Technology in Cancer Research & Treatment最新文献

{"title":"Clinical Value of Human Endogenous Retrovirus-H Long Terminal Repeat Associating 2 (HHLA2) in Small Cell Lung Cancer","authors":"Xiuqin Zhang, Yan Qin, Xu Chen, Mengrui Xiong, Song Shu","doi":"10.1177/15330338241240683","DOIUrl":"https://doi.org/10.1177/15330338241240683","url":null,"abstract":"Objective: Human endogenous retrovirus-H long terminal repeat associating 2 (HHLA2) is a new immune checkpoint in the B7 family, and the value of HHLA2 in small cell lung cancer (SCLC) is unknown. Methods: We retrospectively detected HHLA2 expression by immunohistochemistry in SCLC patients. Moreover, plasma biomarkers of SCLC were detected retrospectively. Results: Seventy-four percent of SCLC patients exhibited HHLA2 expression. HHLA2 staining was localised within the nucleus of SCLC cells, while no staining was detected in normal lung tissue specimens. The correlation between HHLA2 expression and clinical factors was also analysed. Limited stage (LS) SCLC was more common than extensive stage (ES) SCLC among patients with HHLA2 staining. SCLC patients without metastasis had higher HHLA2 expression than SCLC patients with metastasis. HHLA2 expression was more frequently detected in the group with a tumour size greater than 5 cm than in the group with a tumour size less than 5 cm. The proportion of patients with HHLA2-positive staining was greater in the stage III and IV SCLC groups than in the stage I and II SCLC groups. A high proportion of SCLC patients with HHLA2-positive staining had a survival time <2 years. Neuron-specific enolase (NSE), CEA and Ki-67 levels were measured. The NSE level in the HHLA2-positive group was significantly greater than that in the HHLA2-negative group. The CEA and Ki-67 levels did not significantly differ between the HHLA2-positive and HHLA2-negative patients, nor were age, sex, smoking status, nodal metastasis status, Karnofsky Performance Scale (KPS) score, or Ki-67 expression score. HHLA2-positive SCLC patients had higher tumour stages and shorter 2-year survival times than HHLA2-negative patients did. Conclusion: The new immune molecule HHLA2 may be an ideal clinical biomarker for predicting SCLC progression and could serve as a new immunotherapy target in SCLC.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"94 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583501","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":"Identification of Novel Cuproptosis-Related Genes Mediating the Prognosis and Immune Microenvironment in Cholangiocarcinoma","authors":"Qiang Liu, Jianpeng Zhu, Zhicheng Huang, Xiaofeng Zhang, Jianfeng Yang","doi":"10.1177/15330338241239139","DOIUrl":"https://doi.org/10.1177/15330338241239139","url":null,"abstract":"BackgroundCuproptosis is a novel type of mediated cell death strongly associated with the progression of several cancers and has been implicated as a potential therapeutic target. However, the role of cuproptosis in cholangiocarcinoma for prognostic prediction, subgroup classification, and therapeutic strategies remains largely unknown.MethodsA systematic analysis was conducted among 146 cuproptosis-related genes and clinical information based on independent mRNA and protein datasets to elucidate the potential mechanisms and prognostic prediction value of cuproptosis-related genes. A 10-cuproptosis-related gene prediction model was constructed, and its effects on cholangiocarcinoma prognosis were significantly connected to poor patient survival. Additionally, the expression patterns of our model included genes that were validated with several cholangiocarcinoma cancer cell lines and a normal biliary epithelial cell line.ResultsFirst, a 10-cuproptosis-related gene signature ( ADAM9, ADAM17, ALB, AQP1, CDK1, MT2A, PAM, SOD3, STEAP3, and TMPRSS6) displayed excellent predictive performance for the overall survival of cholangiocarcinoma. The low-cuproptosis group had a significantly better prognosis than the high-cuproptosis group with transcriptome and protein cohorts. Second, compared with the high-risk and low-risk groups, the 2 groups displayed distinct tumor microenvironments, reduced proportions of endothelial cells, and increased levels of cancer-associated fibroblasts based on CIBERSORTx and EPIC analyses. Third, patients’ sensitivities to chemotherapeutic drugs and immune checkpoints revealed distinctive differences between the 2 groups. Finally, in replicating the expression patterns of the 10 genes, these results were validated with quantitative real-time polymerase chain reaction results validating the abnormal expression pattern of the target genes in cholangiocarcinoma.ConclusionsCollectively, we established and verified an effective prognostic model that could separate cholangiocarcinoma patients into 2 heterogeneous cuproptosis subtypes based on the molecular or protein characteristics of 10 cuproptosis-related genes. These findings may provide potential benefits for unveiling molecular characteristics and defining subgroups could improve the early diagnosis and individualized treatment of cholangiocarcinoma patients.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"31 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583516","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":"Breast Cancer Prediction Based on Multiple Machine Learning Algorithms","authors":"Sheng Zhou, Chujiao Hu, Shanshan Wei, Xiaofan Yan","doi":"10.1177/15330338241234791","DOIUrl":"https://doi.org/10.1177/15330338241234791","url":null,"abstract":"IntroductionThe incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community.ObjectivesThis study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant.MethodsOur research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm.ResultsThe results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques.ConclusionThe constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583514","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 Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy","authors":"Zhe Wu, Mujun Liu, Ya Pang, Lihua Deng, Yi Yang, Yi Wu","doi":"10.1177/15330338241242654","DOIUrl":"https://doi.org/10.1177/15330338241242654","url":null,"abstract":"Purpose: Deep learning (DL) is widely used in dose prediction for radiation oncology, multiple DL techniques comparison is often lacking in the literature. To compare the performance of 4 state-of-the-art DL models in predicting the voxel-level dose distribution for cervical cancer volumetric modulated arc therapy (VMAT). Methods and Materials: A total of 261 patients’ plans for cervical cancer were retrieved in this retrospective study. A three-channel feature map, consisting of a planning target volume (PTV) mask, organs at risk (OARs) mask, and CT image was fed into the three-dimensional (3D) U-Net and its 3 variants models. The data set was randomly divided into 80% as training-validation and 20% as testing set, respectively. The model performance was evaluated on the 52 testing patients by comparing the generated dose distributions against the clinical approved ground truth (GT) using mean absolute error (MAE), dose map difference (GT-predicted), clinical dosimetric indices, and dice similarity coefficients (DSC). Results: The 3D U-Net and its 3 variants DL models exhibited promising performance with a maximum MAE within the PTV 0.83% ± 0.67% in the UNETR model. The maximum MAE among the OARs is the left femoral head, which reached 6.95% ± 6.55%. For the body, the maximum MAE was observed in UNETR, which is 1.19 ± 0.86%, and the minimum MAE was 0.94 ± 0.85% for 3D U-Net. The average error of the Dmean difference for different OARs is within 2.5 Gy. The average error of V40 difference for the bladder and rectum is about 5%. The mean DSC under different isodose volumes was above 90%. Conclusions: DL models can predict the voxel-level dose distribution accurately for cervical cancer VMAT treatment plans. All models demonstrated almost analogous performance for voxel-wise dose prediction maps. Considering all voxels within the body, 3D U-Net showed the best performance. The state-of-the-art DL models are of great significance for further clinical applications of cervical cancer VMAT.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"47 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583505","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":"Endometrial Cancer Detection by DNA Methylation Analysis in Cervical Papanicolaou Brush Samples","authors":"San-feng Wang, Chong-yang Du, Mi Li, Bin Wen, Qing-jun Shen, Fang Ma, Liang Zhang, Hua Deng","doi":"10.1177/15330338241242637","DOIUrl":"https://doi.org/10.1177/15330338241242637","url":null,"abstract":"Background: Endometrial cancer (EC) is the leading gynecological cancer worldwide, yet current EC screening approaches are not satisfying. The purpose of this retrospective study was to evaluate the feasibility and capability of DNA methylation analysis in cervical Papanicolaou (Pap) brush samples for EC detection. Methods: We used quantitative methylation-sensitive PCR (qMS-PCR) to determine the methylation status of candidate genes in EC tissue samples, as well as cervical Pap brushes. The ability of RASSF1A and HIST1H4F to serve as diagnostic markers for EC was then examined in cervical Pap brush samples from women with endometrial lesions of varying degrees of severity. Results: Methylated RASSF1A and HIST1H4F were found in EC tissues. Further, methylation of the two genes was also observed in cervical Pap smear samples from EC patients. Methylation levels of RASSF1A and HIST1H4F increased as endometrial lesions progressed, and cervical Pap brush samples from women affected by EC exhibited significantly higher levels of methylated RASSF1A and HIST1H4F compared to noncancerous controls ( P < .001). Receiver operating characteristic (ROC) curves and area under the curve (AUC) analyses revealed RASSF1A and HIST1H4F methylation with a combined AUC of 0.938 and 0.951 for EC/pre-EC detection in cervical Pap brush samples, respectively. Conclusion: These findings demonstrate that DNA methylation analysis in cervical Pap brush samples may be helpful for EC detection, broadening the scope of the commonly used cytological screening. Our proof-of-concept study provides new insights into the field of clinical EC diagnosis.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"121 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583412","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":"Blind Spots in Development of Nanomedicines","authors":"Bhagyashree V. Salvi, Maithali Kantak, Kalyani Kharangate, Francesco Trotta, Timothy Maher, Pravin Shende","doi":"10.1177/15330338241245342","DOIUrl":"https://doi.org/10.1177/15330338241245342","url":null,"abstract":"The field of nanomedicine demonstrates immense advantages and noteworthy expansion compared to conventional drug delivery systems like tablet, capsules, etc. Despite the innumerable advantages, it holds certain shortcomings in the form of blind spots that need to be assessed before the successful clinical translation. This perspective highlights the foremost blind spots in nanomedicine and emphasizes the challenges faced before the entry into the market, including the need for provision of safety and efficacy data by the regulatory agencies like FDA. The significant revolution of nanomedicine in the human life, particularly in patient well-being, necessitates to identify the blind spots and overcome them for effective management and treatment of ailments.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"20 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583502","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":"Combined OLA1 and CLEC3B Gene Is a Prognostic Signature for Hepatocellular Carcinoma and Impact Tumor Progression","authors":"Zhoufeng Chen, Liuwei Zeng, Zhuoyan Chen, Jun Xu, Xiangting Zhang, Huiya Ying, Yuan Zeng, Fujun Yu","doi":"10.1177/15330338241241935","DOIUrl":"https://doi.org/10.1177/15330338241241935","url":null,"abstract":"Hepatocellular carcinoma (HCC), partly because of its complexity and high heterogeneity, has a poor prognosis and an extremely high mortality rate. In this study, mRNA sequencing expression profiles and relevant clinical data of HCC patients were gathered from different public databases. Kaplan–Meier survival curves as well as ROC curves validated that OLA1|CLEC3B was an independent predictor with better predictive capability of HCC prognosis compared to OLA1 and CLEC3B separately. Further, the cell transfection experiment verified that knockdown of OLA1 inhibited cell proliferation, facilitated apoptosis, and improved sensitivity of HCC cells to gemcitabine. In this study, the prognostic model of HCC composed of OLA1/CLEC3B genes was constructed and verified, and the prediction ability was favorable. A higher level of OLA1 along with a lower level of CEC3B is a sign of poor prognosis in HCC. We revealed a novel gene pair OLA1|CLEC3B overexpressed in HCC patients, which may serve as a promising independent predictor of HCC survival and an approach for innovative diagnostic and therapeutic strategies.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"437 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602042","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":"Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics.","authors":"Jinghong Pei, Jing Yu, Ping Ge, Liman Bao, Haowen Pang, Huaiwen Zhang","doi":"10.1177/15330338241298554","DOIUrl":"10.1177/15330338241298554","url":null,"abstract":"<p><p>This study aimed to develop an automated classification framework for distinguishing between cervical cancer tumor and normal uterine tissue, leveraging CT images for radiomics feature extraction. We retrospectively analyzed CT images from 117 cervical cancer patients. To distinguish between cancerous and healthy tissue, we segmented gross tumor volume and normal uterine tissue as distinct regions of interest (ROIs) using manual segmentation techniques. Key radiomic parameters were extracted from these ROIs. To bolster model's predictive capability, the data was stratified into train data (70%) and validation data (30%). During feature selection phase, we applied Least Absolute Shrinkage and Selection Operator regression algorithm to identify most relevant features. Subsequently, we built classification models using five state-of-the-art machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT). Ultimately, the performance of each model was evaluated. Through stringent feature selection process, we identified 18 pivotal radiomic features for classification of cervical cancer and normal uterine tissue. When applied to test data, all five models achieved excellent performance, with area under the curve (AUC) values ranging from 0.8866 to 0.9190 (SVM: 0.9144, RF: 0.9078, KNN: 0.9051, DT: 0.8866, XGBoost: 0.9190), all surpassing threshold of 0.8. In terms of test data, all five models had high sensitivity; accuracy of SVM, RF, and XGBoost models was comparable; and specificity of five models was similar. XGBoost model outperformed the others in terms of diagnostic accuracy, achieving an AUC of 0.8737 (95% CI: 0.8198-0.9277) for train data and 0.9190 (95% CI: 0.8525-0.9854) for test data. Our findings underscore the potential of CT radiomics combined with machine learning algorithms for accurately classifying cervical cancer tumors and normal uterine tissue with high recognition capabilities. This approach holds significant promise for clinical diagnostics.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241298554"},"PeriodicalIF":2.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11562001/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to \"Integrating Therapeutic Ultrasound With Nanosized Drug Delivery Systems in the Battle Against Cancer\".","authors":"","doi":"10.1177/15330338231223384","DOIUrl":"10.1177/15330338231223384","url":null,"abstract":"","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338231223384"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10860455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139724032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decanoylcarnitine Inhibits Triple-Negative Breast Cancer Progression via Mmp9 in an Intermittent Fasting Obesity Mouse.","authors":"Yifan Tang, Shuai Chen, Saijun Wang, Ke Xu, Kun Zhang, Dongmei Wang, Ninghan Feng","doi":"10.1177/15330338241233443","DOIUrl":"10.1177/15330338241233443","url":null,"abstract":"<p><p><b>Purpose:</b> Treatment of triple-negative breast cancer (TNBC) remains challenging. Intermittent fasting (IF) has emerged as a promising approach to improve metabolic health of various metabolic disorders. Clinical studies indicate IF is essential for TNBC progression. However, the molecular mechanisms underlying metabolic remodeling in regulating IF and TNBC progression are still unclear. <b>Methods:</b> In this study, we utilized a robust mouse model of TNBC and exposed subjects to a high-fat diet (HFD) with IF to explore its impact on the metabolic reprogramming linked to cancer progression. To identify crucial serum metabolites and signaling events, we utilized targeted metabolomics and RNA sequencing (RNA-seq). Furthermore, we conducted immunoblotting, real-time quantitative polymerase chain reaction (RT-qPCR), cell migration assays, lentivirus-mediated Mmp9 overexpression, and Mmp9 inhibitor experiments to elucidate the role of decanoylcarnitine/Mmp9 in TNBC cell migration. <b>Results:</b> Our observations indicate that IF exerts notable inhibitory effects on both the proliferation and cancer metastasis. Utilizing targeted metabolomics and RNA-seq, we initially identified pivotal serum metabolites and signaling events in the progression of TNBC. Among the 349 serum metabolites identified, decanoylcarnitine was picked out to inhibit TNBC cell proliferation and migration. RNA-seq analysis of TNBC cells treated with decanoylcarnitine revealed its suppressive effects on extracellular matrix-related protein components, with a notable reduction observed in Mmp9. Further investigations confirmed that decanoylcarnitine could inhibit Mmp9 expression in TNBC cells, primary tumors, lung, and liver metastasis tissues. Mmp9 overexpression abolished the inhibitory effect of decanoylcarnitine on cell migration. <b>Conclusion:</b> This study pioneers the exploration of IF intervention and the role of decanoylcarnitine/Mmp9 in the progression of TNBC in obese mice, enhancing our comprehension of the potential roles of various dietary patterns in the process of cancer treatment.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241233443"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10898300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139973564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}