KIF19 Antibody - middle region (ARP33952_P050)
- Known as:
- KIF19 Antibody - middle region (ARP33952_P050)
- Catalog number:
- arp33952_p050
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Aviva Systems Biology
- Gene target:
- KIF19 Antibody - middle region (ARP33952_P050)
Related genes to: KIF19 Antibody - middle region (ARP33952_P050)
- Gene:
- KIF19 NIH gene
- Name:
- kinesin family member 19
- Previous symbol:
- -
- Synonyms:
- FLJ37300, KIF19A
- Chromosome:
- 17q25.1
- Locus Type:
- gene with protein product
- Date approved:
- 2006-01-16
- Date modifiied:
- 2016-10-05
Related products to: KIF19 Antibody - middle region (ARP33952_P050)
Related articles to: KIF19 Antibody - middle region (ARP33952_P050)
- This study aims to explore potential biological biomarkers for brucellosis by integrating transcriptomic profiling and bioinformatics-driven approaches. - Source: PubMed
Publication date: 2026/02/04
Wang RuiHe JuanLi XiaoShi YueDuan HuijuanDing Haitao - Bladder exstrophy and epispadias complex (BEEC) is one of the most severe congenital malformations of the urogenital tract, significantly impacting continence, sexual function, and renal function. To date, the only recurrent genetic aberration identified is the 22q.11.2 microduplication, but several candidate regions and genes including components of the WNT signaling pathway have been proposed. This study aimed to identify additional genes contributing to the pathogenesis of BEEC and to verify previously suggested candidate genes. We performed trio-based whole genome sequencing on 19 individuals with BEEC and their unaffected parents; of those, five carried earlier reported microdeletions. The genome data was also filtered in silico for variants in 204 candidate genes selected from databases, publications, and in-house findings. Variants were prioritized based on allele frequency and predicted functional impact. In 8 of the 19 trios, our findings highlight members of the ADGR-gene family as novel candidate genes for BEEC, alongside other implicated genes such as TRANK1, CSNK1E, IFT122, SDK1, SDK2, and KIF19 and propose two more CNVs as risk factors for BEEC; on chromosome regions 1p36 and 16p11.2. This study identifies novel candidate genes for BEEC within the ADGR gene family. The results also further implicate a complex molecular background of BEEC. - Source: PubMed
Publication date: 2026/02/10
Nordenskjöld AgnetaAlm SamaraEisfeldt JesperCao JiaAnderberg MagnusBarker GillianMatsson HansHolmdahl GundelaLindstrand AnnaLagerstedt-Robinson Kristina - The COVID-19 pandemic has had a devastating impact, with more than 7 million deaths worldwide. Advanced age and comorbidities partially explain severe cases of the disease, but genetic factors also play a significant role. Genome-wide association studies (GWASs) have been instrumental in identifying loci associated with SARS-CoV-2 infection. Here, we report the results from a >820 K variant GWAS in a COVID-19 patient cohort from the hospitals associated with IIS Biobizkaia. We compared intensive care unit (ICU)-hospitalized patients with non-ICU-hospitalized patients. The GWAS was complemented with an integrated phenotype and genetic modeling analysis using HLA genotypes, a previously identified COVID-19 polygenic risk score (PRS) and clinical data. We identified four variants associated with COVID-19 severity with genome-wide significance (rs58027632 in KIF19; rs736962 in HTRA1; rs77927946 in DMBT1; and rs115020813 in LINC01283). In addition, we designed a multivariate predictive model including HLA, PRS and clinical data which displayed an area under the curve (AUC) value of 0.79. Our results combining human genetic information with clinical data may help to improve risk assessment for the development of a severe outcome of COVID-19. - Source: PubMed
Publication date: 2025/03/10
Alloza-Moral IraideAldekoa-Etxabe AneTulloch-Navarro RaquelFiat-Arriola AinhoaMar CarmenUrrechaga EloisaPonga CristinaArtiga-Folch IsabelGarcia-Bediaga NaiaraAspichueta PatriciaMartin CesarZarandona-Garai AitorPérez-Fernández SilviaArana-Arri EunateTriviño Juan-CarlosUranga AneEspaña Pedro-PabloVandenbroeck-van-Caeckenbergh Koen - Congenital anomalies of the kidney and urinary tract (CAKUT) are the most common cause of chronic kidney disease in the first 3 decades of life. Over 40 genes have been identified as causative for isolated human CAKUT. However, many genes remain unknown, and the prioritization of potential CAKUT candidate genes is challenging. To develop an independent approach to prioritize CAKUT candidate genes, we hypothesized that monogenic CAKUT genes are most likely co-expressed along a temporal axis during kidney development and that genes with coinciding high expression may represent strong novel CAKUT candidate genes. - Source: PubMed
Publication date: 2023/07/27
Schierbaum Luca MSchneider SophiaBuerger FlorianHalawi Abdul AzizSeltzsam SteveWang ChunyanZheng BixiaWu Chen-Han WilfriedDai RufengConnaughton Dervla MSalmanullah DaanyaNakayama MakikoMann NinaShril ShirleeHildebrandt Friedhelm - The prognosis of pancreatic cancer is poor because patients are usually asymptomatic in the early stage and the early diagnostic rate is low. Therefore, in this study, we aimed to identify potential prognosis-related genes in pancreatic cancer to improve diagnosis and the outcome of patients. The mRNA expression profile data from The Cancer Genome Atlas database and GSE79668, GSE62452, and GSE28735 datasets from Gene Expression Omnibus were downloaded. The prognosis-relevant genes and clinical factors were analyzed using Cox regression analysis and the optimal gene sets were screened using the Cox proportional model. Next, the Kaplan-Meier survival analysis was used to evaluate the relationship between risk grouping and patient prognosis. Finally, an optimal gene-based prognosis prediction model was constructed and validated using a test dataset to discriminate the model accuracy and reliability. The results showed that 325 expression variable genes were identified, and 48 prognosis-relevant genes and three clinical factors, including lymph node stage (pathologic N), new tumor, and targeted molecular therapy were preliminarily obtained. In addition, a gene set containing 16 optimal genes was identified and included FABP6, MAL, KIF19, and REG4, which were significantly associated with the prognosis of pancreatic cancer. Moreover, a prognosis prediction model was constructed and validated to be relatively accurate and reliable. In conclusion, a gene set consisting of 16 prognosis-related genes was identified and a prognosis prediction model was constructed, which is expected to be applicable in the clinical diagnosis and treatment guidance of pancreatic cancer in the future. - Source: PubMed
Chen ZhiqinSong HaifeiZeng XiaochenQuan MingGao Yong