KCNB1 _ DRK1
- Known as:
- KCNB1 _ DRK1
- Catalog number:
- Y213861
- Product Quantity:
- 200ul
- Category:
- -
- Supplier:
- ABM
- Gene target:
- KCNB1 _ DRK1
Related genes to: KCNB1 _ DRK1
- Gene:
- KCNB1 NIH gene
- Name:
- potassium voltage-gated channel subfamily B member 1
- Previous symbol:
- -
- Synonyms:
- Kv2.1
- Chromosome:
- 20q13.13
- Locus Type:
- gene with protein product
- Date approved:
- 1991-08-13
- Date modifiied:
- 2016-10-05
Related products to: KCNB1 _ DRK1
Related articles to: KCNB1 _ DRK1
- This systematic review aimed to summarize recent progress in precision medicine for all studied potassium gene variants related to epilepsy. It analyzed studies conducted in cell and animal models and in humans. - Source: PubMed
Publication date: 2026/04/16
Xie ChangningYin FeiKessi MiriamPeng Jing - 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 - This study aims to rigorously evaluate the consistency and reliability of a pluripotent stem cell (PSC) differentiation system and explore how the mutation disrupts the temporal regulation of gene expression during neuronal differentiation and modulates neuron function-related pathways. - Source: PubMed
Publication date: 2026/01/28
Guo YufanWu LifangYe DanfengLin XuetingJin YutingZhang ChudiLou YutingMiao PuWang YeZhang BijunFeng Jianhua - The potassium voltage-gated channel Kv2.1 plays a crucial role in the development of the brain's ventricular system. Defects in the development of this system affect the formation of the Reissner fiber, a rope-like structure produced by the flexural and subcommissural organs that secrete Scospondin. - Source: PubMed
Publication date: 2026/01/05
Amini R RosaJain Ruchi PJędrychowska JustynaKorzh Vladimir - Schizophrenia, a debilitating neuropsychiatric disorder with profound socioeconomic consequences, manifests characteristic cortical thinning patterns observable through neuroimaging. While structural magnetic resonance imaging (MRI) studies consistently demonstrate these anatomical disturbances, their molecular signatures remain poorly understood. - Source: PubMed
Publication date: 2026/01/03
Tu YeLi ShenruiGuan ShaodiXin YueyangTao HongZhou ZhiqiangWang ShaofangJiang HongweiXu Hui