Cancer Informatics最新文献

{"title":"Melanoma and Human Leukocyte Antigen (HLA): Immunogenicity of 69 HLA Class I Alleles With 11 Antigens Expressed in Melanoma Tumors.","authors":"Apostolos P Georgopoulos,&nbsp;Lisa M James,&nbsp;Spyros A Charonis,&nbsp;Matthew Sanders","doi":"10.1177/11769351231172604","DOIUrl":"https://doi.org/10.1177/11769351231172604","url":null,"abstract":"<p><p>Host immunogenetics play a critical role in the human immune response to melanoma, influencing both melanoma prevalence and immunotherapy outcomes. Beneficial outcomes that stimulate T cell response hinge on binding affinity and immunogenicity of human leukocyte antigen (HLA) with melanoma antigen epitopes. Here, we use an in silico approach to characterize binding affinity and immunogenicity of 69 HLA Class I human leukocyte antigen alleles to epitopes of 11 known melanoma antigens. The findings document a significant proportion of positively immunogenic epitope-allele combinations, with the highest proportions of positive immunogenicity found for the Q13072/BAGE1 melanoma antigen and alleles of the HLA B and C genes. The findings are discussed in terms of a personalized precision HLA-mediated adjunct to immune checkpoint blockade immunotherapy to maximize tumor elimination.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231172604"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d1/c1/10.1177_11769351231172604.PMC10214068.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10663173","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":"Chemical Complementarity of Breast Cancer Resident, T-Cell Receptor CDR3 Domains and the Cancer Antigen, ARMC3, is Associated With Higher Levels of Survival and Granzyme Expression.","authors":"Nagehan Pakasticali,&nbsp;Andrea Chobrutskiy,&nbsp;Dhruv N Patel,&nbsp;Monica Hsiang,&nbsp;Saif Zaman,&nbsp;Konrad J Cios,&nbsp;George Blanck,&nbsp;Boris I Chobrutskiy","doi":"10.1177/11769351231177269","DOIUrl":"https://doi.org/10.1177/11769351231177269","url":null,"abstract":"<p><strong>Introduction: </strong>One of the most pressing goals for cancer immunotherapy at this time is the identification of actionable antigens.</p><p><strong>Methods: </strong>This study relies on the following considerations and approaches to identify potential breast cancer antigens: (i) the significant role of the adaptive immune receptor, complementarity determining region-3 (CDR3) in antigen binding, and the existence cancer testis antigens (CTAs); (ii) chemical attractiveness; and (iii) informing the relevance of the integration of items (i) and (ii) with patient outcome and tumor gene expression data.</p><p><strong>Results: </strong>We have assessed CTAs for associations with survival, based on their chemical complementarity with tumor resident T-cell receptor (TCR), CDR3s. Also, we have established gene expression correlations with the high TCR CDR3-CTA chemical complementarities, for Granzyme B, and other immune biomarkers.</p><p><strong>Conclusions: </strong>Overall, for several independent TCR CDR3 breast cancer datasets, the CTA, ARMC3, stood out as a completely novel, candidate antigen based on multiple algorithms with highly consistent approaches. This conclusion was facilitated by use of the recently constructed Adaptive Match web tool.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231177269"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/43/52/10.1177_11769351231177269.PMC10259117.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10206790","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":"Antibiotic Treatment in End Stage Cancer Patients; Advantages and Disadvantages.","authors":"Tahmasebi Mamak,&nbsp;Hosamirudsari Hadiseh,&nbsp;Familrashtian Shirin,&nbsp;Parash Masoud,&nbsp;Salehi Mohammadreza,&nbsp;Abbaszadeh Mahsa","doi":"10.1177/11769351231161476","DOIUrl":"https://doi.org/10.1177/11769351231161476","url":null,"abstract":"<p><strong>Aim: </strong>In this study our aim was to elucidate whether advanced cancer patients benefit from antibiotic treatment in the last days of life in addition to reviewing the relevant costs and effects.</p><p><strong>Materials and methods: </strong>We reviewed medical records from 100 end-stage cancer patients and their antibiotic use during the hospitalization in Imam Khomeini hospital. Patient's medical records were analyzed retrospectively for cause and periodicity of infections, fever, increase in acute phase proteins, cultures, type and cost of antibiotic.</p><p><strong>Results: </strong>Microorganisms were found in only 29 patients (29%) and the most microorganism among the patients was E. coli (6%). About 78% of the patients had clinical symptoms. The highest dose of antibiotics was related to Ceftriaxone (40.2%) and in the second place was Metronidazole (34.7%) and the lowest dose was related to Levofloxacin, Gentamycin and Colistin (1.4%). Fifty-one patients (71%) did not have any side effects due to antibiotics. The most common side effect of antibiotics among patients was skin rash (12.5%). The average estimated cost for antibiotic use was 7 935 540 Rials (24.4 dollars).</p><p><strong>Conclusion: </strong>Prescription of antibiotics was not effective in symptom control in advanced cancer patients. The cost of using antibiotics during hospitalization is very high and also the risk of developing resistant pathogens during admission should be considered. Antibiotic side effects also occur in patients, causing more harm to the patient at the end of life. Therefore, the benefits of antibiotic advice in this time is less than its negative effects.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231161476"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1f/9b/10.1177_11769351231161476.PMC10064464.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9234982","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":"A Comprehensive Analysis of the PI3K/AKT Pathway: Unveiling Key Proteins and Therapeutic Targets for Cancer Treatment.","authors":"Emad Fadhal","doi":"10.1177/11769351231194273","DOIUrl":"https://doi.org/10.1177/11769351231194273","url":null,"abstract":"<p><strong>Background: </strong>Cancer development and progression involve a complex network of pathways among which certain pathways play a pivotal role in promoting tumor growth and survival. An important pathway in this context is the PI3K/AKT pathway, which regulates crucial cellular processes including proliferation, viability, and metabolic regulation. Dysregulation of this pathway has been strongly linked to the development of various types of cancers. Consequently, it is imperative to identify the key proteins within this pathway as potential targets for impeding cancer cell proliferation and survival.</p><p><strong>Results: </strong>One of the key findings of this study was the identification of signaling proteins that dominate various forms of PI3K/Akt pathway. Furthermore, proteins play critical roles in cancer networks, acting as oncogenes that promote cancer development or as tumor suppressor genes that inhibit tumor growth. This study identified several genes, including KIT, ERBB2, PDGFRA, MET, FGFR2, and FGFR3, which are involved in various types of the PI3K/Akt pathways. Additionally, this study identified 55 proteins that are commonly found in various forms of PI3K/Akt, and these proteins play crucial roles in regulating various biological functions.</p><p><strong>Conclusions: </strong>This study highlights the importance of identifying key proteins involved in the PI3K/AKT pathway. In this study, we identified several genes involved in different pathways that play essential roles in the activation, signaling, and regulation of the pathway. Understanding the proteins participating in the PI3K/AKT pathway is vital for the development of targeted therapies, not only for cancer but also for other related diseases. By elucidating their roles and functions, this study contributes to the advancement of knowledge in the field and paves the way for the development of effective treatments targeting this pathway.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231194273"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ee/2d/10.1177_11769351231194273.PMC10462777.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10357349","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":"In Silico Modeling Demonstrates that User Variability During Tumor Measurement Can Affect In Vivo Therapeutic Efficacy Outcomes.","authors":"Jake T Murkin,&nbsp;Hope E Amos,&nbsp;Daniel W Brough,&nbsp;Karl D Turley","doi":"10.1177/11769351221139257","DOIUrl":"https://doi.org/10.1177/11769351221139257","url":null,"abstract":"<p><p>User measurement bias during subcutaneous tumor measurement is a source of variation in preclinical in vivo studies. We investigated whether this user variability could impact efficacy study outcomes, in the form of the false negative result rate when comparing treated and control groups. Two tumor measurement methods were compared; calipers which rely on manual measurement, and an automatic 3D and thermal imaging device. Tumor growth curve data were used to create an in silico efficacy study with control and treated groups. Before applying user variability, treatment group tumor volumes were statistically different to the control group. Utilizing data collected from 15 different users across 9 in vivo studies, user measurement variability was computed for both methods and simulation was used to investigate its impact on the in silico study outcome. User variability produced a false negative result in 0.7% to 18.5% of simulated studies when using calipers, depending on treatment efficacy. When using an imaging device with lower user variability this was reduced to 0.0% to 2.6%, demonstrating that user variability impacts study outcomes and the ability to detect treatment effect. Reducing variability in efficacy studies can increase confidence in efficacy study outcomes without altering group sizes. By using a measurement device with lower user variability, the chance of missing a therapeutic effect can be reduced and time and resources spent pursuing false results could be saved. This improvement in data quality is of particular interest in discovery and dosing studies, where being able to detect small differences between groups is crucial.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221139257"},"PeriodicalIF":2.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0a/81/10.1177_11769351221139257.PMC9716635.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35253512","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":"Integration of Artificial Intelligence and CRISPR/Cas9 System for Vaccine Design.","authors":"Elham Maserat","doi":"10.1177/11769351221140102","DOIUrl":"https://doi.org/10.1177/11769351221140102","url":null,"abstract":"<p><p>The CRISPR/Cas9 system offers a new approach to genome editing and cancer treatment. This approach is able to detect drug targets and genomic analysis of cancer. The use of artificial intelligence (AI) capacity to edit genomes through CRISPR/Cas9 enables modification of gene mutations, molecular simulation. AI approaches include knowledge discovery approaches, antigen and epitope prediction approaches, and agent based-model approaches. These methods in combination with CRISPR/Cas9 can be used in vaccine design.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221140102"},"PeriodicalIF":2.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/18/8c/10.1177_11769351221140102.PMC9703516.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40713568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts.","authors":"Jessica Weiss,&nbsp;Nhu-An Pham,&nbsp;Melania Pintilie,&nbsp;Ming Li,&nbsp;Geoffrey Liu,&nbsp;Frances A Shepherd,&nbsp;Ming-Sound Tsao,&nbsp;Wei Xu","doi":"10.1177/11769351221136056","DOIUrl":"https://doi.org/10.1177/11769351221136056","url":null,"abstract":"<p><p>Patient-derived tumor xenograft (PDX) models were used to evaluate the effectiveness of preclinical anticancer agents. A design using 1 mouse per patient per drug (1 × 1 × 1) was considered practical for large-scale drug efficacy studies. We evaluated modifiable parameters that could increase the statistical power of this design based on our consolidated PDX experiments. Real studies were used as a reference to investigate the relationship between statistical power with treatment effect size, inter-mouse variation, and tumor measurement frequencies. Our results showed that large effect sizes could be detected at a significance level of .2 or .05 under a 1 × 1 × 1 design. We found that the minimum number of mice required to achieve 80% power at an alpha level of .05 under all situations explored was 21 mice per group for a small effect size and 5 mice per group for a medium effect size.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221136056"},"PeriodicalIF":2.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40488601","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":"A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy.","authors":"Emma Bigelow, Suchi Saria, Brian Piening, Brendan Curti, Alexa Dowdell, Roshanthi Weerasinghe, Carlo Bifulco, Walter Urba, Noam Finkelstein, Elana J Fertig, Alex Baras, Neeha Zaidi, Elizabeth Jaffee, Mark Yarchoan","doi":"10.1177/11769351221136081","DOIUrl":"10.1177/11769351221136081","url":null,"abstract":"<p><p>Tumor mutational burden (TMB), a surrogate for tumor neoepitope burden, is used as a pan-tumor biomarker to identify patients who may benefit from anti-program cell death 1 (PD1) immunotherapy, but it is an imperfect biomarker. Multiple additional genomic characteristics are associated with anti-PD1 responses, but the combined predictive value of these features and the added informativeness of each respective feature remains unknown. We evaluated whether machine learning (ML) approaches using proposed determinants of anti-PD1 response derived from whole exome sequencing (WES) could improve prediction of anti-PD1 responders over TMB alone. Random forest classifiers were trained on publicly available anti-PD1 data (n = 104), and subsequently tested on an independent anti-PD1 cohort (n = 69). Both the training and test datasets included a range of cancer types such as non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), melanoma, and smaller numbers of patients from other tumor types. Features used include summaries such as TMB and number of frameshift mutations, as well as more gene-level features such as counts of mutations associated with immune checkpoint response and resistance. Both ML algorithms demonstrated area under the receiver-operator curves (AUC) that exceeded TMB alone (AUC 0.63 \"human-guided,\" 0.64 \"cluster,\" and 0.58 TMB alone). Mutations within oncogenes disproportionately modulate anti-PD1 responses relative to their overall contribution to tumor neoepitope burden. The use of a ML algorithm evaluating multiple proposed genomic determinants of anti-PD1 responses modestly improves performance over TMB alone, highlighting the need to integrate other biomarkers to further improve model performance.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"21 ","pages":"11769351221136081"},"PeriodicalIF":2.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c7/0c/10.1177_11769351221136081.PMC9685115.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9390672","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":"A transcriptome-Based Deep Neural Network Classifier for Identifying the Site of Origin in Mucinous Cancer.","authors":"Taejin Ahn,&nbsp;Kidong Kim,&nbsp;Hyojin Kim,&nbsp;Sarah Kim,&nbsp;Sangick Park,&nbsp;Kyoungbun Lee","doi":"10.1177/11769351221135141","DOIUrl":"https://doi.org/10.1177/11769351221135141","url":null,"abstract":"Purpose: There is a lack of tools for identifying the site of origin in mucinous cancer. This study aimed to evaluate the performance of a transcriptome-based classifier for identifying the site of origin in mucinous cancer. Materials And Methods: Transcriptomic data of 1878 non-mucinous and 82 mucinous cancer specimens, with 7 sites of origin, namely, the uterine cervix (CESC), colon (COAD), pancreas (PAAD), stomach (STAD), uterine endometrium (UCEC), uterine carcinosarcoma (UCS), and ovary (OV), obtained from The Cancer Genome Atlas, were used as the training and validation sets, respectively. Transcriptomic data of 14 mucinous cancer specimens from a tissue archive were used as the test set. For identifying the site of origin, a set of 100 differentially expressed genes for each site of origin was selected. After removing multiple iterations of the same gene, 427 genes were chosen, and their RNA expression profiles, at each site of origin, were used to train the deep neural network classifier. The performance of the classifier was estimated using the training, validation, and test sets. Results: The accuracy of the model in the training set was 0.998, while that in the validation set was 0.939 (77/82). In the test set which is newly sequenced from a tissue archive, the model showed an accuracy of 0.857 (12/14). t-SNE analysis revealed that samples in the test set were part of the clusters obtained for the training set. Conclusion: Although limited by small sample size, we showed that a transcriptome-based classifier could correctly identify the site of origin of mucinous cancer.","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221135141"},"PeriodicalIF":2.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40477422","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":"Contextual Validation of the Prediction of Postoperative Complications of Colorectal Surgery by the \"<i>ACS NSQIP</i> ^® <i>Risk Calculator</i>\" in a Tunisian Center.","authors":"Mehdi Ben Abdelkrim,&nbsp;Mohamed Amine Elghali,&nbsp;Amany Moussa,&nbsp;Ahmed Ben Abdelaziz","doi":"10.1177/11769351221135153","DOIUrl":"https://doi.org/10.1177/11769351221135153","url":null,"abstract":"<p><strong>Context: </strong>Models for predicting individual risks of surgical complications are advantageous for operative decision making and the nature of postoperative management procedures.</p><p><strong>Objective: </strong>Validate the \"ACS NSQIP^® Risk Calculator\" in the prediction of postoperative complications during colorectal cancer surgery, operated during the years 2015 to 2019.</p><p><strong>Methods: </strong>this is a prognostic validation study of the \"ACS NSQIP^®\" applied retrospectively to patients operated on for colorectal cancer in the surgical department of Farhat Hached hospital, during the 2015 and 2019 5-year term. Three levels of adjustment. Discrimination and calibration were carried out mainly by ROC curves (AUC ⩾ 0.8).</p><p><strong>Results: </strong>In this study, 129 patients were included with a sex ratio of 1.22 and a median age of 62 years. The most common operative procedure was low segmental colectomy with colorectal anastomosis. Thirty-seven patients (28.7%) had at least one postoperative complication. The prediction and cuts-off points values of mortality (AUC = 0.858; CI_95% [0.570-0.960]; Cuts-off points = 1.8%), cardiac complications (AUC = 0.824; CI_95% [0.658-0.990]; Cuts-off points = 1.8%), thromboembolic complications (AUC = 0.802; CI_95% [0.617-0.987]; Cuts-off point = 3.1%), and renal insufficiency (AUC = 0.802; CI_95% [ 0.623-0.981]; Cuts-off point = 1.2%) were adjusted according to level 1 of the calculator.</p><p><strong>Conclusion: </strong>This work contextualized the prediction of postoperative complications in colorectal surgery in the university general surgery department of Farhat Hached in Sousse (Tunisia), making it possible to improve the quality and safety of surgical care. The application of the Tunisian mini calculator is recommended as well as the generalization of validation following the development of a generic calculator for all operating procedures.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221135153"},"PeriodicalIF":2.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9e/54/10.1177_11769351221135153.PMC9661577.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40468086","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}