Computational and structural biotechnology journal最新文献

{"title":"ProNA3D: Distance-Based Analysis of Nucleic Acid-Containing Interfaces.","authors":"Luca R Genz, Maya Topf","doi":"10.34133/csbj.0202","DOIUrl":"10.34133/csbj.0202","url":null,"abstract":"<p><p>Biomolecular interactions are central to many essential cellular processes, but RNA-containing complexes remain challenging to resolve structurally, even as experimental methods and AI-based prediction have expanded structural coverage. Tools for the integrated analysis of interfaces within such complexes remain limited. We present ProNA3D, a tool that provides a unified platform for analyzing protein-nucleic acid and nucleic acid-only complexes, bridging the gap between structure prediction and functional interpretation. ProNA3D supports both experimental and computationally predicted structures, incorporating scoring metrics for AlphaFold 3 predictions. It also offers interactive 2-dimensional interface visualization and secondary-structure topology plots for RNA and DNA. An interface-based density zoning feature facilitates structure analysis of cryo-electron microscopy maps, allowing the evaluation of dynamic complexes in the context of heterogeneous density. We demonstrate ProNA3D on diverse complexes solved by x-ray crystallography or cryo-electron microscopy, as well as on computational models. In one example, ProNA3D identified a high-connectivity nucleotide with potential functional relevance in a trimeric complex of HIV-1 RNA and a human antibody. Applying ProNA3D to the entire Protein Data Bank revealed distinct interface connectivity trends and interaction modes characteristic of specific classes of nucleic acid-containing complexes (e.g., methyltransferase-DNA and CRISPR-associated). The method is available as both a UCSF ChimeraX plug-in for visualization and a command-line tool at https://gitlab.com/topf-lab/ProNA3D. In addition, the repository contains the results of the large-scale analyses.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0202"},"PeriodicalIF":4.8,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13618712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149073814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual Modulation of Adipogenesis and Lipolysis Augments Facial Adipose Tissue Restoration.","authors":"Nayere Taebnia, Magdalena Murawska, Aurino M Kemas, Hongda Sheng, Yi Wang, Jian Xu, Tingting Zhou, Ivan Galanin, Volker M Lauschke","doi":"10.34133/csbj.0213","DOIUrl":"10.34133/csbj.0213","url":null,"abstract":"<p><p>Aging leads to structural changes in facial adipose tissue, characterized by an imbalance between adipocyte regeneration and hypertrophy. Current therapeutic approaches primarily target individual aspects of these changes, often failing to address the dual nature of facial adipose remodeling. Using a combination of in vivo and ex vivo methods, we present a therapeutic strategy based on <i>Kaempferia parviflora</i> root extract (KPRE) rich in polymethoxyflavonoids. Mechanistically, we demonstrate that KPRE sustains preadipocyte proliferation while reducing hypertrophy in mature adipocytes via activation of the transcriptional regulators SOX9 and GATA3 while suppressing PPARγ and SREBP1. To translate these findings toward human application, we conducted a clinical pilot study in 35 participants with topical application of KPRE long-chain fatty acid emulsions for 6 months. By comparing high-resolution stereoscopic imaging and digital 3-dimensional modeling data at baseline and after 3 months and 6 months of follow-up, we show significant volume restoration in atrophic regions and reduction in hypertrophic areas, consistent with the coordinated effects observed in the organotypic adipose culture models. These findings provide a successful example for the rapid translation of molecular insights gained from organotypic human tissue models to human application and provide clinical evidence supporting the potential of KPRE-based formulations for facial adipose tissue rejuvenation.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0213"},"PeriodicalIF":4.8,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13572798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148966817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Overview on T-Cell Engagers: The Current Position in Both the Biological and Mathematical Context.","authors":"Jonathan Davies, Liam V Brown, Mark W Jones, Mark Penney, Gibin Powathil","doi":"10.34133/csbj.0209","DOIUrl":"10.34133/csbj.0209","url":null,"abstract":"<p><p>T-cell engagers (TCEs) are immunotherapeutic drugs that are gaining interest and momentum in oncology, and since 2022, they have been increasingly approved for clinical use. However, there are still many issues and unknowns relating to their pharmacokinetics, efficacy, and adverse side effects. Mathematical models are in the perfect position to improve understanding of these key issues, expand on, and optimize currently approved TCEs. This paper will review the relevant biology behind TCEs, such as the mechanisms behind the antitumor response, the molecular biology of TCE construction, and their use in clinical settings. Then, the review will move to discuss TCE mathematical modeling, key methods, and conclusions to date.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0209"},"PeriodicalIF":4.8,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13562800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148955145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BeitAI-pHLA: Multiallele Peptide-HLA Class I Binding Prediction Using Protein Language Model and Multi-Instance Learning.","authors":"Shigang Qiu, Yong Sun, Xiaofei Ye","doi":"10.34133/csbj.0220","DOIUrl":"10.34133/csbj.0220","url":null,"abstract":"<p><p>Human leukocyte antigen (HLA) molecules participate in cellular immune responses by binding to peptide fragments derived from antigens. Exploring this process is crucial to understanding the mechanisms and underlying factors that regulate the cellular immune system. Due to the extreme polymorphism of HLA, the peptides obtained from mass spectrometry of eluted ligand experiments typically correspond to multiple HLA alleles. They are thus poly-specific, providing various options for HLA assignments. This introduces notable challenges for accurate peptide-HLA assignment and interpretation. To address this limitation, we present BeitAI-pHLA, a deep learning framework for predicting peptide-HLA class I binding by integrating protein language model embeddings with an attention-based multi-instance learning mechanism. Our evaluations on benchmark and external datasets indicate that BeitAI-pHLA markedly outperforms other advanced prediction tools across multiple external validation datasets, achieving an area under the precision-recall curve of 0.967 on the multiallele dataset and maintaining robust performance under varying negative peptide proportions. Furthermore, BeitAI-pHLA demonstrates superior motif deconvolution capability, with higher motif similarity (13.6% increase in position-specific scoring matrix correlation coefficient), and achieves the best area under the precision-recall curve in immunogenic neoepitope prediction, offering improved prioritization of candidate peptides for immunogenicity evaluation. BeitAI-pHLA provides a highly accurate tool for binding prediction and deconvolution in multiallele immunopeptidomics data, offering considerable potential for biomedical research and clinical translation. The BeitAI-pHLA prediction tool is accessible at https://phla.kindstarbiotech.com/, and the source code is available at https://github.com/KindstarGlobalInstitute/BeitAI-pHLA.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0220"},"PeriodicalIF":4.8,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13562803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148955154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-state Structure Prediction of G Protein-Coupled Receptor Proteins via Prompting on AlphaFold.","authors":"Zhigang Sun, Tao Zhang, Kexin Zhang, Anqi Pang, Jiale Yu, Liting Zeng, Sibei Yang, Suwen Zhao, Jie Zheng","doi":"10.34133/csbj.0179","DOIUrl":"10.34133/csbj.0179","url":null,"abstract":"<p><p>G protein-coupled receptors (GPCRs), an essential family of transmembrane proteins, widely participate in signal transduction in organisms and have long been recognized as a major class of therapeutic targets. In structure-based drug design, high-resolution structures of GPCRs in both active and inactive states are essential for designing agonists and antagonists, respectively. However, obtaining experimental GPCR structures is costly, while homology modeling and artificial-intelligence-driven approaches including AlphaFold 2 and AlphaFold 3 often show reduced accuracy for active-state conformations. To address the above limitations, we propose PromptGPCR, an AlphaFold-based inference framework aiming to predict highly accurate structures of GPCRs in both active and inactive states. We provide AlphaFold-Multimer and AlphaFold 3 with biological sequences based on knowledge of structural biology as prompts to guide the models in the multi-state prediction task. Experimental results demonstrate that PromptGPCR can accurately predict active and inactive structures compared to baselines, suggesting its ability to provide structural hypotheses where state-resolved experimental structures are unavailable. Furthermore, PromptGPCR exhibits higher success rates in molecular docking than baselines, and this indicates that the predictions of PromptGPCR may have a certain degree of usability in downstream application scenarios involving specific GPCR conformational states.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0179"},"PeriodicalIF":4.8,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148929192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative Chemistry Platform for Small Molecules Targeting RNA: A Case Study for Chemical Optimization.","authors":"Timothy E H Allen, Susan M Boyd, Maurinne Bonnet, Rabia T Khan","doi":"10.34133/csbj.0218","DOIUrl":"10.34133/csbj.0218","url":null,"abstract":"<p><p>We introduce the Serna Bio GenAI platform, a generative chemistry and multiparametric optimization platform for the design of RNA-targeting small molecules. Targeting RNA with small molecules has proven historically challenging but offers notable potential upsides, including access to unique mechanisms of action and the ability to target otherwise untargetable genes. We consider a major challenge here to be designing chemistry specific to RNA-targeting. Molecular design is a valuable application of artificial intelligence in drug discovery, but many publicly available models use training data focused on protein-targeting-the modality best historically explored in drug discovery. We showcase the difference and value in building a specifically RNA-targeting platform, comparing its performance to state-of-the-art public chemical generators, and experimentally validating its chemical designs in comparison to chemistry designed by a human expert.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0218"},"PeriodicalIF":4.8,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148929155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PatternExtract: A Facile, Scalable Pipeline for Point Pattern Generation from Spatial Imaging Data.","authors":"Shruti Sridhar, Gayatri Kumar, Victoire Ringler, Kanav Gupta, Siddham Jasoria, Ziwei Meng, Patrick Jaynes, Chartsiam Tipgomut, Vaibhav Rajan, David Scott, Claudio Tripodo, Kasthuri Kannan, Anand Devaprasath Jeyesekharan","doi":"10.34133/csbj.0141","DOIUrl":"10.34133/csbj.0141","url":null,"abstract":"<p><p>Spatial point pattern analysis offers a mathematically rigorous framework for characterizing cell distribution in cancer tissue, yet existing pipelines lack robust methods for defining accurate spatial windows that exclude noncellular artifacts. We present PatternExtract, an open-source, multi-platform pipeline for generating biologically accurate spatial point patterns from multiplexed imaging data. The pipeline introduces a 2-kernel tissue segmentation approach that overlays concentric circular kernels on cellular coordinates to construct tissue masks without dependence on proprietary software or fluorescence composite channels, making it compatible with RGB images and coordinate outputs from any cell segmentation tool. Benchmarked on 568 images from 274 diffuse large B cell lymphoma (DLBCL) patients, PatternExtract identified and correctly excluded tissue artifacts in 27% of images, reducing window area by a mean of 9.8 ± 8.2% relative to convex hull methods. Convex hull window misspecification inflated K function AUC by up to 649.6% and produced 100% false rejection of complete spatial randomness under simulation, errors fully corrected by PatternExtract. Cross-cohort validation on an independent 20-patient cohort confirmed pipeline consistency across imaging platforms and staining protocols. Biological application to Ki67⁺ cell distributions in DLBCL demonstrated the pipeline's utility in detecting genuine short-range clustering beyond spatial inhomogeneity. PatternExtract is available at https://github.com/shrutisridhar99/PatternExtract with a Streamlit-based graphical user interface (GUI) for accessibility across programming backgrounds.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"1 1","pages":"0141"},"PeriodicalIF":4.8,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148918655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reference-Free Microsatellite Instability Detection from Tumor Sequencing Using Intrasample Variability Modeling.","authors":"Georgios Vlachos, Tina Moser, Mitesh Patel, James R White, Carina Pischler, Lisa Glawitsch, Thomas Bauernhofer, Philipp Jost, Leo Edlinger, Karl Kashofer, Jochen B Geigl, Luis A Diaz, Ellen Heitzer","doi":"10.34133/csbj.0219","DOIUrl":"10.34133/csbj.0219","url":null,"abstract":"<p><p>Microsatellite instability (MSI) is a predictive biomarker in several tumor types. However, many next-generation sequencing-based callers require matched normal samples, reference panels, or pretrained models, limiting their portability across assays and sequencing centers. We developed PROMIS (PROfiling of Microsatellite InStability), a tumor-only, reference-free pipeline that uses a discrete mixture model to characterize intrasample repeat-length distributions at predefined microsatellite loci. Locus-level classifications are then aggregated into a continuous MSI score. We benchmarked PROMIS in colorectal (CRC), endometrial (UCEC), and gastric (STAD) cancers from The Cancer Genome Atlas. PROMIS achieved an overall area under the receiver operating characteristic curve (AUC) of 0.995 and cohort-specific AUCs of 1.00 in CRC and stomach adenocarcinoma and 0.999 in uterine corpus endometrial carcinoma, comparable to established tools despite not using matched normals or pretrained models. Subsampling demonstrated robust performance with substantially fewer loci. In silico dilution showed progressively reduced MSI-microsatellite-stable discrimination, with the pooled AUC declining from 0.83 at 10% tumor fraction to 0.53 at 1%. At low tumor fractions, tumor-type-specific baseline microsatellite variability increasingly influenced PROMIS scores. Finally, in prostate and CRC cell-free DNA cohorts, including Illumina TSO500 data and an 18-gene panel, PROMIS yielded MSI scores concordant with orthogonal tissue- and panel-based classifications across the evaluated Illumina-based sequencing contexts. Accordingly, the present validation should be considered limited to Illumina-based sequencing platforms. PROMIS is intended to complement existing genomic profiling workflows by enabling MSI assessment from sequencing data already generated for broader molecular analyses. Prospective clinical validation remains necessary before clinical implementation.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0219"},"PeriodicalIF":4.8,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148918620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Skill-File-Guided Virtual Screening: A Single-Target Formyl Peptide Receptor 2 Case Study of Large-Language-Model-Coordinated Physics and Diffusion Docking.","authors":"Osman Gani","doi":"10.34133/csbj.0187","DOIUrl":"10.34133/csbj.0187","url":null,"abstract":"<p><p><b>Background:</b> Virtual screening outcomes depend on protocol choices normally made implicitly by experts. I tested whether encoding that expertise as a \"skill file\" (a Markdown reference read by a large-language-model coding agent) changes virtual screening outcomes versus naive agent defaults, in a single-target case study on formyl peptide receptor 2 (FPR2; Protein Data Bank 7T6S, 3.0 Å cryo-electron microscopy). <b>Methods:</b> A Claude Code agent curated 1,000 pChEMBL ≥5 FPR2 actives and 9,829 property-matched decoys, prepared ligand libraries under naive and skill-guided protocols, and screened them with 2 engines: Uni-Dock (graphics-processing-unit-accelerated AutoDock Vina) and DiffDock v1 (native confidence score). The evaluation used receiver operating characteristic (ROC) area under the curve (AUC), Boltzmann-enhanced discrimination of ROC, enrichment factors (EFs), and paired bias-corrected and accelerated bootstrap, DeLong, and permutation tests. <b>Results:</b> On Uni-Dock, the skill-guided protocol raised ROC AUC from 0.701 to 0.733 (paired ΔAUC = +0.020; DeLong <i>P</i> = 3.4 × 10^-3), while top-of-list enrichment regressed (EF@1% 2.67 → 1.23; EF@0.5% 3.33 → 0.00). Because the arms differ on 5 axes at once, this is a joint-protocol effect, not skill-file guidance alone. DiffDock v1 stayed near random (AUC 0.54 to 0.56) on this out-of-distribution G protein-coupled receptor. <b>Conclusion:</b> In this single-target case study, skill-file-guided protocol selection improved global ranking for a physics-based engine on FPR2 while degrading early enrichment, and it did not rescue a diffusion model outside its training distribution. The +0.020 AUC gain reflects a bundled preparation-and-docking-parameter intervention rather than the skill file alone, comes from a single run per protocol with no seed control, and awaits multitarget, multi-seed replication. Code, data, and the skill file are openly released.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0187"},"PeriodicalIF":4.8,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13554421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148918600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening.","authors":"Danny Vu, Andrew Kowalczewski, Sarah D Burnett, Courtney Sakolish, Xiyuan Liu, Huaxiao Yang, Ivan Rusyn, Zhen Ma","doi":"10.34133/csbj.0217","DOIUrl":"10.34133/csbj.0217","url":null,"abstract":"<p><p>Cardiotoxicity remains a major cause of drug attrition and postmarket withdrawal, yet the vast majority of environmental chemicals to which humans may be exposed remain uncharacterized for cardiotoxicity risk. Human induced pluripotent stem cell (hiPSC)-based testing has been proposed to address this gap. Here, we present an unsupervised deep learning framework for multi-donor cardiotoxicity screening using high-throughput calcium transient recordings from hiPSC-derived cardiomyocytes (hiPSC-CMs). We analyzed data from a library of 1,029 compounds tested in hiPSC-CMs from 5 donors across a concentration range. An autoencoder trained exclusively on baseline signals quantified chemically induced functional perturbations through reconstruction error, bypassing the need for labeled training data while capturing the full spectrum of calcium-handling disruptions. Aggregation of donor-specific scores revealed substantial inter-individual variability in potential cardiotoxicity, underscoring the value of this approach for multi-donor risk prediction. We identified microbiocides, dyes, and pesticides as chemical classes of potential concern, characterized by high toxicity scores and low interdonor variability. This framework establishes a scalable, human-relevant, and genetically diverse platform for cardiotoxicity surveillance across both pharmacological and environmental chemical spaces.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"35 1","pages":"0217"},"PeriodicalIF":4.8,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13547733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148905308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}