Bioinformatics and Biology Insights最新文献

{"title":"Evaluation of the Antigenic Potential of Epitopes Derived From <i>Leishmania braziliensis</i>.","authors":"Ntc Costa, Ams Pereira, C C Silva, Abx Silva, E O Souza, Lfgr Ferreira, M Z Hernandes, Vra Pereira","doi":"10.1177/11779322251375244","DOIUrl":"https://doi.org/10.1177/11779322251375244","url":null,"abstract":"<p><p><b>Background:</b> Leishmaniasis is a neglected tropical disease caused by protozoa of the genus Leishmania, predominantly affecting populations with limited socioeconomic resources. <i>Leishmania (V.) braziliensis</i> is one of the primary etiological agents for cutaneous leishmaniasis (CL) in Brazil. This study aims to evaluate the interactions between IgG antibodies and 10 antigens derived from <i>L braziliensis</i> for diagnostic applications. These antigens were selected using in silico reverse vaccinology approaches, based on previous research conducted by our group. <b>Methods:</b> A total of 124 IgG antibody structures were retrieved from the SAbDab database. Antigen-antibody (Ag-Ab) complexes were subjected to molecular docking analyses using the SnugDock protocol implemented in the Rosetta platform. In parallel, enzyme-linked immunosorbent assays (ELISA) were performed to assess the diagnostic performance of the selected peptides in detecting active CL. <b>Results:</b> Peptides VIII, VI, V, and I showed the most favorable docking scores, indicating a higher predicted binding affinity with IgG. In ELISA assays, sensitivity values ranged from 0% to 96%, whereas specificity varied from 29% to 86%. Peptides III, IV, and V demonstrated the highest sensitivity, achieving values of 96%, 96%, and 94%, respectively. <b>Conclusions:</b> Considering both in silico and in vitro results, peptides IV and V corroborate significatively, demonstrating higher predicted affinity (more negative docking score values) with the set of antibodies (Ab) used in calculations.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251375244"},"PeriodicalIF":2.4,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12497966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243666","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":"Identification of Potent HDAC6 Inhibitors for Breast Cancer Through Multi-Stage In Silico Modeling.","authors":"Vaishali Pankaj, Inderjeet Bhogal, Sudeep Roy","doi":"10.1177/11779322251379037","DOIUrl":"10.1177/11779322251379037","url":null,"abstract":"<p><p>Histone deacetylases (HDACs) are essential epigenetic regulators, with HDAC6 overexpression linked to estrogen receptor (ER) activity and breast cancer progression. While several HDAC6 inhibitors have been investigated, their clinical success remains limited due to toxicity and off-target effects, necessitating the discovery of novel, selective inhibitors. This study employs a multi-stage computational approach to identify potent HDAC6 inhibitors for breast cancer therapy. A large-scale virtual screening of 264 834 compounds was conducted, followed by molecular docking, molecular dynamics (MD) simulations (100 ns), molecular mechanics/generalized born surface area (MM/GBSA) binding free energy calculations, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. The HDI-3 emerged as the most promising candidate among replicate simulations, exhibiting a substantially favorable MM/GBSA binding free energy of -130.67 kcal/mol-indicative of strong thermodynamic stability and stronger binding affinity compared to reference inhibitors Trichostatin A and Ricolinostat. Molecular dynamics simulations revealed that HDI-3 maintained structural stability, persistent key interactions with active site residues (ASP649, HIS651, ASP742), and low conformational fluctuations. The ADMET evaluation confirmed HDI-3's favorable pharmacokinetic properties, including optimal bioavailability, non-mutagenicity, and low hepatotoxicity. Essential dynamics and principal component analysis further validated its stable binding profile. While these findings highlight HDI-3 as a selective and pharmacologically viable HDAC6 inhibitor, it is important to acknowledge that the results are entirely computational. Therefore, experimental validation is essential to confirm the compound's efficacy and safety. This integrated computational pipeline provides an efficient strategy to accelerate targeted drug discovery, laying the groundwork for future experimental investigations.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251379037"},"PeriodicalIF":2.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184534","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":"Understanding the Impact of Individual Nucleotide on Oxford Nanopore Current Signals With Interpretable Prediction Models.","authors":"Yenan Wang, Zhixing Wu, Jia Meng","doi":"10.1177/11779322251378620","DOIUrl":"10.1177/11779322251378620","url":null,"abstract":"<p><p>Oxford nanopore sequencing enabled real-time, long-read analysis of DNA by detecting ionic current signals associated with K-mer sequences. Although many studies analyzed sequence and modification detection, our understanding of how multiple nucleotides of the K-mer sequence determine nanopore signals together is still limited. In this study, we seek to unveil the positional impact of individual nucleotide through interpretable prediction models. Multiple machine learning models were trained and optimized. To increase model interpretability and explore underlying mechanisms, the tool of SHapley Additive exPlanations was applied to make an assessment of both nucleotides and positions. Our results show that previously unseen Oxford nanopore signals were accurately predicted, and results were consistent on two different modes (R² = 0.9984 for 260 bps, R² = 0.9983 for 400 bps, R10.4 flow cell, XGBoost). Thymine bases (T) at positions 6 and 7 were the most influential, while nucleotides at positions 1, 2, 3, 4, and 9 have minimal impacts on signals. In addition, heatmap analysis toward transitions of bases revealed the impact of individual nucleotide on signal changes in a position-specific manner. Briefly, our work provided predictive and interpretable modeling of nanopore signals, concentrating on influential bases and positions among all obtainable features, which enhanced understanding of nanopore sequencing mechanisms and nucleotide/position-related signal variations.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251378620"},"PeriodicalIF":2.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12457769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145147637","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":"Metagenomic Insights Into Biopile Remediation of Petroleum-Contaminated Soil Using Chicken Droppings in Rivers State, Nigeria.","authors":"Emmanuel O Fenibo, Rosina Nkuna, Tonderayi Matambo","doi":"10.1177/11779322251371117","DOIUrl":"10.1177/11779322251371117","url":null,"abstract":"<p><p>Petroleum hydrocarbon pollution is an escalating global issue, particularly in developing countries, where it has attracted significant attention from researchers focusing on bioremediation, monitoring and sustainability. This study utilised metagenomics to investigate the bacterial community's response in polluted soil undergoing field-scale biopile treatment, with chicken droppings as a nutrient source. Hydrocarbon concentrations were monitored over a 90-day remediation period using the Fourier transform infrared (FTIR) spectrometry technique. Molecular and bioinformatic analyses were conducted to track the dynamics of bacterial species, their abundance and functional roles during the bioremediation process. The initial total petroleum hydrocarbon (TPH) concentration of 446 945 ppm was first reduced to 80 332 ppm through dilution. Following a 90-day bioremediation process using poultry waste, the level further decreased to 5326 ppm, representing a 93.37% reduction. In the metagenomic analysis, a total of 26 736 reads were obtained, averaging 6684 counts per sample. In addition, the study identified diverse bacterial metagenomes, including well-established hydrocarbon-degrading bacteria from Proteobacteria, Firmicutes, Acidobacteria and Actinobacteria phyla, and species previously not reported as hydrocarbon-degrading. Biomarkers associated with hydrocarbon metabolisms, such as aromatic dioxygenases, alkane-1-monooxygenase and methanol oxidation pathways, were identified. A significant decrease in the relative abundance of bacterial genera in heavily polluted soil was observed, alongside an increased presence of <i>Caballeronia</i>, <i>Paraburkholderia</i> and <i>Fontibacillus</i> genera. These findings indicate that chicken droppings contribute 0.30% to the reduction of TPH in the biopiling remediation technique used for treating heavily contaminated soil. A comparative assessment of hydrocarbon attenuation in nutrient-amended vs unamended soils indicates that a 3-month remediation timeframe is insufficient to achieve optimal bioremediation outcomes. However, the TPH reduction in unamended treatment highlights the intrinsic natural attenuation capacity of the impacted soil matrix, attributable to indigenous microbial consortia and prevailing environmental conditions.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251371117"},"PeriodicalIF":2.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12432321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145063312","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":"Repurposing of Anti-Infectives for the Management of Onchocerciasis Using Machine Learning and Protein Docking Studies.","authors":"Cyril Tetteh, Andy Andoh Mensah, Bernice Ampomah, Mahmood B Oppong, Michael Lartey, Paul Owusu Donkor, Kwabena Fm Opuni, Lawrence A Adutwum","doi":"10.1177/11779322251368252","DOIUrl":"10.1177/11779322251368252","url":null,"abstract":"<p><p>There is a need to improve the discovery of new drugs for neglected tropical diseases (NTDs), as the lack of financial incentives has slowed their development. Currently, ivermectin and moxidectin are used in the management of onchocerciasis. We present a proof-of-concept study based on computational methods to find anti-infectives that can be repurposed or serve as lead compounds for onchocerciasis. A combination of exploratory data analysis, machine learning (ML), and molecular docking studies was used to evaluate 58 anti-infective agents. Out of the 58 test drugs, 14 were predicted by at least 5 ML models to be potentially useful in managing onchocerciasis. Molecular docking studies with the 14 predicted drugs using glutamate-gated chloride channel, a known target of ivermectin, an onchocerciasis drug, yielded good results. Cridanimod, diminazene, and vandetanib were the top 3 agents showing the highest binding affinities of -7.8, -7.2, and 7.1 kcal/mol, respectively, higher than the native ligand glutamate, which has a value of -4.5 kcal/mol. The binding interactions of these agents also showed overlaps with that of doramectin and pyrvinium agents that have demonstrated activity against onchocerciasis and ivermectin, the gold standard for onchocerciasis management. This study highlights the potential of cridanimod, diminazene, and vandetanib as promising candidates for developing new treatments for onchocerciasis.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251368252"},"PeriodicalIF":2.4,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013759","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":"Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.","authors":"Megha Hegde, Jean-Christophe Nebel, Farzana Rahman","doi":"10.1177/11779322251358314","DOIUrl":"10.1177/11779322251358314","url":null,"abstract":"<p><p>Interpreting the effects of variants within the human genome and proteome is essential for analysing disease risk, predicting medication response, and developing personalised health interventions. Due to the intrinsic similarities between the structure of natural languages and genetic sequences, natural language processing techniques have demonstrated great applicability in computational variant effect prediction. In particular, the advent of the Transformer has led to significant advancements in the field. However, transformer-based models are not without their limitations, and a number of extensions and alternatives have been developed to improve results and enhance computational efficiency. This systematic review investigates over 50 different language modelling approaches to computational variant effect prediction over the past decade, analysing the main architectures, and identifying key trends and future directions. Benchmarking of the reviewed models remains unachievable at present, primarily due to the lack of shared evaluation frameworks and data sets.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251358314"},"PeriodicalIF":2.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013820","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":"R2eGIN: Residual Reconstruction Enhanced Graph Isomorphism Network for Accurate Prediction of Poly (ADP-Ribose) Polymerase Inhibitors.","authors":"Candra Zonyfar, Soualihou Ngnamsie Njimbouom, Sophia Mosalla, Jeong-Dong Kim","doi":"10.1177/11779322251366087","DOIUrl":"10.1177/11779322251366087","url":null,"abstract":"<p><p>An advanced graph neural network (GNN) is of great promise to facilitate predicting Poly ADPribose polymerase inhibitors (PARPi). Recent studies design models by leveraging graph representations and molecular descriptor representations, unfortunately, still face challenges in comprehensively capturing spatial relationships and contextual information between atoms. Moreover, combining molecular descriptors with graph representations may introduce information redundancy or lead to the loss of intrinsic molecular structures. To this end, we proposed a novel Residual Reconstruction Enhanced Graph Isomorphism Network (R2eGIN) learning model. Specifically, we first designed a residual GIN to learn molecular representations, reduced the impact of vanishing gradients, and enabled the model to capture long-range dependencies. Then, the reconstruction block, by predicting adjacency matrices and node features, was adopted to reconstruct the input graph. To prove the effectiveness of the proposed model, extensive experiments were conducted on 4 data sets of PARPi and compared with 7 existing models. Our evaluation of R2eGIN, conducted using 4 PARPi data sets, shows that the proposed model is comparable to or even outperforms other state-of-the-art models for PARPi prediction. Furthermore, R2eGIN can revolutionize the drug repurposing process through a substantial reduction in the time and costs commonly encountered in traditional drug development methods.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251366087"},"PeriodicalIF":2.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12397607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144942092","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":"<i>In Silico</i> Separation of <i>in Vitro</i> Transcription-Derived Duplicates From PCR Duplicates to Enhance Sequence Data Utilization.","authors":"Ryoga Suzuki, Kenichi Horisawa, Kazumitsu Maehara, Yasuyuki Ohkawa, Atsushi Suzuki","doi":"10.1177/11779322251365042","DOIUrl":"10.1177/11779322251365042","url":null,"abstract":"<p><p>The polymerase chain reaction (PCR) amplification process of deoxyribonucleic acid (DNA) libraries can introduce bias in the sequence ratios. Consequently, several recent genomic and transcriptomic methods employing next-generation sequencing (NGS) utilize <i>in vitro</i> transcription (IVT) to amplify template polynucleotide chains. IVT amplifies nucleic acid sequences linearly, making it less susceptible to bias than the exponential amplification of PCR. Chromatin integration labeling sequencing (ChIL-seq), a tool for analyzing transcription factor binding and histone modifications, has incorporated IVT by replacing PCR in the DNA amplification step, enabling the analysis of small sample sizes, including single cells. In this study, we discovered that many of the excluded sequences known as PCR duplicates during the pre-processing step of ChIL-seq data analysis contain amplification products derived from IVT. Furthermore, we developed an <i>in silico</i> method to selectively eliminate PCR duplicates from NGS data while retaining IVT-derived amplification products. The method prevents excessive data reduction and significantly improves the utilization efficiency of NGS data.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251365042"},"PeriodicalIF":2.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12381453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144942153","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":"Repurposing terfenadine and domperidone for inhibition of apoptotic gene association in colorectal cancer: A system pharmacology approach integrated with molecular docking, MD simulations, and post-MD simulation analysis.","authors":"Pushpaveni C, Hemavathi S, Santosh Prasad Chaudhary Kurmi, Biswa Ranjan Patra, V Angelin Esther, Chandrajeet Kumar Yadav, Mahalakshmi Suresha Biradar, Shankar Thapa","doi":"10.1177/11779322251365019","DOIUrl":"10.1177/11779322251365019","url":null,"abstract":"<p><p>Colorectal cancer (CRC) remains a leading cause of global cancer mortality, underscoring the need for novel therapeutic strategies. This study used a systems pharmacology approach integrated with molecular docking and molecular dynamics (MD) simulations to evaluate the potential of repurposing terfenadine and domperidone for inhibition of apoptotic gene associations in CRC. Network pharmacology analysis identified 4 principal targets-SLC6A4 (5I6X), DRD2 (7DFP), HTR2A (6WGT), and EGFR (6LUD)-involved in the apoptotic regulatory network. Molecular docking studies demonstrated high binding affinities of both terfenadine and domperidone against all selected targets (-7.1 to -11.5 kcal/mol), with the strongest interaction observed with DRD2, where both compounds exhibited a binding affinity of -11.5 kcal/mol. Detailed interaction profiling revealed critical hydrogen bonding and hydrophobic interactions stabilizing the drug-target complexes. Molecular dynamics simulations over a 100 ns timescale confirmed the structural stability and conformational fidelity of the docked complexes, evidenced by low root mean square deviation values and consistent hydrogen bond occupancy. Furthermore, post-MD simulation study supports the stable score landscape and stability of complex. In conclusion, this integrative computational analysis highlights terfenadine and domperidone as promising candidates capable of modulating key apoptotic pathways in CRC. The findings provide a strong rationale for subsequent in vitro and in vivo studies to validate their therapeutic potential and facilitate clinical translation in CRC management.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251365019"},"PeriodicalIF":2.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144942129","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":"Newly Identified Genetic Associations of Alzheimer Disease by Conditional Selective Inference: Potential Implications for the Tau Hypothesis.","authors":"Scott Hebert, Eric Nels Pederson, Zhengqing Ouyang","doi":"10.1177/11779322251358309","DOIUrl":"10.1177/11779322251358309","url":null,"abstract":"<p><p>Over 6 million people are estimated to have been living with Alzheimer disease (AD) in 2020, with another 12 million living with Mild Cognitive Impairment (MCI). Research has been conducted to evaluate genetic links to AD, but more research is needed to improve early disease detection and improve patient outcomes. Diagnostic, demographic information, and single nucleotide polymorphism (SNP) data were collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI). We performed LASSO regression with conditional selective inference to perform feature selection on the SNPs and other predictors (which included education, race, and marital status), which reduced the number of SNPs from 55 106 to 13 and removed all non-SNP predictors except years of education and marital status. The included SNPs reside in genes that have clinical significance and may be associated with diseases that affect cognitive performance. The results propose the alternative alleles for 7 SNPs are associated with increased risk of AD/MCI diagnosis, while 6 SNPs are associated with decreased risk of diagnosis. The results point to a new potential pathway of disease regarding the <i>PAK5</i> gene and the <i>Tau</i> protein hypothesis, which is supported by previous research. This research may have clinical implications and should be further studied.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"19 ","pages":"11779322251358309"},"PeriodicalIF":2.4,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144942083","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}