Polyclonal Rabbit APOF Antibody
- Known as:
- Polyclonal Rabbit APOF Antibody
- Catalog number:
- KA0250
- Product Quantity:
- 100ul
- Category:
- -
- Supplier:
- KareBay
- Gene target:
- Polyclonal Rabbit APOF Antibody
Related genes to: Polyclonal Rabbit APOF Antibody
- Gene:
- APOF NIH gene
- Name:
- apolipoprotein F
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 12q13.3
- Locus Type:
- gene with protein product
- Date approved:
- 1994-11-16
- Date modifiied:
- 2016-10-05
Related products to: Polyclonal Rabbit APOF Antibody
Related articles to: Polyclonal Rabbit APOF Antibody
- Circulating proteomics acts as an intermediate phenotype linking genetic susceptibility to MASLD. However, current evidence rarely establishes a direct concordance between serum protein levels and hepatic gene expression. We aimed to perform a multi-cohort joint analysis of serum proteomics and transcriptomics to characterize essential molecular features for MASLD. - Source: PubMed
Publication date: 2026/04/17
Xu JinjianGou WanglongWang XinyueRu DongmeiHu WeiChen JietengLi Bang-YanXi YueZheng Ju-ShengChen Yu-Ming - Heart failure (HF) and its main subtypes, heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), impose an enormous health burden on elders. Assessment of the circulating proteome to illuminate pathogenesis could open new opportunities for treatment. - Source: PubMed
Publication date: 2026/02/04
Njoroge Joyce NSanders van Wijk SandraAustin Thomas RBrody Jennifer ASitlani Colleen MHamerton EmilyBis Joshua CHenry AlbertLumbers R Thomas Seshaiah TaliaShojaie AliYang YiminLamberson VictoriaYu BingShah Amil MBansal NishaShah Sanjiv JTracy Russell PGerszten Robert EJennings Lori LGudmundsdottir ValborgGudnason VilmundurEmilsson ValurPsaty Bruce MKizer Jorge R - Tumor staging is critical for guiding therapeutic decisions and determining prognosis in liver hepatocellular carcinoma (LIHC). This study aimed to identify potential tissue biomarkers intrinsically linked to disease stage to enhance our understanding of LIHC biology. - Source: PubMed
Publication date: 2026/01/12
Xue WeinaXie ShuyingWu TongLi RuixiLu DingyanChen ShuaishuaiXu YueWang Yonglin - South Asians (SAs) in the UK are at an increased risk of cardiovascular disease (CVD), develop type 2 diabetes mellitus at a lower age and body mass index, and have a lower high-density lipoprotein cholesterol (HDL-C) concentration than their white European (EU) counterparts. The failure of HDL-C raising therapies for CVD risk reduction has turned attention to its composition and function. A previous study comparing the effect of moderate weight gain on SA and EU men found baseline and weight gain-induced ethnic differences in body composition, adipocyte function and insulin resistance (ClinicalTrials.gov registration: NCT02399423). This study investigated differences in HDL protein composition, subclass distribution and in vitro vascular functions at baseline and after weight gain in the same cohort of men. HDL protein composition was determined by nano liquid chromatography tandem mass spectrometry using label-free quantification. HDL subclass distribution was measured by native gel electrophoresis. HDL in vitro paraoxonase-1 (PON-1) activity was measured by monitoring the PON-1 mediated hydrolysis of phenylacetate. In vitro HDL anti-inflammatory function was assessed in an endothelial cell assay of adhesion molecule inhibition. SAs had higher levels of immunity- and inflammation-related proteins and a detrimental profile of lipid metabolism-related proteins at baseline and with weight gain (including lower apolipoprotein (apo) A-IV and apoF and higher apoC-III) compared with EU. HDL subclass distribution and in vitro vascular function were not different between EUs and SAs. HDL protein composition reflects systemic physiology and acts as a mechanistic marker of impaired lipid metabolism in SAs. - Source: PubMed
Beazer Jack DMcLaren JamesChristoffersen ChristinaFerraz Maria JMulder Monique TGraham DelythKarlsson HelenLjunggren Stefan AGill Jason M RFreeman Dilys J - The escalating annual death toll attributed to Cholangiocarcinoma (CCA) is, in part, a consequence of delayed diagnosis. This study developed an optimal CCA diagnostic model through the application of 11 machine-learning algorithms. Initially, 105 differentially expressed genes (DEGs) were identified by analyzing gene expression profiles from 307 CCA tumor tissues and 124 adjacent non-tumor tissues. WGCNA, F-test, characteristic importance, and Lasso regression analysis were employed to identify key DEGs, including APOF, DIO1, APOM, and OTC. Subsequently, diagnostic models were constructed based on APOF, DIO1, and OTC using 11 machine-learning algorithms. The LightGBM algorithm was determined as the optimal model through ROC curve analysis and machine learning performance evaluation, achieving an AUC of 0.84, with accuracy, precision, and recall values of 0.80, 0.83, and 0.90, respectively. Subsequent analyses included gene enrichment, protein-protein interaction (PPI), and CCA-related drug assessments. Additionally, the study revealed an imbalance in immune cell infiltration in CCA and identified CCL16 as a chemokine involved in immunoregulation. RT-qPCR confirmed that APOF, DIO1, and OTC were significantly downregulated in CCA tumor tissues. In conclusion, this research provides new directions for the diagnosis and immunotherapy of this disease. - Source: PubMed
Publication date: 2025/12/02
Zhang ZeyuGeng XueyanYin MaopengLiang YongyuanZheng Guixi