (p < 0.05). The predictive model demonstrated robust performance (C-index: 0.885, 95% CI: 0.810-0.956), emphasizing the clinical relevance of precise timing in endoscopic intervention.ConclusionThis study underscores the importance of integrating technology with clinical practice to optimize endoscopic timing and improve AUGIB outcomes. The predictive model offers a valuable tool for risk stratification and clinical decision-making in modern healthcare settings.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"2026-2033"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ADET MODEL: Real time autism detection via eye tracking model using retinal scan images.","authors":"Jesu Mariyan Beno Ranjana, Rajendran Muthukkumar","doi":"10.1177/09287329241301678","DOIUrl":"10.1177/09287329241301678","url":null,"abstract":"<p><p>BackgroundDeficits in concentration with social stimuli are more common in children affected by autism spectrum disorder (ASD). Developing visual attention is one of the most vital elements for detecting autism. Eye tracking technology is a potential method to identify an early autism biomarker based on children's abnormal visual patterns.ObjectiveEye tracking retinal scan path images can be generated by eyeball movement during the time of watching the screen and capture the eye projection sequences, which helps to analyze the behavior of the children. The Shi-Tomasi corner detection methodology uses open CV to identify the corners of the eye gaze movement in the images.MethodsIn the proposed ADET model, the corner detection-based vision transformer (CD-ViT) technique is utilized to diagnose autism at an early stage. Generally, the transformer model divides the input images into patches, which can be fed into the transformer encoder process. The vision transformer is fine-tuned to resolve binary classification issues once the features are extracted via remora optimization. Specifically, the vision transformer model acts as the cornerstone of the proposed work with the help of the corner detection technique. This study uses a dataset with 547 eye-tracking retinal scan path images for both autism and non-autistic children.ResultsExperimental results show that the suggested ADET frameworkachieves a better classification accuracy of 38.31%, 23.71%, 13.01%, 1.56%, 18.26%, and 44.56% than RM3ASD, MLP, SVM, CNN, SVM, and our proposed ADET methods.ConclusionsThis screening method strongly suggests that it be used to assist medical professionals in providing efficient and accurate autism detection.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1661-1678"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of enhanced recovery after surgery in pediatric patients with obstructive sleep apnea-hypopnea syndrome.","authors":"Xiang Qi, Changjun Yang, Wei Huang, Wei Han, Yujie Li","doi":"10.1177/09287329251314265","DOIUrl":"10.1177/09287329251314265","url":null,"abstract":"<p><p>BackgroundEnhanced Recovery After Surgery (ERAS) has demonstrated effectiveness in accelerating recovery and reducing complications across surgical fields, with limited application in Ear-Nose-Throat surgeries. Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS), a prevalent condition affecting pediatric patients, calls for innovative management due to its impact on health and the need for surgical interventions like tonsillectomy.ObjectiveThe present study aimed to investigate the efficacy of ERAS in pediatric patients with OSAHS.MethodsReview and analyze 1100 cases of pediatric patients with OSAHS who underwent plasma-coblation tonsillectomy and adenoidectomy using nasal endoscopy from June 2016 to June 2022 in our hospital. Among these cases, a total of 564 patients were managed according to ERAS theory, while 536 patients were treated with classical theory. The incidence of preoperative discomfort, postoperative pain, bleeding, and other complications between the two groups were compared.ResultsERAS group showed comparable preoperative-discomfort rates to the control (<i>P </i>= 0.799). However, ERAS patients exhibited significantly lower pain scores at 24-, 48-, and 72-h post-operation (<i>P </i>< 0.05). Mental state scores were similar between ERAS and control 4 h pre-surgery (<i>P </i>> 0.05), but notably lower in ERAS at 30 min pre-op and 6-, 12-, and 24-h post-operation (<i>P </i>< 0.05). ERAS had lower complication rates and intra/postoperative bleeding, quicker ambulation/oral intake, and shorter hospital stays than control (<i>P </i>< 0.05).ConclusionERAS management in patients with OSAHS resulted in notable reductions in postoperative pain and incidence of complications, along with improved postoperative recovery and shorter hospital stays.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1755-1762"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143459996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence based BCI using SSVEP signals with single channel EEG.","authors":"Venkatesh Kanagaluru, Sasikala M","doi":"10.1177/09287329241302740","DOIUrl":"10.1177/09287329241302740","url":null,"abstract":"<p><p>BackgroundBrain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices. Steady-state visual-evoked potentials (SSVEPs) are particularly useful in BCIs because of their rapid communication capabilities and minimal calibration requirements. Although SSVEP-based BCIs are highly effective, traditional classification methods face challenges in maintaining high accuracy with minimal EEG channels, especially in real-world applications. There is a growing need for improved classification techniques to enhance performance and efficiency.ObjectiveThe aim of this research is to improve the classification of SSVEP signals using machine-learning algorithms. This involves extracting dominant frequency features from SSVEP data and applying classifiers such as Decision Tree (DT), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to achieve high accuracy while reducing the number of EEG channels required, making the method practical for BCI applications.MethodsSSVEP data were collected from the Benchmark Dataset at Tsinghua BCI Lab using 64 EEG channels per subject. The Oz channel was selected as the dominant channel for analysis. Wavelet decomposition (db4) was used to extract frequency features in the range 7.8 Hz to 15.6 Hz. The frequency of the maximum amplitude within a 5-s window was extracted as the key feature, and machine learning models (DT, LDA, and SVM) were applied to classify these features.ResultsThe proposed method achieved a high classification accuracy, with 95.8% for DT and 96.7% for both LDA and SVM. These results show significant improvement over existing methods, indicating the potential of this approach for BCI applications.ConclusionThis study demonstrates that SSVEP classification using machine-learning models improves accuracy and efficiency. The use of wavelet decomposition for feature extraction and machine learning for classification offers a robust method for SSVEP-based BCIs. This method is promising for assistive technologies and other BCI applications.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1905-1916"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DABiG: Breath pattern classification using the hybrid deep learning with optimal feature selection.","authors":"P Kaleeswari, R Ramalakshmi, T Arun Prasath, A Muthukumar, R Kottaimalai, M Thanga Raj","doi":"10.1177/09287329241303368","DOIUrl":"10.1177/09287329241303368","url":null,"abstract":"<p><p>BackgroundA person's breathing pattern can be a reflection of their emotional and physical well-being because it shows the frequency, intensity, and rhythm of their breathing.ObjectiveThis research article presents a comprehensive approach to breathe pattern classification utilizing gyroscope and accelerometer readings obtained from individuals using two distinct sensors. The study encompasses the acquisition of six diverse breathing patterns, with a focus on data pre-processing through Min-Max normalization.MethodsTo select essential features from the normalized data, an innovative optimization algorithm, Adaptive Chimp Optimization (AdCO), is introduced. AdCO integrates an adaptive weighting strategy into the conventional Chimp optimization algorithm, enhancing convergence rates and enabling global optimal feature selection. Furthermore, the article introduces the application of the selected features in breath pattern classification using a hybrid deep learning mechanism, DABiG. DABiG leverages the Bidirectional Gated Recurrent Unit (BiGRU), a neural network architecture capable of processing sequential data bi-directionally.ResultsSpatial and temporal attention mechanisms are incorporated into DABiG to enhance its ability to focus on relevant spatial regions and time steps within the breath pattern data.ConclusionSpatial attention assigns weights to spatial regions, while temporal attention assigns weights to time steps, improving feature extraction and classification accuracy.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1612-1625"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technological advancements in right heart acoustic angiography for predicting patent foramen ovale and its correlation with AIS: A retrospective study.","authors":"Haidong Qin, Xipiao Qin, Xianzhi Jiang","doi":"10.1177/09287329241307809","DOIUrl":"10.1177/09287329241307809","url":null,"abstract":"<p><p>BackgroundAcute ischemic stroke (AIS) is a leading cause of disability and mortality worldwide, often resulting from embolic events. Patent foramen ovale (PFO), a common congenital heart defect, has been identified as a potential source of emboli in AIS patients, especially in cases of cryptogenic stroke where no other etiology is found.ObjectiveThis study aimed to investigate the relationship between AIS and PFO, and to explore the diagnostic value of right heart acoustic angiography for PFO.MethodsA study included 148 AIS and 111 non-AIS patients with suspected PFO, using transesophageal echocardiography (TEE) as the diagnostic gold standard. Sensitivity, specificity, and accuracy of right heart acoustic angiography for PFO diagnosis were evaluated. ROC and Spearman correlation analyses assessed diagnostic value and the association between AIS and PFO.ResultsPFO was detected in 79 AIS patients and 20 non-AIS patients, with a higher detection rate in the AIS group (<i>P </i>< 0.05). Right heart acoustic angiography showed a diagnostic accuracy of 95.37% for PFO, with a sensitivity of 95.37% and a specificity of 97.78%. The AUC value of the ROC curve for right heart acoustic imaging was 0.975, indicating high diagnostic efficacy for PFO. PFO was positively correlated with AIS (r = 0.318, <i>P </i>< 0.001).ConclusionPFO is commonly detected in AIS patients and is positively correlated with AIS. Right heart acoustic imaging has high diagnostic efficacy for PFO and can be a valuable diagnostic tool for patients with PFO.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1703-1710"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual reality-induced motion sickness under autonomous vehicle driving conditions: EEG-based recognition and ANOVA analysis of various driving modes.","authors":"Shuyu Shao, Yang Zhang, Hongjue Wang, Xiaoli Fan","doi":"10.1177/09287329241308093","DOIUrl":"10.1177/09287329241308093","url":null,"abstract":"<p><p>Motion sickness induced by autonomous driving technology poses a new challenge to the emerging sustainable transportation systems. This study investigates the association between motion sickness in autonomous driving and electroencephalogram (EEG) signals under three laboratory-based simulated scenarios: manual driving, resting, and autonomous driving. EEG data were recorded from participants in each mode, alongside the collection of motion sickness symptoms through questionnaires. Data analysis and exploration were conducted to explore the relationship between autonomous driving-induced motion sickness and EEG signals. The results indicate a significantly higher probability of motion sickness among passengers in autonomous driving mode than in manual one. Across different driving modes, a correlation was observed between the amplitude and latency of N200 and P300 event-related potentials (ERPs) in the Go/Nogo paradigm, reflecting response inhibition and the occurrence of motion sickness. Temporal analysis of EEG signals revealed significant differences in the Kolmogorov complexity values at Cz, Fz, and Pz channels, suggesting the potential use of EEG-based detection of motion sickness. Frequency domain analysis indicated increased activity in alpha and gamma waves and decreased activity in beta waves following the onset of motion sickness during autonomous driving. Distinct changes were observed in the electrocortical topography of N200 and P300 components in autonomous driving through event-related potential waveforms and topographic maps. These findings provide new insights into the neural mechanisms of motion sickness in autonomous driving and offer guidance for future intervention methods and improvements in the design of autonomous driving systems, thereby promoting their sustainability and safety.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1839-1849"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method framework of cruciate ligaments segmentation and reconstruction from MRI images.","authors":"Ahsan Humayun, Bin Liu, Mustafain Rehman, Zhipeng Zou, Luning Xu","doi":"10.1177/09287329241306201","DOIUrl":"10.1177/09287329241306201","url":null,"abstract":"<p><p>Segmenting anterior and posterior cruciate ligaments (ACL/PCL) presents challenges in medical imaging due to diverse characteristics, including size, shape, and intensity. Our study uses superpixel-based spectral clustering for knee cruciate ligament segmentation in 2D DICOM slices, renowned for generating high-quality clusters. The proposed method addresses the challenges by (i) identifying the ligamentous region (ROI) through superpixel-based computation, (ii) extracting features (intensity-based, shape-based, geometric complexity, and Scale-Invariant Feature Transform) from the ROI, and (iii) segmenting knee ligament tissues using spectral clustering on the extracted features. Superpixel-based spectral clustering addresses the challenge of constructing a dense similarity matrix and significantly reduces the computational burden. Furthermore, 3D visualization of ligament structures is performed using the Visualization Toolkit (VTK). We evaluated our proposed approach on a dataset of knee MRI slices, assessing the results via the dice score, average surface distance (ASD), and root mean squared error (RMSE) metrics. Our method achieved an average dice score of 0.912 for ACL segmentation and 0.896 for PCL segmentation, outperforming other clustering methods. These scores showed an enhancement of 10.7% and 14.9% in segmentation accuracy for the ACL and PCL, respectively. Furthermore, reduced error margins were demonstrated with the mean ASD values of 1.60 and 1.78 and the mean RMSE values of 1.76 and 1.86 for ACL and PCL, respectively. These results show the effectiveness of the proposed method for cruciate ligament segmentation and its potential for increasing the segmentation accuracy and speed, offering significant advantages over manual segmentation by reducing time and expertise.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"1717-1735"},"PeriodicalIF":1.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}