Ask about this productRelated genes to: KCNB1 Blocking Peptide
- 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 Blocking Peptide
Related articles to: KCNB1 Blocking Peptide
- Urinary proteomic profiling (UPP) provides insights in disease mechanisms and origin of symptoms. Using UPP, this study aimed at deepening insight in the biology of exercise tolerance. - Source: PubMed
Publication date: 2026/07/30
Liu Chu-HaoMartens Dries SAn De-WeiSiwy JustynaLatosinska AgnieszkaPellicori PierpaoloVerdonschot Job A JAhmed Fozia ZWei Fang-FeiRossignol PatrickPetutschnigg JohannesHeymans StephaneCuthbert Joe JYu Yu-LingGirerd NicolasClark Andrew LVerhamme PeterZhang Dong-YanLi YanNawrot Tim SCleland John GZannad FaiezMischak HaraldStaessen Jan A - Obesity and related metabolic disorders represent a major global health burden, yet their genetic determinants remain incompletely characterized, particularly in non-European populations. We aimed to identify body mass index (BMI)-associated loci in an admixed Brazilian population and to functionally characterize ancestry-enriched variants contributing to obesity risk. - Source: PubMed
Publication date: 2026/05/07
Teixeira Samantha KuwadaLaurindo Caroline PanceraSato Fábio TakeoZuleta Luiz Fernando GodaDuarte Nubia EJensen Ana Vitória VPaço Samantha L GPereira Alexandre CSakabe NoboruNobrega MarceloKrieger Jose Eduardo - Sleep disturbances are frequent comorbidities in epilepsies and developmental encephalopathies. This study aimed to characterize sleep abnormalities in individuals with KCNB1-related disorders, focusing on their prevalence, clinical manifestations, and impact on daily functioning. - Source: PubMed
Publication date: 2026/05/21
Scorrano GiovannaBreuillard DelphineThimon AnaisChemaly NicoleNabbout Rima - 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