Anti-Human CD131 PE 25 tests
- Known as:
- Antibody toHuman CD131 PE 25 tests
- Catalog number:
- 12-1319-41
- Category:
- -
- Supplier:
- eBioscience
- Gene target:
- Anti-Human CD131 25 tests
Related genes to: Anti-Human CD131 PE 25 tests
- Gene:
- CSF2RB NIH gene
- Name:
- colony stimulating factor 2 receptor beta common subunit
- Previous symbol:
- IL3RB
- Synonyms:
- IL5RB, CD131, betaGMR
- Chromosome:
- 22q12.3
- Locus Type:
- gene with protein product
- Date approved:
- 1991-08-07
- Date modifiied:
- 2017-07-12
Related products to: Anti-Human CD131 PE 25 tests
Related articles to: Anti-Human CD131 PE 25 tests
- (CT) infection has been identified as an independent predictor of risk of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC); however, its prognostic relevance and molecular mechanisms remain poorly understood. Therefore, we aimed to construct a CESC prognostic model based on genes associated with CT infection. - Source: PubMed
Publication date: 2026/07/09
Wang MeiXiong ShiyinQiu XiufengHu ZhengruiXie LangZhao HaoYou Cong - Type 2 diabetes (T2DM) is clinically heterogeneous. A subgroup with markedly reduced C-peptide has poorer glycemic stability and different therapeutic needs, but the associated peripheral immune features and practical non-invasive markers are not well defined. We investigated whether T2DM with low C-peptide (T2DM-LowC) is associated with a distinct peripheral immune profile. - Source: PubMed
Publication date: 2026/07/08
Xu YajingLi YitingZeng XiyingChen LijunLin HongliXie WenxinWang ZitongJiang ZhengrongDuan HonghongHuang Huibin - Depletion of CD4 cells during an anti-tumor immune response promotes tumor regression and accelerates proliferation of tumor-antigen-specific CD8 T cells in draining lymph nodes (dLNs). However, the effect of the depletion on inter-organ kinetics of antigen presentation and qualitative changes in dendritic cells (DCs) are not yet understood. Here, we established a novel approach for simultaneous detection of cellular movement and cell cycle phase by KikGR/Fucci mice and used it to examine migratory and LN resident DC (LNDC) dynamics after CD4 cell depletion. We found that CD4 cell depletion enhanced migration of CD11c MHC class II migratory DCs from tumor to dLNs and induced activation-associated phenotypic changes, such as increased MHC class II expression. Despite reduced overall cellularity in tumor dLNs, LNDC numbers were relatively maintained. Within the LNDC compartment, CD4 depletion increased the relative abundance of a CD8αCD11b subset and promoted influx and cell-cycle activity among newly recruited LNDCs. Publicly available single-cell RNA sequencing data further delineated ligand-receptor expression relationships, including and axes, within tumor dLNs. These findings reveal remodeling of DC migration, activation, and LNDC turnover within tumor dLNs in the context of CD4 cell depletion, which enhanced anti-tumor immunity. - Source: PubMed
Publication date: 2026/06/10
Moriya TaikiHashimoto MayukoUeda MizukiAoyagi TatsuyaKawaguchi AyakoTakahashi KentaroTsukasa KobayashiDoi KazukiHemmi HiroakiKaisho TsuneyasuUeha SatoshiMatsushima KojiKusumoto YutakaChtanova TatyanaTomura Michio - IntroductionLupus nephritis (LN) is characterized by significant heterogeneity and a complex pathophysiology, which traditional methods struggle to fully resolve. Advanced multi-omics approaches are essential to disentangle its cellular and molecular drivers.MethodsWe employed an integrative strategy combining single-cell RNA sequencing (scRNA-seq) profiling of LN biopsies with large-scale bulk RNA-seq cohorts. We applied non-negative matrix factorization (NMF) to scRNA-seq data to define robust immune meta-programs and utilized CellChat to decode cell-cell communication networks. Leveraging these insights to overcome sample size limitations, we prioritized key pathways and developed 399 machine learning predictive models using bulk transcriptomics, validated on independent cohorts.ResultsScRNA-seq analysis revealed a distinct cellular landscape, including a rare population of plasmacytoid dendritic cells (pDCs) and an expanded population of CD56dimCD16 natural killer (NK) cells expressing high levels of IFN-γ and perforin, suggesting a role in inflammatory pathology. Macrophage subpopulation CM2 emerged as a central pro-inflammatory hub, potentially driving fibrosis via autocrine signaling and epithelial activation. We observed reduced Treg-B cell interactions, suggesting a regulatory collapse. Our machine learning models, based on innate immunity, circadian rhythms, apoptosis, and NF-κB signaling, achieved high diagnostic accuracy (AUC = 0.929 for innate immunity). Hub genes, including , , and , were confirmed to be upregulated in LN and correlated with clinical severity in external validation datasets. Molecular docking simulations suggested a potential structural basis for CYBB-dexamethasone interaction, providing a hypothesis for future verification.DiscussionThis study identifies CM2 macrophages and dysregulated pDC-NK axes as key drivers of LN. By bridging cellular interactomes with clinical predictive modeling, we provide a robust roadmap for precision detection and identifying potential therapeutic targets in LN. - Source: PubMed
Publication date: 2026/04/27
Mou LishaLu YingWu ZijingPu Zuhui - BACKGROUNDS: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, stands as a leading cause of cancer-related mortality worldwide. Immunogenic cell death (ICD )enhances cancer therapy efficacy by inducing immune responses and reshaping the TIME. While ICD increases cytotoxic T lymphocyte infiltration and reduces immunosuppressive elements, the specific TIME subtypes associated with ICD in lung adenocarcinoma (LUAD) remain undefined. METHODS: Publicly available datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were utilized in this study to identify differentially expressed genes related to ICD. We applied consensus molecular clustering to integrate these genes with clinical phenotypes, revealing distinct NSCLC subtypes with varying prognostic outcomes. The tumor immune microenvironments of these clusters were characterized using the ‘estimate’ R package ‘and ‘CIBERSORT’.A prognostic model was established utilizing LASSO Cox regression and validated across independent cohorts. Functional validation involved RNA interference targeting the KCNN4 gene in PC-9 lung cancer cells, including quantitative PCR, cell proliferation assays, wound healing assays, and Transwell invasion assays. Additionally, molecular docking and molecular dynamics simulations were performed to identify and validate small-molecule drugs targeting KCNN4. RESULTS: Unsupervised clustering of ICD-related gene expressions delineated three novel NSCLC subtypes. Cluster 1, characterized by younger patients, exhibited enhanced immune activity with significant infiltration of activated CD4+ and CD8+ T cells, correlating with a favorable prognosis and responsiveness to immunotherapy. Cluster 2, predominantly female, displayed suppressed immune responses with reduced effector memory T cells and γδ T cells, associated with poorer outcomes. Cluster 3, linked to varying cancer stages, showed moderate immune activity with lower immune cell infiltration and higher tumor purity, indicating an intermediate prognosis. A comprehensive prognostic model combined the expression levels of five key ICD-related genes (CSF2RB, CD3D, ADA2, KCNN4, and AREG) with critical clinical factors. Targeted silencing of KCNN4 in PC-9 cells significantly reduced tumor aggressiveness, supporting its role as a therapeutic target. Molecular docking identified four promising small-molecule drugs, with ZINC000000001547 (Hydroxystilbamidine) showing stable interactions in molecular dynamics simulations. CONCLUSIONS: This study identifies three distinct ICD-related TIME subtypes in NSCLC with significant translational potential: Cluster 1 patients are ideal candidates for immune checkpoint inhibitors; Cluster 2 may benefit from combination therapies to overcome resistance; and Cluster 3 requires aggressive multimodal treatments. Additionally, the predictive model enhances risk stratification, improving patient outcomes in NSCLC. - Source: PubMed
Publication date: 2026/02/25
Yang ZhengzhengYang QianruLi ShujiaoHan RuiyangHe HaiqiuZhang RunzeLi HaimingLi YudongCheng PeiyuLiu Xiawei