Omics A Journal of Integrative Biology最新文献

{"title":"Genome-Wide Association Study Identifies Human Genetic Variants Associated with Systemic Chronic Inflammation.","authors":"Jiayi Xue, Wenxin Sun, Peipei Liu, Jingyu Wang, Youxin Wang","doi":"10.1177/15578100261458847","DOIUrl":"10.1177/15578100261458847","url":null,"abstract":"<p><p>Chronic low-grade inflammation has been identified as the etiology of disease across the life span; however, the underlying genetic mechanism are poorly understood, and no genetic association study is performed. Here, we quantified chronic low-grade inflammation using the low-grade inflammation score (INFLA-score) and performed a genome-wide association study in a large European cohort of 217,984 individuals, involving 6,134,151 single nucleotide polymorphisms (SNPs). We identified 20,182 SNPs at 194 loci reaching genome-wide significance (<i>p</i> < 5 × 10^-8), with the lead SNP rs429358 in apolipoprotein E (APOE) showing an extremely low <i>p</i>-value of 8.69 × 10^-166. Gene analysis found 470 genes with <i>p</i> < 2.70 × 10^-6 (0.05/18,519), among which phosphodiesterase 4B was most significant (<i>p</i> = 1.67 × 10^-22). Among those, 15 SNPs were first reported, including GS1-259H13.10, AC068533.7, AL355490.1, and so on. Tissue expression analysis revealed significant links between genetic variants and chronic low-grade inflammation in spleen and whole blood. Our findings identify variants linked to chronic low-grade inflammation and related inflammatory and cellular regulation traits, and suggest a close association between chronic low-grade inflammation and the spleen.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"449-458"},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148218879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network-Based Multiomics Integration Reveals Immunometabolic Convergence Between COVID-19 and Pulmonary Arterial Hypertension.","authors":"Defne Cig Kabakcioglu, Ceyda Kasavi","doi":"10.1177/15578100261462757","DOIUrl":"10.1177/15578100261462757","url":null,"abstract":"<p><p>Coronavirus disease 2019 and pulmonary arterial hypertension (PAH) are clinically distinct disorders that converge on severe pulmonary vascular dysfunction, endothelial injury, and cardiopulmonary failure. However, the shared systems-level molecular architecture linking acute, virus-induced vascular damage with chronic pulmonary vascular remodeling remains undefined. To address this critical gap, we conducted a novel, integrative multilayered network analysis. By simultaneously combining transcriptome profiles of lung tissue samples with protein-protein interaction, metabolic, and regulatory networks, we systematically compared the previously unmapped molecular landscapes of both conditions. Despite their distinct upstream triggers-immune receptor-dominated signaling in acute severe acute respiratory syndrome coronavirus 2 infection versus remodeling- and ion channel-associated signaling in PAH-cross-disease integration revealed a highly structured, convergent immunometabolic regulatory architecture. This core is defined by extensive transcriptional reprogramming and the coordinated rewiring of oxidative phosphorylation and acetyl-CoA-associated metabolism. Taken together, these findings define a systems-level convergence model that mechanistically links acute viral endothelial injury and chronic pulmonary vasculopathy through a shared immunometabolic axis. This integrative molecular framework provides a new foundation for prioritizing candidate biomarkers and therapeutic nodes that target the overlapping vascular and inflammatory mechanisms of both conditions.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"479-491"},"PeriodicalIF":2.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148302427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating Immune Microenvironment of Inflammatory Bowel Disease and the Mitochondrial Metabolism-Related Molecular Mechanisms: Combining a Multiomics Approach and Experimental Validation.","authors":"Hao Li, Xichuan Wu, Qingping Wu, Jian Wang","doi":"10.1177/15578100261456804","DOIUrl":"10.1177/15578100261456804","url":null,"abstract":"<p><p>Inflammatory bowel disease (IBD) is a chronic and recurrent gastrointestinal disease, the pathogenesis of which has not been fully elucidated. Increasing evidence suggests that the disorder of mitochondrial metabolism is closely related to the pathogenesis of IBD, but its specific regulatory network and key genes remain to be further investigated. IBD-related transcriptome datasets (GSE3365 and GSE75214) and single-cell sequencing dataset (GSE134809) were obtained from the Gene Expression Omnibus database. Differentially expressed genes and hub genes were identified through differential expression analysis and weighted gene co-expression network analysis, and candidate genes were obtained by intersecting these with mitochondrial metabolism-related genes, followed by functional enrichment analysis. Machine learning algorithms were used to screen key genes and construct risk prediction models. Additionally, analysis of GSE134809 single-cell data identified characteristic cell types and expression distribution of key genes in IBD and explored communication between different cell types. Furthermore, immune cell infiltration, competitive endogenous RNA (ceRNA) network, and transcription factor prediction were performed. Finally, the diagnostic performance of key genes was validated in GSE75214 and reverse transcription-quantitative polymerase chain reaction. Two key genes, mitochondrial ribosomal protein L35 (<i>MRPL35</i>) and <i>MRPL39</i>, were identified, which were downregulated in IBD, and had good diagnostic potential. Single-cell analysis revealed that key genes were predominantly highly expressed in mononuclear phagocyte (MNP) cells. MNP cells communicated with other cells through receptor ligands including MIF-(CD74 + CXCR4), MDK-SDC1, and ITGB2-ICAM2, which are complexly related to mitochondrial metabolism. With the progression of IBD, infiltration levels of resting natural killer cells, naive B cells, M2 macrophages, and naive CD4 T cells decreased, and correlations between different cells continuously changed. A ceRNA network centered on XIST, hsa-miR-103a-3p, and <i>MRPL35</i> was constructed. Additionally, therapeutic drugs targeting key genes were predicted, including cimetidine, eugenol, chlortetracycline, vincristine, irinotecan, bisacodyl, and sulpiride, with molecular docking validating high affinity between these drugs and key targets. This study constructed a multiomics integrated analysis strategy and identified <i>MRPL35</i> and <i>MRPL39</i> as potential markers and therapeutic targets, providing new insights for the diagnosis and treatment of IBD.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"396-408"},"PeriodicalIF":2.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148252598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Pharmacological Analysis of Lentinan in Regulating Intervertebral Disc Degeneration: Combined with Machine Learning-Based Screening and Molecular Dynamics Validation.","authors":"Hanhua Cai, Yihuang Chen, Huan Liu, Tao Zhang","doi":"10.1177/15578100261454407","DOIUrl":"10.1177/15578100261454407","url":null,"abstract":"<p><p>Lentinan (LNT) is a polysaccharide with antioxidant and anti-inflammatory properties; however, its link to intervertebral disc degeneration (IVDD) remains unclear. Here, we integrated network pharmacology, machine learning, molecular docking, and molecular dynamics (MD) simulations to investigate the LNT-IVDD relationship. Using dataset GSE176205, 16,361 potential IVDD-related genes were identified, and 97 LNT-associated targets were collected from four databases, yielding 46 intersecting targets. GO and KEGG analyses revealed enrichment in pathways including calcium, PI3K-Akt, Notch, and Rap1 signaling. Protein-protein interaction network analysis combined with three ML methods prioritized two core targets: signal transducer and activator of transcription 3 (STAT3) and fibroblast growth factor 2 (FGF2). The diagnostic relevance of these genes was subsequently validated using receiver operating characteristic curves across three independent IVDD datasets. Molecular docking showed strong binding (energy < -5 kcal/mol) between LNT and these targets, further supported by stable complex formation in MD simulations. Immune-infiltration analysis delineated the IVDD microenvironment, and virtual knockout based on single-cell transcriptomics systematically assessed how perturbing key genes remodels cell states and signaling networks. Together, this work provides novel insights into the molecular mechanisms underlying LNT's therapeutic effects in IVDD and establishes a foundation for its translational study.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"380-395"},"PeriodicalIF":2.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148055617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiomics Analysis Identifies Candidate Molecular, Microbiota, and Metabolite Features Associated with Acute Myocardial Infarction Combined with Chronic Renal Failure.","authors":"Huijing Li, Huiqiong Li, Rongjing Wu, Miao Zhong","doi":"10.1177/15578100261457660","DOIUrl":"10.1177/15578100261457660","url":null,"abstract":"<p><p>Chronic renal failure (CRF) is a major risk factor for acute myocardial infarction (AMI) progression and poor outcomes, but the molecular, microbial, and metabolic features of AMI combined with CRF remain unclear. In this study, four Gene Expression Omnibus datasets were analyzed separately. GSE194388 and GSE180393 were used as discovery cohorts for AMI and CRF, and GSE109048 and GSE142153 were used as validation cohorts. Fecal samples from CRF patients without AMI (negative control [NC], <i>n</i> = 10) and patients with AMI and advanced CKD (SY, <i>n</i> = 10) were analyzed by 16S rRNA sequencing. Matched serum samples were analyzed by untargeted metabolomics. Four shared candidate hub genes, PTPRC, ITGAL, CD44, and SELL, were identified and showed preliminary discriminatory performance in validation datasets, although potential overfitting should be considered. These genes were positively correlated with increased immune cell subsets. Compared with NC, SY showed exploratory taxa-level microbiota differences, including increased Enterobacterales and decreased <i>Bifidobacterium</i>, without significant global diversity differences. Differential serum metabolites, including increased l-kynurenine and decreased glucose, were also identified and correlated with altered taxa. This integrated multiomics analysis identified candidate immune-related genes, microbiota features, and serum metabolites associated with AMI combined with CRF, providing preliminary evidence for future validation.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"369-379"},"PeriodicalIF":2.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148212088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Procaterol Enhances Cisplatin-Induced Cytotoxicity via Apoptosis, Oxidative Stress, and Cell Cycle Arrest in Breast and Cervical Cancer Cells.","authors":"Neha Sajwani, Giridhara Prema Suchitha, T S Keshava Prasad, Shobha Dagamajalu","doi":"10.1177/15578100261459475","DOIUrl":"10.1177/15578100261459475","url":null,"abstract":"<p><p>Breast and cervical cancers remain significant causes of cancer-related deaths in women, necessitating the development of effective treatments. This study investigated the repositioning potential of procaterol as a chemosensitizer to increase the efficacy of cisplatin in HeLa and MCF-7 cells. On its own, procaterol showed limited cytotoxicity; however, it significantly inhibited cell viability when combined with low doses of cisplatin. Both colony formation and cell proliferation studies showed a strong inhibition of cell growth, especially in HeLa cells. Similarly, wound-healing studies showed greater inhibition of cell migration in the presence of both drugs than with either drug alone. Live/dead assays and elevated TNF-α (tumour necrosis factor-alpha) expression confirmed apoptosis. The increased generation of reactive oxygen species suggested that HeLa cells are under oxidative stress. Flow cytometric analyses showed that procaterol induced G0/G1 arrest, cisplatin induced G2/M arrest, and the combination induced both arrests while significantly decreasing S-phase populations. Procaterol sensitized cisplatin by increasing apoptosis and oxidative stress and inducing cell cycle arrest. As an FDA-approved compound, it can be used as a cost-effective adjuvant for treating breast and cervical cancer. Additional <i>in vivo</i> verification and clinical trials are required to establish the translational value of this new combination for the treatment of cervical and breast cancer.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"409-419"},"PeriodicalIF":2.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148278319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Bioinformatics and Structural Assessment Suggest Doxycycline as a Potential Candidate Targeting MMP7 in Lung Cancer and SSc-Associated ILD.","authors":"Sanjukta Dasgupta, Moupiya Ghosh, Sukhendu Sadhukhan, Srijit Set","doi":"10.1177/15578100261454404","DOIUrl":"10.1177/15578100261454404","url":null,"abstract":"<p><p>Lung cancer (LC), particularly lung adenocarcinoma, is a leading cause of cancer-related mortality, while systemic sclerosis-associated interstitial lung disease (SSc-ILD) is a chronic fibrotic autoimmune disorder. Growing evidence suggests shared molecular mechanisms between these conditions, although their genetic overlap remains insufficiently characterized. In this study, integrative bioinformatics approaches were applied to identify common differentially expressed genes (DEGs) and regulatory networks linking LC and SSc-ILD. Comparative transcriptomic analysis revealed 72 shared DEGs, including 10 hub genes: <i>MMP7</i>, <i>CXCL14</i>, and <i>CD79A</i> (upregulated), and <i>CXCR2</i>, <i>IL1A</i>, <i>FCAR</i>, <i>GRIA1</i>, <i>IL1R2</i>, <i>CSF3</i>, and <i>FFAR2</i> (downregulated). Regulatory network analysis identified key miRNAs (hsa-miR-34a-5p, hsa-miR-126-3p, hsa-miR-335-5p) and central transcription factors (FOS, JUN, NFKB1). Pathway enrichment highlighted interleukin-10 signaling as a critical shared pathway, implicating immune regulation and extracellular matrix remodeling. Molecular docking suggested a potential interaction between doxycycline and MMP7 (binding affinity -6.4 kcal/mol), while X-ray diffraction and Rietveld refinement confirmed its crystalline purity. Additionally, doxycycline demonstrated antibacterial activity against <i>Staphylococcus aureus</i> via minimum inhibitory concentration assays. Overall, these findings provide preliminary insights into shared molecular features between LC and SSc-ILD and suggest doxycycline as a potential repurposed candidate warranting further experimental and clinical validation.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"334-343"},"PeriodicalIF":2.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148033771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative Proteomics Reveals Distinct Lipid Metabolic and Gut Microbiota-Associated Antidepressant Mechanisms of Two Herbal Medicines in a Chronic Stress Model.","authors":"Jingxuan Tan, Linjie Li, Xixuan Li, Xuemei Han, Bin Deng, Yanjie Qin, Yu Zhang, Xuejia Zhai, Yongning Lu","doi":"10.1177/15578100261454586","DOIUrl":"10.1177/15578100261454586","url":null,"abstract":"<p><p>Depression is a prevalent and disabling mental disorder associated with increased suicide risk and impaired quality of life. Herbal medicines such as <i>Hypericum perforatum</i> L. (St. John's Wort [SJW]) and Chaihu Shugan San (CSGS) have been widely used to treat depressive symptoms, yet their system-level molecular mechanisms remain incompletely understood. In this study, we applied a multi-omics strategy centered on quantitative proteomics, combined with metabolomics and microbiota profiling, to investigate the antidepressant mechanisms of CSGS and SJW in a chronic unpredictable mild stress rat model. Behavioral assessments confirmed that both treatments significantly alleviated depressive-like behaviors. Proteomic analysis identified 204 differentially expressed proteins (DEPs) in the CSGS group and 473 DEPs in the SJW group. Functional enrichment analyses suggested that CSGS was more closely associated with lipid metabolism, whereas SJW was linked to broader metabolic processes. Integrative metabolomic and microbiota analyses further supported these proteomics-driven pathway distinctions, with SJW showing stronger associations with gut microbiota alterations. CSGS and SJW alleviated depressive-like behaviors through divergent yet partially overlapping molecular mechanisms. These findings highlight the utility of multi-omics approaches for elucidating system-level mechanisms of complex herbal medicines.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"354-367"},"PeriodicalIF":2.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148199602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic Sequencing from Sanger to Next-Generation Sequencing: Historical Context, Comparative Advances, and Prospects for Next-Generation Phenomics.","authors":"Akash Ranga, Anvita Gupta Malhotra, Jitendra Singh, Khushhali M Pandey","doi":"10.1177/15578100261433762","DOIUrl":"10.1177/15578100261433762","url":null,"abstract":"<p><p>DNA sequencing has revolutionized biological and biomedical research, offering profound insights into genome organization, function, and variability. From the pioneering Sanger capillary electrophoresis method to the advent of next-generation sequencing, the field has evolved toward unprecedented speed, scalability, and cost decreases over the years. These advancements have enabled diverse applications across genomics, transcriptomics, metagenomics, epigenomics, and precision medicine, powering global initiatives such as the Human Genome Project, the Human Microbiome Project, and the 1000 Genomes Project. Bioinformatics has also advanced in data processing, variant detection, and functional annotation, helping transform raw sequencing data into biologically meaningful insights and knowledge. Although highly advanced, sequencing technologies still encounter challenges, including accuracy trade-offs and the need for efficient management of rapidly increasing volumes of data. Leveraging the genomic revolution, this review explores the shifts toward next-generation phenomics (NGP), an archetype that uses artificial intelligence that integrates multi-omics data with digital phenotyping, the Internet of Things, and real-time analytics. The goal of NGP is to integrate genotypic and phenotypic data to support predictive modeling of health, disease, and environmental interactions. By tracing history, advances in sequencing technologies, and future perspectives on NGP, this article offers a comprehensive overview for researchers and clinicians, highlighting how the integration of omics and digital data will drive the generation of personalized and systems-level biology.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"317-333"},"PeriodicalIF":2.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147499455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"piR-LGBM: A Sparse Autoencoder-Enhanced Gradient Boosting Framework to Uncover Disease-Associated piRNAs.","authors":"Aiswarya Mohan, Deepthi K","doi":"10.1177/15578100261456808","DOIUrl":"10.1177/15578100261456808","url":null,"abstract":"<p><p>Piwi-interacting RNAs (piRNAs) are a distinctive category of single-stranded, noncoding RNAs that are crucial for regulating gene expression. Recent studies have shown that piRNAs have a major role in regulating germ and stem cell development, and their dysregulation is connected to various diseases. Consequently, precise identification of piRNA-disease correlations is crucial for understanding disease prognosis and therapy. Establishing the relationships between diseases and piRNAs through experimental research poses challenges and requires a substantial amount of cost and time. Computational approaches offer a promising alternative to mitigate these limitations. This study presents an ensemble approach piR-LGBM that relies on sparse autoencoder and Light Gradient Boosting Machine (LightGBM) classifier to uncover new associations between piRNAs and diseases. The proposed framework generates feature vectors by utilizing the information from piRNA sequences, disease semantics, and currently available piRNA-disease correlation data. The extracted features are then passed through a sparse autoencoder and subsequently to a LightGBM classifier for predicting novel piRNA-disease associations. piR-LGBM yielded an AUC of 0.9640 on fivefold cross-validation. We analyzed the performance of the model against leading methods and classifiers. The empirical results and case-based insights highlight piR-LGBM's efficacy in identifying piRNA biomarkers behind diseases.</p>","PeriodicalId":19530,"journal":{"name":"Omics A Journal of Integrative Biology","volume":" ","pages":"344-353"},"PeriodicalIF":2.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148212105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}