Ask about this productRelated genes to: MAPK3 Blocking Peptide
- Gene:
- MAPK3 NIH gene
- Name:
- mitogen-activated protein kinase 3
- Previous symbol:
- PRKM3
- Synonyms:
- ERK1, p44mapk, p44erk1
- Chromosome:
- 16p11.2
- Locus Type:
- gene with protein product
- Date approved:
- 1993-11-05
- Date modifiied:
- 2015-09-03
Related products to: MAPK3 Blocking Peptide
Related articles to: MAPK3 Blocking Peptide
- Qinggan Jiangzhi Cha (QGJZC), a compound formulation rooted in Traditional Chinese Medicine, is traditionally employed to clear heat, soothe the liver, and reduce lipid accumulation to alleviate hepatic stagnation and indigestion, aligning with modern NAFLD therapeutic strategies targeting lipid metabolism and inflammation. - Source: PubMed
Publication date: 2026/05/02
Bai YuTian ZhiliLi YinGuo XinjianGao HuiLiu YitaoMa JunLiu Fanrong - : Patients with type 2 diabetes mellitus (T2DM) undergoing cardiac surgery represent a high-risk population characterized by substantial cardiometabolic stress and increased susceptibility to postoperative heart failure, renal dysfunction, and unplanned rehospitalization. Although sodium-glucose cotransporter 2 (SGLT2) inhibitors provide established cardiorenal protection in ambulatory populations, their perioperative impact in cardiac surgery cohorts remains insufficiently defined. : In a single-center retrospective cohort of 620 T2DM patients, inverse probability of treatment weighting and time-dependent Cox regression were applied to account for perioperative treatment interruption and delayed postoperative reinitiation when evaluating the association between chronic SGLT2 inhibitor therapy and 12-month rehospitalization risk. To provide biological context for the observed clinical associations, target-driven systems pharmacology, molecular docking against SGLT2, NHE1, AMPK, and NLRP3, and protein-protein interaction (PPI) network analysis were performed. Hub proteins were identified using Maximal Clique Centrality, followed by functional enrichment (GO/KEGG) analysis. : Chronic SGLT2 inhibitor therapy was associated with reduced first rehospitalization (HR 0.64; 95% CI 0.48-0.85; = 0.002) and a lower cumulative rehospitalization burden (IRR 0.61; 95% CI 0.46-0.82; = 0.001), primarily driven by heart failure-related and metabolic phenotypes. Molecular docking analyses identified favorable binding with SGLT2 and additional cardiometabolic and inflammatory targets, including NHE1, AMPK, NLRP3, IKKβ, IL-6Rα, and PPAR isoforms, suggesting modulation of myocardial ion homeostasis, metabolic resilience, and inflammatory signaling. PPI analysis identified eight hub proteins (AKT1, MTOR, STAT3, EGFR, PIK3CA, SRC, MAPK1, and MAPK3) significantly enriched in PI3K/AKT, MAPK/ERK, and ErbB signaling pathways. : Chronic SGLT2 inhibitor therapy was independently associated with reduced postoperative rehospitalization and cumulative event burden in T2DM patients undergoing cardiac surgery. Integrated in silico analyses offer mechanistic hypotheses consistent with the observed clinical associations. These findings suggest that structured perioperative SGLT2 inhibitor management may contribute to improved postoperative outcomes, while prospective validation in future studies would strengthen these findings. However, given the retrospective observational design, these findings should be interpreted as associative rather than causal. - Source: PubMed
Publication date: 2026/04/10
Onar Lutfi CagatayGuner ErsinYilmaz Ibrahim - Information on the autopolyploid of remains limited until now. Previously, the autotetraploid of was successfully generated via colchicine-induced chromosome doubling from the diploid cultivar 'Hongxing' in our lab. - Source: PubMed
Publication date: 2026/04/17
Feng LiliWang LexiangLi JiaminLi XianglongRong ErhuaWu Yuxiang - Osteoarthritis (OA) is a complex degenerative joint disease for which early diagnosis and clear molecular characterization remain limited. DNA methylation has been increasingly recognized as an important regulatory factor in OA pathogenesis. In this study, we proposed an integrative computational framework combining statistical analysis, machine learning, deep learning, and functional genomics to identify and validate OA-associated genes and methylation biomarkers for diagnostic and biological interpretation. Candidate CpG sites were obtained using two complementary strategies: differential methylation analysis and selection of loci located near transcription start sites of previously reported OA-related genes. Key features were further refined using support vector machine recursive feature elimination and random forest algorithms. Based on the selected loci, we developed a feature-fusion diagnostic model that combines Transformer and convolutional neural networks with adaptive weighting to capture both global dependency structures and local methylation patterns. A panel of 220 methylation sites demonstrated stable and reproducible diagnostic performance in an independent cohort. Functional annotation and pathway analysis highlighted several established OA-associated genes, including , , , and , and suggested as a potential novel effector gene, with additional support for and involvement. Overall, this study presents a robust methylation-based framework for identifying key OA-associated genes and provides new insights into the epigenetic mechanisms underlying OA. - Source: PubMed
Publication date: 2026/04/09
Zhao JianWu ChangwuKuang ZhejunWang HanShi Lijuan - Pancreatic cancer is a major cause of death and one of the most challenging types of cancer which responds poorly to conventional chemotherapy and has limited therapeutic options. The scenario highlights the urgent need for the development of newer multi-targeting anticancer drugs for pancreatic cancers with higher potency, selectivity and safety profiles. The proposed research was focused on revealing the anticancer potential and mechanistic involvement of naturally occurring sesquiterpenoid nootkatone against pancreatic cancer through an integrative in silico approach. Network pharmacology and systems biology approaches were applied to identify common therapeutic targets for nootkatone that were pathophysiologically associated with the progression of pancreatic cancer. Protein-protein interaction (PPI), Gene Ontology (GO), and KEGG enrichment analyses were executed to validate the target's involvement in the pathophysiology of pancreatic cancer. Molecular docking and dynamics simulations were performed to reveal the binding potential and interactions of the nootkatone with the shortlisted anticancer targets, followed by density functional theory (DFT) analysis, ADMET prediction, and clinical relevance assessment to confirm its electrochemical and physicochemical involvement. A total of 27 overlapping targets were identified with AKT1, MAPK3, IL1β, and COX-2 revealed as the four hub target genes for nootkatone by network pharmacology. Enrichment analyses confirmed the significant involvement of PD-L1/PD-1 immune checkpoint, PI3K-AKT, MAPK, and inflammatory signaling pathways (FDR < 0.05) in the progression of pancreatic cancer. Docking analyses revealed that nootkatone has binding affinity for its targets, especially for MAPK3/ERK1 (- 8.03 kcal/mol) and AKT1 (- 7.35 kcal/mol). MD simulation over 100 ns demonstrated the stable protein-ligand complexes. DFT calculations showed a HOMO-LUMO energy gap of 2.868 eV, indicating moderate chemical reactivity and stability. Nootkatone was found to exert stronger and more stable binding against all four concerned anticancer targets with impressive electrochemical properties. ADMET predictions suggested favourable drug-likeness and Pharmacokinetics. Nootkatone can be a potential multi-targeting agent with anticancer and immunomodulatory properties by modulating the PD-L1/PD-1 immune checkpoint and signalling pathways associated with pancreatic cancer progression. However, these findings are based on in silico analyses and require further validation through in vitro and in vivo experimental studies to develop new plant-based anticancer therapeutics for pancreatic cancer. - Source: PubMed
Publication date: 2026/05/01
Goel KaranSingh Thakur GurjeetMujwar Somdutt