Proteomics最新文献

{"title":"An Integrated Whole-Body Normal-Tissue Proteomics Framework Maps Tissue Variation in Senescence-Associated and Ferroptosis-Related Protein Programs.","authors":"Xianbin Meng, Qiyue Luan","doi":"10.1002/pmic.70202","DOIUrl":"https://doi.org/10.1002/pmic.70202","url":null,"abstract":"<p><p>Normal-tissue proteomics can contextualize senescence-associated and ferroptosis-related protein programs. We analyzed 580 normal human tissue specimens (466 adult and 114 fetal) representing 9 adult and 8 fetal Patient IDs. After excluding 748 all-missing proteins, 12,861 proteins were retained. The matrix contained 64.28% missing entries. Pathway scoring used 20 stochastic downshift completions as a model-conditional Monte Carlo ensemble, rather than formal multiple imputation. Across 41 adult tissue categories with at least three specimens, the senescence-associated proteomic proxy was highest in gall bladder and bone and lowest in spinal cord. Observed LGALS7, H1-3, MYH2, MYL1, and GFAP patterns largely reflected tissue identity. GPX4 and FTL were broadly detected, whereas SLC7A11 and CTHRC1 were sparsely detected, describing target presence rather than exposure or toxicity. Observed-only Welch diagnostics identified 5030 proteins with Benjamini-Hochberg false-discovery rates below 0.05 among 8497 testable proteins under specimen-independence assumptions. Extensive missingness, incomplete marker coverage, repeated donor sampling, and limited batch-stage overlap constrain developmental and mechanistic interpretation. Independent biological validation is required. Together, these findings provide a tissue-resolved proteomic reference and an accompanying local platform with a traceable analytical workflow for hypothesis generation and selection of follow-up tissues and proteins.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149316529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Evaluation of Adaptive Focused Acoustics- and SP3-Based Workflows for Deep Proteomic Profiling of 30-Year-Old Formalin-Fixed Paraffin-Embedded Tissues","authors":"Rei Noguchi,&nbsp;Masatsugu Ishii,&nbsp;Yuki Adachi,&nbsp;Yuki Yoshimatsu,&nbsp;Kazuki Sasaki,&nbsp;Nobuyoshi Hiraoka,&nbsp;Sumio Ohtsuki,&nbsp;Tadashi Kondo","doi":"10.1002/pmic.70169","DOIUrl":"10.1002/pmic.70169","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Formalin-fixed paraffin-embedded (FFPE) tissues represent a vast archive for translational research that offers access to specimens with clinical outcomes. However, formalin-induced crosslinking hinders efficient protein extraction and limits proteomic applications. We systematically compared three protein extraction workflows, adaptive focused acoustics (AFA) with Covaris buffer (AFA/Covaris-b), AFA with SDS buffer (AFA/SDS-b), and SDS-based lysis followed by SP3 clean-up and digestion (SP3/SDS-b), using hepatocellular carcinoma (HCC) tissues archived for over 30 years. Benchmarking was performed on two HCC cases using three serial tumor sections each, followed by protein quantification using data-independent acquisition mass spectrometry on a ZenoTOF 7600+ instrument. All workflows identified 5206 proteins, with similar mean identifications (AFA/Covaris-b: 4068; AFA/SDS-b: 4134; SP3/SDS-b: 4048), but distinct reproducibility (CVs: 11.9%, 12.2%, and 20.8%, respectively), leading to the exclusion of SP3. Method selection across eight HCC cases, including paired tumor and adjacent non-tumorous tissues, revealed a total of 6018 proteins, with mean identifications of 3876 (AFA/Covaris-b) and 3852 (AFA/SDS-b). Both AFA workflows showed high reproducibility, with slightly lower variability for AFA/Covaris-b (CVs: HCC: 13.5%, non-tumorous liver tissue (NTL): 11.0%) compared with AFA/SDS-b (CVs: HCC: 14.8%, NTL: 11.3%). While upregulated pathways were not consistently detected across workflows, downregulated pathways showed greater concordance between methods. These findings should be interpreted cautiously given the lack of appropriate temporal or fresh-frozen controls. Collectively, AFA-based workflows enable reproducible proteomic profiling of long-archived FFPE tissues and provide a practical approach for methodological evaluation in retrospective proteomics studies.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Significance of the study&lt;/h3&gt;\u0000 \u0000 &lt;div&gt;\u0000 &lt;ul&gt;\u0000 \u0000 &lt;li&gt;\u0000 &lt;p&gt;FFPE tissues are one of the most valuable clinically annotated biospecimen resources worldwide. However, their routine use in proteomics is hindered by extensive protein cross-linking, particularly in long-term archived blocks. Our study addresses this challenge by systematically evaluating and validating protein extraction strategies for FFPE hepatocellular carcinoma tissues stored for more than 30 years. By directly benchmarking AFA-based and SP3 workflows, we show that AFA protocols, particularly those with the Covaris buffer, provide superior reproducibility while maintaining broad proteome coverage. Importantly, we showed that archival samples yielded more than 6,000 quantifiable proteins, with reproducible pathway-level patterns observed across methods, although interpretation of","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"26 11","pages":"75-87"},"PeriodicalIF":4.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148676359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning Model for Prediction of Unknown Gene Functionality: Gene Bio-BERT","authors":"Srinivas Kudipudi,&nbsp;Vyshnavi Durga Chirumamilla,&nbsp;Pavani Ippili","doi":"10.1002/pmic.70173","DOIUrl":"10.1002/pmic.70173","url":null,"abstract":"<div>\u0000 \u0000 <p>The Human Genome Project (HGP) was a large international research effort that timelined between 1990 and 2003, marking the successful mapping of the entire human genome. Despite the promising performance of deep learning models, especially LLMs like Bio-BERT and ProtBERT on well-annotated datasets. Bio-BERT is a pre-trained language model designed for biomedical corpora, it is not able to independently predict gene functionality. A pre-trained model called ProtBERT uses protein sequences to learn features. It is unable to predict gene functionality directly, without more training data. In order to overcome these challenges, a Gene Bio-BERT based framework is proposed for automated gene function prediction utilizing deep learning methods in biomedical data analysis. This Gene Bio-BERT Framework is divided into 3 modules such as Data collection and Preprocessing, Gene Bio-BERT model training, Feature aggregation layer and prediction of functionality. The initial module focuses on data collection and preprocessing. Data is collected using entrez API from NCBI (National Center for Biotechnology Information) to retrieve human gene data. Preprocessing techniques like Tokenization and feature extraction are then used to handle the data. In the second module, a Gene Bio-BERT transformer encoder with an attention-based feature fusion layer and optimized hyperparameters is used to train the model and learn contextual embeddings. The third module generates results by using aggregated transformer representations to produce functional predictions for unknown genes. In predicting gene function, the proposed Gene Bio-BERT model attains an exceptional accuracy of 94.5% and F1 score of 0.87. Additionally, the model's predictive accuracy remains similar when tested on an unannotated gene which gives similarity score 0.84.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"26 11","pages":"100-110"},"PeriodicalIF":4.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148700272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mitochondrial Omics in Macrophage Foam Cell Formation and Atherosclerosis","authors":"Enhui Wu,&nbsp;Zengyu Wang,&nbsp;Yiwen Wang,&nbsp;Liang Qiao,&nbsp;Dong Xie","doi":"10.1002/pmic.70172","DOIUrl":"10.1002/pmic.70172","url":null,"abstract":"<div>\u0000 \u0000 <p>Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death worldwide, with atherosclerosis serving as the core pathological mechanism driving ischemic heart disease, ischemic stroke, and peripheral artery disease. Macrophages play a central role in atherosclerosis by internalizing modified lipids and transforming them into foam cells, thereby driving plaque formation and progression. Mitochondrial metabolism is critically involved in regulating macrophage function, and targeting mitochondrial dysfunction may provide strategies for limiting plaque inflammation and improving plaque stability. Advances in high-throughput omics technologies and bioinformatic analysis methods have provided powerful tools for in-depth investigation of mitochondrial function. This review first summarizes macrophage heterogeneity and foam cell formation in atherosclerotic plaques. It then compares mitochondrial omics approaches, including proteomics, interactomics, metabolomics, lipidomics, isotope tracing, and emerging single-cell and spatial omics strategies. Finally, it discusses how mitochondrial dysfunction and metabolic reprogramming contribute to macrophage foam cell formation, plaque inflammation, and atherosclerosis progression.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"26 11","pages":"12-23"},"PeriodicalIF":4.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148759610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disease-Specific Glycopattern Signatures of Serum Small Extracellular Vesicles in Depression, Schizophrenia, and Glioblastoma","authors":"Jinye Gong,&nbsp;Zonglin Li,&nbsp;Jiangbo Ding,&nbsp;Shuyi Chen,&nbsp;Muhammad Anas Ramzan,&nbsp;Wanqi Wang,&nbsp;Liying Liu,&nbsp;Yannan Qin","doi":"10.1002/pmic.70170","DOIUrl":"10.1002/pmic.70170","url":null,"abstract":"<div>\u0000 \u0000 <p>Serum small extracellular vesicles (sEVs) carry glycoproteins that reflect the pathophysiological states of their cells of origin, yet their glycomic profiles in neuropsychiatric disorders remain largely unexplored. Here, we isolated sEVs from healthy controls and patients with depressive disorder (DD), schizophrenia (SCZ), or brain glioma (BG), and characterized their glycopatterns using a high-throughput lectin microarray. Concentrations and CD81 expression of sEVs were significantly elevated across all disease groups relative to healthy controls. Lectin microarray analysis revealed both shared and disease-specific glycan alterations: PSA-recognized glycans (high-mannose and core-fucosylated structures) were consistently downregulated in all three conditions, whereas STA-binding glycans (GlcNAc oligomers and core GlcNAc of N-glycans) were upregulated in SCZ and BG but slightly decreased in DD. Disease-specific lectin signatures—including UEA-I, PHA-L, and DBA in DD, and ACA in SCZ—enabled clear discrimination among the four groups. The STA/PSA fluorescence ratio, validated in individual samples via sEVs microarray, effectively distinguished SCZ and BG from DD and controls. These findings establish that serum sEVs harbor disease-specific glycopatterns capable of differentiating depression, schizophrenia, and glioblastoma, offering a minimally invasive diagnostic approach and providing insights into glycan-related mechanisms underlying these disorders.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"26 11","pages":"88-99"},"PeriodicalIF":4.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Issue Information: Proteomics 11'26","authors":"","doi":"10.1002/pmic.70201","DOIUrl":"https://doi.org/10.1002/pmic.70201","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"26 11","pages":"1-5"},"PeriodicalIF":4.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.70201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149312842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exoproteome Remodeling by the BraI/R Quorum Sensing Circuit and Interspecies Competition in Paraburkholderia","authors":"Gabrielle Tomé Cordeiro,&nbsp;Yara Martins da Silva,&nbsp;Giovanna Fernandes Lessa,&nbsp;Amanda Codeço Ferreira Monteiro,&nbsp;Hadassa Loth de Oiveira,&nbsp;Fábio César Sousa Nogueira,&nbsp;Bianca Cruz Neves","doi":"10.1002/pmic.70163","DOIUrl":"10.1002/pmic.70163","url":null,"abstract":"<div>\u0000 \u0000 <p>Quorum sensing (QS) is a cell-to-cell communication mechanism that enables bacteria to coordinate collective behaviors in response to population density. The BraI/R QS system is conserved among <i>Burkholderia</i> and <i>Paraburkholderia</i> species, and its regulatory role is still unknown. In this study, the BraI/R QS of <i>P. kururiensis</i> was investigated for biological control of another bacterial species. The results demonstrate that the functional BraI/R system influences interbacterial competition, as <i>braI</i> and <i>braR</i> mutant strains failed to inhibit the growth of <i>Escherichia coli</i>, in contrast to the wild-type strain. These findings suggest that the BraI/R QS system plays a regulatory role in modulating interspecies antagonism. Comparative exoproteome analyses were performed for the wild-type and the BraI/R QS-mutant <i>P. kururiensis</i> strains, revealing distinct protein populations, including structural and effector proteins of the Type VI secretion system (T6SS). Genomic mapping revealed that two of those T6SS-proteins were encoded within a specific T6SS gene cluster (T6SS-3), while others originate outside any major T6SS gene cluster. The identification of exoproteome targets regulated by the BraI/R QS system represents a significant advance toward the understanding of the molecular mechanisms underpinning <i>P. kururiensis</i> interactions with competing bacteria, further supporting its potential as a biological control agent against phytopathogenic and/or free-living species.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"26 11","pages":"53-63"},"PeriodicalIF":4.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148403143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Reusable Are Public Proteomics Datasets in Practice?","authors":"Giuseppe G. F. Leite,&nbsp;Victor Corasolla Carregari,&nbsp;Alexandre Keiji Tashima,&nbsp;Reinaldo Salomão","doi":"10.1002/pmic.70175","DOIUrl":"10.1002/pmic.70175","url":null,"abstract":"<p>Public proteomics repositories have expanded rapidly, but large-scale reuse still depends on whether deposited studies are computationally reusable. We provide an evidence-based snapshot of this gap by evaluating 500 non-redundant human ProteomeXchange datasets released in 2025. Experimental design annotation was classified as metadata-based when a deposited table (e.g., SDRF, spreadsheet, or text table) explicitly linked samples, files, channels, or quantitative columns to biological groups; sample-based when groups could only be inferred from sample, column or channel names; and no information when reliable group assignment was not possible. Sample-based datasets represented the largest category, comprising 195 deposits (39.0%), followed by datasets lacking reliable design information (170; 34.0%) and metadata-based datasets (135; 27.0%). Among metadata-based deposits, 81 (60.0%) included additional contextual covariates. Processed quantitative outputs were fully available in 245 datasets (49.0%), partially available in 168 (33.6%), and absent in 87 (17.4%). Combining processed quantitative outputs with available biological design information, 189 datasets (37.8%) supported direct matrix-level reuse. Under a stricter scenario requiring metadata-based design, contextual covariates and available processed quantitative outputs, only 64 datasets (12.8%) fulfilled all criteria. These findings suggest that scalable reuse requires more consistent machine-actionable sample-to-file mapping, interpretable design annotation, processing information and processed outputs, while preserving raw data for harmonized reanalysis.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"26 11","pages":"6-11"},"PeriodicalIF":4.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.70175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148872401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tape-Stripped Proteomics: A Comparative Analysis of Different Sample Collection Approaches","authors":"Ali Azimi,&nbsp;Lauren Faul,&nbsp;Petrina Chand,&nbsp;Pablo Fernandez-Penas","doi":"10.1002/pmic.70177","DOIUrl":"10.1002/pmic.70177","url":null,"abstract":"<p>Tape-stripping of the stratum corneum is a promising non-invasive approach for proteomic analysis of skin lesions, but the effects of sampling design and disc pressure on proteome readout remain poorly defined. Here, we assessed how inclusion of surrounding normal-looking skin and variation in adhesive disc pressure influence protein detection from small melanocytic lesions. D-squame tape strips were collected from six participants, including three moles and one matched normal skin site per participant. For lesion 1 (L1), discs were applied using light finger pressure. For lesion 2 (L2), the mole area was marked on the disc and separated from the surrounding skin, which was analysed separately (L2S). For lesion 3 (L3), increased pressure was applied using the upper barrel tip of a marker before excising the lesion-matched area. Samples were analysed by DIA-MS with DIA-NN processing. In total, 2882 protein groups were identified, including 1719 shared across all groups. L3 yielded the highest number of unique proteins (77) and significantly altered proteins versus normal skin (142), compared with L2 (54 unique; 52 altered), L1 (30 unique; 4 altered), L2S (16 unique; 3 altered), and normal skin (7 unique). These findings show that precise lesion-matched disc trimming and increased application pressure substantially improve tape-strip proteomic depth and lesion-specific signal detection. Multivariate analysis and supervised classification further separated lesional samples—most clearly the pressure-assisted L3—from other collection methods, supporting these observations, although the small cohort warrants cautious interpretation.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"26 11","pages":"35-39"},"PeriodicalIF":4.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.70177","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148975206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Novel Interacting Proteins of FUZ and GPR161","authors":"Gabriella Salazar,&nbsp;Anna Battenhouse,&nbsp;Andrew J. Kim,&nbsp;Sung-Eun Kim","doi":"10.1002/pmic.70164","DOIUrl":"10.1002/pmic.70164","url":null,"abstract":"<p>Protein–protein interactions are central to the dynamic regulation of signaling pathways and provide critical insight into the cellular mechanisms underlying human disease. Our previous study demonstrated biochemical and genetic interactions between FUZ and GPR161 in sonic hedgehog signaling during spinal neural tube development. In this study, we sought to identify novel interacting proteins of FUZ and GPR161 to further characterize their biochemical and functional relationships. Using affinity-based liquid chromatography–tandem mass spectrometry of immunoprecipitated complexes from cells overexpressing FUZ and GPR161, we identified 159 shared co-interacting proteins, along with 289 proteins exclusive to FUZ and 617 proteins exclusive to GPR161. Gene Ontology (GO) analysis of the co-interactome revealed significant enrichment in proteasomal catabolic processes and intracellular trafficking pathways. GO analysis of the FUZ-specific interactome showed enrichment in cell cycle progression, mitochondrial membrane, and RNA metabolism, whereas the GPR161-specific interactome was enriched in receptor complex and endoplasmic reticulum-Golgi transport pathways. These findings were supported by STRING network analysis. Among the prioritized candidates, FKBP8 was validated as a binding partner of both FUZ and GPR161. Collectively, our proteomic study defines the protein interaction networks of FUZ and GPR161, highlighting their distinct and cooperative roles in multiple cellular processes and providing a foundation for future functional studies.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"26 11","pages":"64-74"},"PeriodicalIF":4.1,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.70164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}