APOH ELISA kit
- Known as:
- APOH Enzyme-linked immunosorbent assay test reagent
- Catalog number:
- DL-APOH-Hu
- Product Quantity:
- 96T
- Category:
- Elisa Kits
- Supplier:
- WDSTD
- Gene target:
- APOH ELISA kit
Related genes to: APOH ELISA kit
- Gene:
- APOH NIH gene
- Name:
- apolipoprotein H
- Previous symbol:
- B2G1
- Synonyms:
- BG
- Chromosome:
- 17q24.2
- Locus Type:
- gene with protein product
- Date approved:
- 1987-09-11
- Date modifiied:
- 2015-12-17
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- Immune checkpoint inhibitors like pembrolizumab exhibit variable efficacy in metastatic gastric cancer (GC). This study aimed to identify molecular drivers of pembrolizumab response, explore mechanisms of immune checkpoint inhibitors (ICIs) efficacy, and develop a prognostic signature. Transcriptomic analysis of pembrolizumab-treated GC (TIGER database) identified 165 response-associated differentially expressed genes (DEGs). Functional annotation and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) revealed that responder-upregulated genes (R-DEGs) were enriched in immune activation pathways and mainly localized to CD8 + T/NK cells. In contrast, non-responder-upregulated genes (D-DEGs) were linked to extracellular matrix (ECM) remodeling and mainly expressed in fibroblasts/endothelial cells. CellChat analysis demonstrated that key DEGs mediate immune-stromal crosstalk via MHC-I and collagen/laminin signaling. A prognostic signature (Lasso-StepCox[forward] Riskscore; LSR: APOD, APOH, BATF2, GJA1, MAGED1, SLC5A1, SLCO2A1, VWF, VCAN) was derived and validated in four independent GC cohorts from the GEO and Cancer Genome Atlas (TCGA) database. Multi-omics analyses showed that LSR-high tumors exhibited aggressive clinicopathological features, increased stromal components, reduced cytotoxic immune infiltration, diminished tumor mutational burden (TMB), and poorer prognosis. Immunohistochemistry (IHC) and spatial transcriptomics in GC showed that stromal VWF/VCAN expression correlates with reduced CD8⁺ T cell granzyme B expression, suggesting T cell dysfunction. High VWF expression in GC predicted poor survival, and a combined VWF/VCAN score showed enhanced prognostic stratification. This study highlights stromal-immune crosstalk as a driver of pembrolizumab resistance and provides a signature as a clinical tool for prognosis and personalized therapy in metastatic GC. - Source: PubMed
Publication date: 2026/04/23
Zhang FanZhang QingqingShao ShuaiLi XiaoboCheng YiCao XuYu XiaotangGao Zhengming - The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. This study aims to identify protein biomarkers that differentiate sepsis from non-infectious systemic inflammatory response syndrome (NISIRS), addressing the need for early sepsis diagnosis. - Source: PubMed
Publication date: 2026/04/24
Ruiz-Sanmartín AdolfoRibas VicentSuñol DavidChiscano-Camón LuisMartín LauraBajaña IvánBastida JulianaLarrosa NievesGonzález Juan JoséCarrasco María DoloresCanela NúriaFerrer RicardRuiz-Rodríguez Juan Carlos - Developing reliable biomarkers capable of differentiating Parkinson's disease from other neurological conditions is crucial for both patient care and research. In this study, we leveraged recent advances in high-throughput proteomic technology and machine learning to develop candidate biomarkers for Parkinson's disease. Using the Olink Explore 3072 assay, we obtained plasma proteomic profiles from 698 study participants, comprising Parkinson's disease cases (n = 149), neurologically healthy controls (n = 230), and participants with other neurological conditions (n = 319). The study cohort was split into Training Set (n = 560) and Test Set (n = 138). We conducted differential protein abundance analysis and pathway enrichment analysis, and subsequently applied the Boruta algorithm to identify differentially abundant proteins that are predictive of Parkinson's disease. To create a diagnostic biomarker panel, we trained a stacking ensemble machine learning (ML) model on the Training Set (n = 118 Parkinson's patients, n = 184 healthy controls, and n = 258 individuals with other neurological disorders) using eleven proteins (APOH, ARG1, CCN1, CXCL1, CXCL8, DDC, GRAP2, IL1RAP, OSM, PRL, and SPRY2) as model features. We used the Shapley Additive Explanations (SHAP) framework and network analysis to evaluate predictive importance and biological relevance of each protein in the ML model. The model demonstrated high accuracy in the held-out Test Set (n = 138) and three external cohorts-the UK Biobank (n = 43,969), the Parkinson's Disease Biomarkers Program (n = 138), and the Parkinson's Progression Markers Initiative (n = 385), with areas under the receiver operating characteristic curve of 0.939, 0.789, 0.909, 0.816, respectively. Additionally, network and pathway analyses helped interpret the model, revealing activity related to inflammatory mediators, ErbB signaling, T-cell receptor signaling, and lipid metabolism. Our findings highlight the potential of plasma protein biomarkers to improve Parkinson's disease diagnosis and deepen biological understanding of this complex neurological disorder. Our model demonstrates high specificity and reliability across multiple independent cohorts, indicating the significant potential of proteomics-based biomarkers and the clinical utility of ML-supported diagnosis in Parkinson's disease care. The model also helps to elucidate potential novel risk factors and pathways associated with Parkinson's disease. - Source: PubMed
Publication date: 2026/04/22
Adewale BoluwatifeChia RuthMoaddel RuinLandeck NatalieRasheed MemoonaAlba CamilleReho PaoloVasta RosarioCalvo AndreaMoglia CristinaCanosa AntonioManera UmbertoSnyder AllisonLee Yi-JungGrassano MaurizioGao ChristineZhu MinBrunetti MauraCasale FedericoArvind Kumar Dawson Ted MRosenthal Liana SHall Anna JPantelyat Alexander YDing JinhuiGibbs J RaphaelEgan Josephine MCandia JuliánTanaka ToshikoFerrucci LuigiChiò AdrianoNarendra Derek PKwan Justin YEhrlich Debra JDalgard Clifton LTraynor Bryan JScholz Sonja W - Ultracentrifugation (UC) has long been considered the "gold standard" for extracellular vesicle (EV) isolation. However, due to its drawbacks such as high cost of an ultracentrifuge and rotors, time-consuming and labor-intensive protocol, low yield considering initial biofluid volume and low throughput, development of new EV isolation approaches is still ongoing. Here we compare three methods for isolating the most studied EV subtype, small extracellular vesicles (sEVs), from human plasma: ultracentrifugation (UC), express asymmetric depth filtration (ExADFi), and anti-CD9 immunoaffinity capture (AS-CD9) with focus on their Raman and proteomic profiles. For all three methods, purity and quality of the sEV isolation were assessed based on the level of contamination of the sEV fraction with major plasma proteins such as albumin and apolipoproteins (APOA1, APOH, APOA4, APOC2, APOC1, and APOC4). UC showed the highest ratio of protein to nanoparticle concentration. AS-CD9 and ExADFi provided comparable to UC purity and levels of non-vesicular contaminants with AS-CD9 requiring minimal time and labor. ExADFi showed characteristics including purity of the sEV samples, yield, and isolation time that is between the UC and AS-CD9 methods. Raman spectroscopy provided more details about characteristics of the isolated sEVs and confirmed differences observed in the proteomic profiles. The findings demonstrate that the AS-CD9 and ExADFi methods could be appropriate substitutes of the classical UC-based isolation method and be chosen depending on the final requirements and use of the purified sEVs such as further functional and biomarker studies. - Source: PubMed
Chernyshev Vasiliy SStarodubtseva Natalia LRimskaya Elena NBugrova Anna EKononikhin Alexey SSilachev Denis NTokareva Alisa OEvtushenko Ekaterina AYakovlev Alexander AYurin Alexander MKepsha Maria AMezhevitinova Elena ANikolaev Eugene NFrankevich Vladimir ENazarova Niso MPrilepskaya Vera NSukhikh Gennadiy T - Preterm labor is a serious concern that can lead to preterm birth, posing substantial risks for both the mother and the neonate. Despite approximately 15 million preterm births worldwide each year, there is a lack of sufficient strategies for predicting and preventing preterm labor. Here, we present a non-invasive method for simultaneously detecting exosomal miRNA and protein biomarkers in vaginal discharge, enabling early diagnosis of life-threatening conditions in both the mother and the neonate. Our non-invasive vaginal discharge biopsy using a swab enables the isolation of enriched intact extracellular vesicles through our microfluidic platform called Biologically-intact Exosome Separation Technology (BEST). We observed differential expression of specific miRNAs, including up-regulated hsa-miR-206 and down-regulated hsa-miR-3674, hsa-miR-365a-5p, and hsa-miR-193b-3p, in mothers experiencing preterm labor. We also found significant differences in protein expression in mothers with preterm labor compared to full-term mothers, indicating the involvement of HGS, ATL3, APOH, and GUSB in preterm labor mechanisms. We envision a future in which non-invasive detection of unique miRNA and protein biomarkers in vaginal discharge transforms global healthcare by enabling early detection and effective treatment of preterm labor. - Source: PubMed
Publication date: 2026/04/02
Kim TaewoonPark Jee YoonLee Hyo JinChoi Bo YoungKim Hyeon JiLee Luke PHong Jong Wook