a1BG ELISA kit
- Known as:
- a1BG Enzyme-linked immunosorbent assay test reagent
- Catalog number:
- DL-a1BG-Hu
- Product Quantity:
- 96T
- Category:
- Elisa Kits
- Supplier:
- WDSTD
- Gene target:
- a1BG ELISA kit
Related genes to: a1BG ELISA kit
- Gene:
- A1BG NIH gene
- Name:
- alpha-1-B glycoprotein
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 19q13.43
- Locus Type:
- gene with protein product
- Date approved:
- 1989-06-30
- Date modifiied:
- 2015-07-13
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- Blood-Heat syndrome is a core syndrome of Traditional Chinese Medicine (TCM) in Henoch-Schönlein purpura nephritis (HSPN), yet its biological basis remains unclear. This study aimed to systematically elucidate the scientific basis of Blood-Heat syndrome within the context of HSPN and to identify its objective biomarkers using a multidimensional biological approach. - Source: PubMed
Publication date: 2026/04/10
Xu ShuangXu YanBi YuefengDing YingZhang XiaXi LeyingRen Xianqing - Chronic hepatitis B virus (HBV) infection is a major risk factor of hepatocellular carcinoma (HCC), and hepatocyte-derived host factors play important roles in HBV-associated tumor progression. Alpha-1B glycoprotein (A1BG) is a plasma glycoprotein reported to be dysregulated in multiple cancers. In this study, we investigated the functional role of A1BG in HBV-associated HCC progression. - Source: PubMed
Publication date: 2026/02/18
Lyu JuanNosaka TakutoMurata YosukeAkazawa YuTanaka TomokoTakahashi KazutoNaito TatsushiOhtani MasahiroZhang LihongNakamoto Yasunari - Diabetic nephropathy (DN) represents the predominant microvascular complication associated with diabetes mellitus; however, existing diagnostic techniques are inadequate. This study evaluated candidate urinary protein biomarkers for diagnosing DN. A cohort comprising 59 patients with type 2 diabetes, 60 patients with DN, and 60 healthy volunteers was recruited. Urine proteomics was utilized to investigate differential protein expression levels among various patient groups and to identify potential biomarkers in conjunction with data analysis from the gene expression omnibus database. Machine learning classification methods were utilized to construct differential diagnosis models for DN. The data set IPX0003092000 was used to validate these diagnostic models. Six potential biomarkers─SERPINF1, FABP4, CP, CFB, C4A, and A1BG─were identified. The diagnostic models for DN, constructed by using machine learning algorithms, demonstrated robust diagnostic performance. Notably, models employing the glmnet, plr, and ranger classification methods achieved AUC values exceeding 0.800 in both the training and test data sets. In the validation cohort, the AUC values for models constructed using the ranger, glmnet, and plr methods were 0.928, 0.942, and 0.850, respectively. We evaluated six candidate urinary biomarkers (SERPINF1, FABP4, CP, CFB, C4A, and A1BG) using urinary proteomics and developed a diagnostic model for DN using machine learning algorithms. - Source: PubMed
Publication date: 2026/02/13
Yu JiangenZhou DiLi DaChen YuboZhao DanChen FangfangWang DezhenLi XiuhongGao JunliChen Jun - Early recognition of a risk of Alzheimer's disease (AD) remains a global challenge, and blood proteomic markers are of particular interest for wide-scale diagnostic use. Quantitative multiple reaction monitoring (MRM) approach demonstrates good reproducibility in the characteristic changes in the levels of reported candidate biomarkers (CBs) in different cohorts in AD. Following up on our previous study, we performed a joint analysis of 331 blood plasma samples from two different clinical cohorts of participants, comprising a total of 95 samples from patients with AD, 136 samples from patients with mild cognitive impairment (MCI), and 100 samples from controls. The obtained results confirm the significance of 37 CBs. A logistic regression-based algorithm was used to build protein classifiers, and a total of 21 important proteins were selected, 13 of which (ORM1, APOA4, LBP, HP, FN1, BCHE, APOE, PZP, A1BG, TF, SERPINA7, TTR, and F12) formed a universal panel that demonstrated strong classification performance in distinguishing AD patients from controls (ROC-AUC = 0.90) and in separating stable and progressing patients with MCI (ROC-AUC = 0.81). Overall, the analysis confirms the high potential of the MRM method for validating CBs in independent cohorts. - Source: PubMed
Publication date: 2025/12/19
Strelnikova Polina AKononikhin Alexey SZakharova Natalia VBugrova Anna EIndeykina Maria IFedorova Yana BKolykhalov Igor VMorozova Anna YAndryushchenko Alisa VFedoseeva Elena DEmelyanova Marina AGryadunov Dmitry AGavrilova Svetlana IMitkevich Vladimir AKostyuk George PChaika Yulia AMakarov Alexander ANikolaev Evgeny N - Panitumumab shows limited clinical benefit in colorectal cancer (CRC), and reliable predictive biomarkers to guide patient selection remain lacking. To address this gap, we investigated molecular determinants of therapeutic response using tumor samples from patients with primary and metastatic CRC. By integrating PIMS-based metastatic classification, NPOT interaction profiling and quantitative proteomics, this study aimed to identify response-associated pathways and potential prognostic biomarkers that could support improved stratification for panitumumab therapy. - Source: PubMed
Quartier AngeliqueSanin Ahmed YNagelschmitz JuliaSchneider JustineShi WenjieWartmann ThomasDölling MaximilianStelter FrederikeAndric MihailoCroner Roland SEftekhari PierreKahlert Ulf D