DCXR polyclonal antibody (A01)
- Known as:
- DCXR pab (anti-) (A01)
- Catalog number:
- H00051181-A01
- Product Quantity:
- 50 uL
- Category:
- -
- Supplier:
- Abno
- Gene target:
- DCXR polyclonal antibody (A01)
Related genes to: DCXR polyclonal antibody (A01)
- Gene:
- ABRAXAS2 NIH gene
- Name:
- abraxas 2, BRISC complex subunit
- Previous symbol:
- KIAA0157, FAM175B
- Synonyms:
- Em:AC068896.4, ABRO1
- Chromosome:
- 10q26.13
- Locus Type:
- gene with protein product
- Date approved:
- 2004-03-16
- Date modifiied:
- 2017-04-27
- Gene:
- AKR1C3 NIH gene
- Name:
- aldo-keto reductase family 1 member C3
- Previous symbol:
- HSD17B5
- Synonyms:
- KIAA0119, DDX, HAKRB, PGFS
- Chromosome:
- 10p15.1
- Locus Type:
- gene with protein product
- Date approved:
- 1998-09-29
- Date modifiied:
- 2016-10-05
- Gene:
- ARHGAP4 NIH gene
- Name:
- Rho GTPase activating protein 4
- Previous symbol:
- -
- Synonyms:
- KIAA0131, C1, p115, RhoGAP4, SrGAP4
- Chromosome:
- Xq28
- Locus Type:
- gene with protein product
- Date approved:
- 1997-08-28
- Date modifiied:
- 2015-09-11
- Gene:
- ARHGEF7 NIH gene
- Name:
- Rho guanine nucleotide exchange factor 7
- Previous symbol:
- -
- Synonyms:
- KIAA0142, PIXB, DKFZp761K1021, Nbla10314, DKFZp686C12170, BETA-PIX, COOL1, P85SPR, P85, P85COOL1, P50BP, PAK3, P50
- Chromosome:
- 13q34
- Locus Type:
- gene with protein product
- Date approved:
- 2001-11-21
- Date modifiied:
- 2016-10-05
- Gene:
- BCLAF1 NIH gene
- Name:
- BCL2 associated transcription factor 1
- Previous symbol:
- -
- Synonyms:
- KIAA0164, BTF
- Chromosome:
- 6q23.3
- Locus Type:
- gene with protein product
- Date approved:
- 2004-01-13
- Date modifiied:
- 2017-06-09
Related products to: DCXR polyclonal antibody (A01)
Related articles to: DCXR polyclonal antibody (A01)
- Digital chest X-ray (dCXR) enables early TB detection where symptoms screening is inadequate. We evaluated the impact and cost of three dCXR delivery models: long-term community-based mobile vans (LT-vans), long-term facility-based containers (LT-containers), and short-term community-based mobile vans (ST-vans), implemented by a non-governmental organisation (NGO) across two South African provinces. - Source: PubMed
Publication date: 2026/09/15
Coetzee LEvans DFononda AHausler HBooyens LSteingo JHirasen KJamieson LKubjane MMeyer-Rath G - Mitochondrial dysfunction is linked to sleep disorders in previous report, but the potential roles of specific genes remain unclear. This study aimed to dissect different subtype-specific genetic associations and their underlying mechanisms. A multi-omics Summary-data-based Mendelian Randomization (SMR) approach was performed to identify potential causal links between mitochondrial function-related genes and sleep disorders. We integrated GWAS data from FinnGen database (the discovery set), independent GWAS datasets (covering different sleep-disorder subtypes and used for validation), and cis-QTLs (including mQTLs, eQTLs, and pQTLs) to perform systematic exploration. Specially, we performed targeted validation of tissue-specific effects, leveraging gene expression data from disease-relevant brain regions within the GTEx database. Our SMR analysis identified mitochondrial function-related genes potentially modulating sleep disorders across biological layers, initially identifying 102 genes at the methylation level, 48 at the gene expression level, and 6 at the protein abundance level. Integrative analysis subsequently prioritized DCXR and ACADVL and revealed their distinct, subtype-specific associations. DCXR exhibited a protective role in sleep apnea while ACADVL showed a paradoxical risk conferring role in daytime sleepiness. In addition, the analysis identified an epigenetic regulatory mechanism for DCXR in which its expression and protein levels are modulated by DNA methylation. Finally, validation in brain-hypothalamus tissue confirmed DCXR as a significant potential protective factor (OR = 0.929, 95% CI: 0.887-0.973, P_HEIDI = 0.999, FDR = 0.2449). Our findings implicate key mitochondrial genes, particularly DCXR and ACADVL, in the pathophysiology of specific sleep disorder subtypes, highlighting potential avenues for precision medicine. Clinical trial number: Not applicable. - Source: PubMed
Publication date: 2026/09/12
Xu JunjunZhang ZiyanYu LiuyangFeng Yi - Tuberculosis (TB) remains the leading cause of death among persons with advanced HIV disease (AHD) in high HIV-burden settings. Digital chest X-ray (dCXR) with computer-aided detection (CAD) is a promising tool to overcome human resource constraints and improve TB case detection. This study evaluates the real-world implementation and performance of dCXR/CAD for TB screening within a specialized AHD clinic in Maputo, Mozambique. We conducted a retrospective cohort analysis of 487 new AHD patients at Centro de Referência do Alto Maé (CRAM) from October 2023 to September 2024. Of these, 238 underwent dCXR with CAD interpretation. All patients underwent systematic TB screening according to Ministry of Health (MoH) guidelines. Using the recorded diagnosis of TB (bacteriologically confirmed or clinically diagnosed) as the reference standard, we calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the nationally adopted CAD threshold (≥0.5). Among 238 AHD patients screened with CAD, 116 (49%) were diagnosed with TB. At the ≥0.5 threshold, sensitivity was 50% (58/116; 95% CI: 41-59), specificity 92% (112/122; 95% CI: 85-96), PPV 85% (58/68; 95% CI: 75-92), and NPV 65.9% (112/170; 95% CI: 58-73). TB diagnosis rates increased sharply with CAD score: 30% (43/143) in normal, 52% (15/27) in abnormal non-suggestive, and 85% (58/68) in suggestive cases. Bacteriological confirmation was low across all groups (19-26%), reflecting reliance on clinical diagnosis. Integrating dCXR/CAD into AHD care is feasible and identifies a high TB burden. However, at the adopted threshold of ≥0.5, CAD demonstrated high specificity but low sensitivity (50%) in this population, missing half of all TB cases. These findings suggest that CAD functions better as a confirmatory decision-support tool than a standalone screening test in AHD. Threshold optimization for this specific population warrants prospective evaluation. Implementation challenges including fragmented systems and lack of dedicated human resources must be addressed to realize CAD's full potential in TB programs. - Source: PubMed
Publication date: 2026/07/30
Ruano Camps MariaJose BenditaZindoga PereiraCouto AlenyCumbe CeliaMuvale GilBene RosaCossa Admilson FShapiro Adrienne ELane JeffMudender FlorindoNacarapa Edy - Gestational diabetes mellitus (GDM) poses significant health risks, yet the causal genetic and epigenetic mechanisms linking glycolipid metabolism dysregulation to GDM remain elusive. This study aimed to identify key causal genes and regulatory pathways by integrating multi-omics data with large-scale genetic association studies. - Source: PubMed
Publication date: 2026/05/20
Lin XiaoxiaoZheng JingjingQin NingningLi Yimei - Recent evidence has established a significant link between N4 acetylcytidine (ac4C) mRNA modification, mediated by N Acetyltransferase 10 (NAT10), and bone metabolism. Nonetheless, the precise role and regulatory targets of NAT10, along with its associated ac4C modification in human bone formation, remain inadequately characterized. This study employed bioinformatics analysis of transcriptomic datasets from primary osteoblasts of individuals with high versus low bone mineral density (BMD), alongside a curated set of ac4C-modified genes, to identify key differentially expressed genes (DEGs) regulated by this pathway within an osteogenic context. Overall, eleven key NAT10/ac4C-associated DEGs linked to BMD status were identified: CFD, CTSF, DCXR, FADS1, GOLIM4, IMPA2, MLEC, NCLN, NT5DC2, PTGFRN, and VASP. Notably, FADS1, NT5DC2, and PTGFRN emerged as crucial ac4C-modified genes across three machine learning models. Furthermore, the tri-gene signature (FADS1/NT5DC2/PTGFRN) showed excellent diagnostic performance in distinguishing different BMD statuses. In vitro validation using MC3T3-E1 osteoblastic cells revealed that the knockdown of NAT10 via lentiviral delivery markedly impaired cell proliferation and osteogenic differentiation. This impairment was evidenced by the results of the CCK-8 proliferation assay, alkaline phosphatase staining, and Alizarin Red staining. Additionally, qRT PCR analysis demonstrated a significant downregulation of FADS1 and NT5DC2 expression subsequent to NAT10 knockdown. These findings underscore the role of NAT10-mediated ac4C modification as a pivotal regulator of osteoblast activity and gene expression programs associated with BMD. This research offers novel insights into the regulation of bone metabolism and proposes potential diagnostic markers and therapeutic targets for osteoporosis. - Source: PubMed
Tang YWang QDong WJiang GLei MHu XWu YJiang WHao JHu Z