Anti_Human, mab CXCL11 Source Mouse
- Known as:
- Anti_Human, mab CXCL11 Source Mouse
- Catalog number:
- 101-M349
- Product Quantity:
- 100 µg
- Category:
- -
- Supplier:
- Reliatech
- Gene target:
- Anti_Human mab CXCL11 Source Mouse
Related genes to: Anti_Human, mab CXCL11 Source Mouse
- Gene:
- CXCL11 NIH gene
- Name:
- C-X-C motif chemokine ligand 11
- Previous symbol:
- SCYB9B, SCYB11
- Synonyms:
- H174, b-R1, I-TAC, IP-9
- Chromosome:
- 4q21.1
- Locus Type:
- gene with protein product
- Date approved:
- 1998-06-23
- Date modifiied:
- 2016-10-05
Related products to: Anti_Human, mab CXCL11 Source Mouse
Related articles to: Anti_Human, mab CXCL11 Source Mouse
- Mucosal-Associated Invariant T (MAIT) cells are a subset of unconventional T cells that rapidly respond to early signs of inflammation, infection, and tissue damage. While MAIT cells have been typically associated with microbial infections, given their ability to respond to both microbial-derived riboflavin metabolites and proinflammatory cytokines such as IL-12, IL-15, or IL-18, their role in other inflammatory conditions remains mostly unknown. Alarmins, including IL-25, IL-33, and thymic stromal lymphopoietin (TSLP), are crucial effectors for early inflammatory responses, being released upon epithelial damage and strongly promoting the polarization of a wide variety of immune cells, including leukocytes like neutrophils, monocytes, macrophages, dendritic cells (DCs), NK cells, ILCs and T cells. However, how alarmins-induced environments influence MAIT cells' activity and function is yet to be explored. - Source: PubMed
Publication date: 2026/06/26
García-Escribano CarlotaGallardo-Jiménez MariaGarcía-Cadarso AnaFernández Martínez PalomaArroyo-Solera RicardoZaldívar-Martínez Luis SenadorLin KelinAubé JeffreyBarral PatriciaChivato TomásBarber DomingoEscribese Maria MIzquierdo ElenaLópez-Rodríguez Juan Carlos - Pancreatic ductal adenocarcinoma is a malignancy characterized by profound immunosuppression and universal resistance to immunotherapy. The complex tumor microenvironment is a critical determinant of therapeutic failure. The chemokine C-X-C motif chemokine ligand 11 (CXCL11) exerts dual immunomodulatory roles in various tumors; however, its expression pattern, clinical significance, and molecular mechanisms in pancreatic cancer remain unclear. - Source: PubMed
Publication date: 2026/07/10
Wei FangYang LuZeng XianghaiWang QianWang ZiqiZhao BenYuan Xianglin - Dysregulated innate immunity contributes to clonal cytopenias and myeloid neoplasms, but its extent across disease stages and clinical relevance remain incompletely defined. We analyzed plasma ASC/NLRP3 double-positive (DP) specks, ASC single-positive (SP) specks, and 45 cytokines in 223 patients with idiopathic cytopenias of undetermined significance (ICUS)/clonal cytopenias of undetermined significance (CCUS), myelodysplastic syndromes (MDS), and chronic myelomonocytic leukemia (CMML) and 39 matched non-inflammatory controls using adjusted regression, survival modeling, and paired longitudinal analyses. Inflammasome activation and cytokine perturbations were evident across the disease spectrum. DP-ASC specks were elevated in MDS and CMML, whereas SP-ASC specks were increased across all groups, indicating activation of ASC-containing inflammasomes beyond NLRP3. Cytokines followed a graded ICUS → MDS → CMML pattern, with widespread upregulation of interleukins and chemokines (including IL-7, IL-8, IL-11/CXCL11, and CCL7) alongside suppression of stem and progenitor support factors such as CSF3, FLT3LG, TRAIL, and TWEAK. At baseline, elevated IL-15 and MMP1 predicted progression to acute myeloid Leukaemia, while higher IL-10, CXCL8, and IL-18 were associated with reduced survival; ASC specks were not independently prognostic. Longitudinal increases in selected cytokines distinguished progressors (area under the curve 0.82; 95% CI: 0.49-1.0). Cytokine patterns correlated with mutation categories, with the isolated SF3B1 mutation associated with higher DP-ASC specks. These findings define early and progressive inflammasome engagement and nominate dynamic cytokine panels and the inflammasome-IL-1 axis as actionable biomarkers and therapeutic targets. - Source: PubMed
Publication date: 2026/07/07
Ibbotson AliceCrouch SimonFerrari JacquelineYoung ThomasMorgan Ann WGallì AnnaPozzi SaraSarchi MartinaCamilotto VirginiaBoldini MartinaElena ChiaraMalcovati LucaSavic Sinisa - Diabetic retinopathy (DR) is a primary contributor to vision loss worldwide and growing with an increase in the elderly population. Preventing or slowing DR progression remains a critical unmet need. Recognizing the significance of systemic and ocular chronic inflammation in the progression of DR, this study aimed to explore inflammatory factor profiles in plasma and tear samples to identify potential biomarkers and therapeutic targets. - Source: PubMed
Publication date: 2026/06/30
Yu TingLi ShengCheng FangLiu YanziHou GuimeiDong JichengHuang YanjieQu ShoufangChen Qiong - Pancreatic ductal adenocarcinoma (PDAC) exhibits five-year survival rates below 10% with profound therapeutic resistance mediated by stromal barriers and immunosuppression (Rahib et al. 2014; Siegel et al. 2023). Recent evidence suggests intratumoral mycobiome alterations may influence disease progression (Aykut et al. 2019; Riquelme et al. 2019). We performed comprehensive network pharmacology analysis to elucidate potential therapeutic mechanisms of (SB) and Clostridium histolyticum collagenase (CHC) in PDAC. Gene sets representing immune activation, metabolic competition, and stromal remodeling were analyzed using protein-protein interaction networks (STRING v11.5), functional enrichment (DAVID v2021), and pathway databases (KEGG, Reactome, WikiPathways). Structural assessment employed blind molecular docking (CB-Dock2) and coarse-grained molecular dynamics (CABS-flex). Computational analysis was performed on TCGA-PAAD data (n = 177 patients, 93 events) using Cox regression and five machine learning classifiers (Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM) with Q1/Q3 survival stratification and strict fivefold nested cross-validation with within-fold feature selection (SelectKBest, k = 5). Random survival forest (RSF) incorporating 67 high-quality genes from four mechanistically curated modules was developed for time-to-event prediction, evaluated by AUC-ROC and Harrell's C-index. Permutation-based feature selection identified prognostic biomarkers. Network analysis identified TNF, IL6, IFNG, and TLR4 as central hubs (degree > 15) with dense interconnectivity (clustering coefficient = 0.78). Pathway enrichment revealed over-representation in IL-17, TLR, and IL-10 signaling (BH-FDR < 0.05). Molecular docking revealed high-affinity interactions with AKT1 (ΔG = - 7.2 kcal/mol), TLR2 (- 7.6 kcal/mol), TLR4 (- 6.9 kcal/mol), and KRAS-SOS1 interface (- 6.9 kcal/mol). Kaplan-Meier analysis demonstrated significant survival differences by composite immune-metabolic signature (log-rank = 0.0001) and immune sub-signature (CXCL9, CXCL10, CXCL11; = 0.0028). The metabolic sub-signature (LDHA, ALDH9A1) demonstrated a significant continuous prognostic effect by Cox regression (HR = 1.431, = 0.0292), with median dichotomisation not reaching significance (log-rank = 0.2164), indicating a gradient rather than threshold effect. Cox modelling of the composite five-gene score identified it as a strong negative prognostic factor (HR = 1.897, < 0.0001). Machine learning classifiers achieved good-to-strong discrimination of survival extremes under strict nested cross-validation (AUC range: 0.631-0.745; Logistic Regression: AUC = 0.745 ± 0.036), confirmed by permutation testing (n = 1000 permutations, empirical < 0.01). RSF modelling on the full cohort achieved C-index = 0.649 ± 0.040 using the top-5 gene signature, representing acceptable discrimination for PDAC. Permutation-based feature selection identified CXCL11, CXCL10, CXCL9, LDHA, and ALDH9A1 as top prognostic biomarkers. Tertile-based risk stratification demonstrated significant survival separation between Low and High Risk groups (log-rank = 0.0005). Kinetic modeling showed 4.2-fold SB competitive advantage under tumor hypoglycemia (< 1 mM glucose). Network analysis, structural modeling, and machine learning support biological plausibility of SB anti-tumor activity through immune activation and stromal remodeling. RSF modelling (C-index = 0.649) and Cox regression (HR = 1.897, < 0.0001) provide a computational framework for patient stratification. Identification of CXCL11, CXCL10, CXCL9, LDHA, and ALDH9A1 as prognostic biomarkers spanning immune and metabolic modules provides targets for clinical monitoring. This systems-level framework provides mechanistic rationale for clinical investigation of intratumoral as adjuvant PDAC therapy. - Source: PubMed
Publication date: 2026/06/29
Novruzov MuradRaval KevalMammadova MarziyyaKhan Waseem UllahKhan Inam UllahShiraliyeva UlkarSaleem Abdullah