S100A13, human, 15N
- Known as:
- S100A13, H. sapiens, 15N
- Catalog number:
- 201SA13_N
- Category:
- -
- Supplier:
- ProtEra
- Gene target:
- S100A13 human 15N
Related genes to: S100A13, human, 15N
- Gene:
- S100A13 NIH gene
- Name:
- S100 calcium binding protein A13
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 1q21.3
- Locus Type:
- gene with protein product
- Date approved:
- 1997-10-30
- Date modifiied:
- 2016-10-05
Related products to: S100A13, human, 15N
Related articles to: S100A13, human, 15N
- Cerebral small vessel disease (SVD), manifesting as white matter hyperintensities (WMH), lacunar infarctions, and cerebral microbleeds on magnetic resonance imaging (MRI), has been linked to developmental epigenetic alterations. This study aimed to identify and validate gene-specific promoter methylation changes as epigenetic markers associated with SVD, using MRI-defined imaging features and blood inflammatory cells. - Source: PubMed
Publication date: 2026/03/12
Kim JeeyeonPark JihyeKang KeunsooLee Young HoShin Byoung-SooKim Dae-HyunShin Dong-IckAhn Seong HwanKim Jae GukKang Hyun GooJeong HyeseonYum Kyu SunChae Hee-YunKim Do-HyungKim Jei - Alzheimer's disease (AD) is a common neurodegenerative disorder in the elderly population, and early screening can effectively delay the progression of the disease. Mild cognitive impairment (MCI) occurs prior to the onset of AD; however, the accuracy of existing MCI-to-AD prediction methods remains relatively low. Additionally, small sample sizes and high feature dimensions often lead to model overfitting, highlighting the need for effective early screening approaches. To address the aforementioned issues, this study integrated non-paired multi-modal features-including clinical indicators from the ADNI database, blood biomarkers, brain region volume features extracted from MRI, and genetic biomarkers from the GEO database-and proposed a gender-corrected random matching strategy. The Random Forest algorithm was adopted to evaluate this strategy, analyze feature importance, and compare the performance of 9 machine learning algorithms based on the top 40 ranked features. The predictive performance of multi-modal data was superior to that of single-modal data, and the proposed strategy achieved favorable results in early AD screening. 16 specific genetic features (e.g., IFI27, EDF1, RAP2A, KIF5C, SERPINA3, FBXW7, IFITM1, ISG15, PSMB3, APOE4, KCNB1, PSPH, HMGN2, S100A13, IFIT3, and CALM1) and 6 brain region volume features ranked high in terms of importance. When validated using paired datasets from ADNI across the 9 algorithms, ensemble learning models demonstrated significantly stronger fitting capabilities. The non-paired multi-modal fusion approach not only expands the sample size but also enhances the generalization ability and robustness of the model. This provides a theoretical basis for the application of this strategy in the field of small-sample medical research. - Source: PubMed
Publication date: 2026/03/06
Zhang ZhihaoZhang RuixiaYang WenzhongLv KeWu MiaoXu Lianghui - Osteosarcoma is a highly aggressive primary bone malignancy in which distant metastasis critically determines patient prognosis. However, the molecular drivers of metastasis remain incompletely understood, making the elucidation of these drivers a research priority. - Source: PubMed
Publication date: 2026/03/05
Tang FuxingWang YishengFeng TaoZhou ShuhengZeng XianghongHuang PiweiPeng JinhuiLiang Bowei - Skeletal muscle is essential for metabolic health and physical function. While resistance training promotes muscle hypertrophy, alternative therapeutic strategies are needed for individuals unable to engage in physical activity. Because beta-adrenergic stimulation induces muscle growth without mechanical load, we assessed muscle fibre type-specific proteomic adaptations to prolonged beta-adrenergic stimulation and resistance training to decipher shared and distinct remodelling patterns. - Source: PubMed
Jessen SørenDi Credico AndreaMoreno-Justicia RogerMoesgaard LukasLemminger AndersStocks BenDi Baldassarre AngelaBangsbo JensDeshmukh Atul SHostrup Morten - N-acetylcytidine (acC) is an emerging epitranscriptomic modification that regulates mRNA stability and translation, yet its biological and clinical relevance in cholangiocarcinoma (CCA) remains unclear. - Source: PubMed
Publication date: 2026/01/23
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