Human placenta growth factor,PLGF ELISA kit
- Known as:
- Human placenta growth factor,PLGF Enzyme-linked immunosorbent assay test reagent
- Catalog number:
- 201-12-0139
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Sunredbio SunBT Sun red bio
- Gene target:
- Human placenta growth factor PLGF ELISA kit
Related genes to: Human placenta growth factor,PLGF ELISA kit
- Gene:
- PGF NIH gene
- Name:
- placental growth factor
- Previous symbol:
- PGFL
- Synonyms:
- PLGF, PlGF-2, PlGF, SHGC-10760, D12S1900, PIGF
- Chromosome:
- 14q24.3
- Locus Type:
- gene with protein product
- Date approved:
- 1994-01-14
- Date modifiied:
- 2016-05-20
Related products to: Human placenta growth factor,PLGF ELISA kit
Related articles to: Human placenta growth factor,PLGF ELISA kit
- Visible-thermal tiny pedestrian detection in UAV aerial images is crucial for online decision-making in urban security and disaster response. However, the extremely small scale and sparse distribution of pedestrians cause discriminative cues to be submerged by dominant low-frequency background and contextual redundancy during feature learning. Meanwhile, cross-modal spatial misalignment and spatially varying modality reliability hinder stable fine-grained correspondence, thereby degrading fusion quality. To address these issues, QAFDet is proposed as a quality-aware adaptive alignment fusion network comprising three modules: spectrum-spatial decoupled enhancement module (SDE), cross-modal correspondence mining module (CCM), and prior-informed gated fusion (PGF). SDE leverages the discrete cosine transform to disentangle redundant low-frequency background information, while deep semantic gating propagates high signal-to-noise ratio details into shallow representations to enhance subtle cues of tiny pedestrians and suppress high-frequency noise. To establish fine-grained neighborhood correspondences under slight spatial offsets, thermal-guided local asymmetric cross-attention is designed in CCM. Finally, region-level quality and modality discrepancy are jointly modeled for adaptive cross-modal fusion in PGF. Extensive experiments on multiple UAV-based RGBT detection benchmarks demonstrate that QAFDet achieves state-of-the-art performance and exhibits strong robustness in challenging aerial scenes. - Source: PubMed
Publication date: 2026/05/29
Tan YifangYuan LijunXie ChuanjiangZhou ChaoLi XinZhu Xinyu - To examine the relationship between feeding milestones and the postnatal growth of preterm infants. - Source: PubMed
Publication date: 2026/06/09
Bala FaithAlshaikh EnasJadcherla Sudarshan R - This study aims to identify macrophage polarization (MP)-related genes implicated in esophageal cancer (EC) by integrating methylation quantitative trait loci (mQTL), expression QTL (eQTL), and protein QTL (pQTL) data with EC genome-wide association study (GWAS) data. - Source: PubMed
Publication date: 2026/06/06
Shi MumuZhou RenshiYu BoYu FutianGao Jinghai - This study aimed to identify aqueous humor (AQ) proteins that correlate with vitreous humor (VH) levels in proliferative diabetic retinopathy (PDR). Using paired VH and AQ samples from 41 patients undergoing vitrectomy for PDR, we measured 92 protein biomarkers via a high-throughput immunoassay. We assessed Spearman correlations between AQ and VH levels for each protein and associations with PDR severity parameters. Thirty-two proteins showed positive AQ-VH correlations. A stepwise selection process, based on detectability, consistency and correlation strength narrowed this down to 21 proteins. An exploratory analysis of associations with disease severity parameters suggested that of these proteins, CXCL-10, IL-8, and MCP-4 were most strongly associated with fibrosis, while GAL-9 and PGF were most strongly associated with the extent of neovascularization, supporting the potential of these proteins as useful biomarkers. In conclusion, 21 AQ proteins show significant correlation with patient-matched VH levels in PDR patients, indicating that relative patient-level differences in AQ reflect relative differences in VH for these proteins. This methodological framework provides a foundation for future studies aiming to identify diagnostic or treatment stratification biomarkers for PDR and related complications such as diabetic macular edema. - Source: PubMed
Publication date: 2026/06/06
Klaassen IngeborgTanck Michael W TDuncan Holly JYildiz ElifSchlingemann Reinier OSteel David H W - Poor graft function (PGF) is a serious and life-threatening complication following allogeneic hematopoietic stem cell transplantation (allo-HSCT). Current therapeutic approaches show suboptimal clinical outcomes, with substantial variability in treatment efficacy across studies, which highlights the critical need for timely identification of PGF and appropriate preventive strategies to improve prognosis. To identify PGF risk factors and establish a nomogram-based predictive model of PGF, this real-world study analyzed 795 hematological malignancies patients underwent allo-HSCT. We identified lower CD34 cell dose in the graft, infection, cytomegalovirus activation, splenomegaly and anti-HLA antibody as independent risk factors for PGF. Severe graft-versus-host disease exhibited significant association with secondary PGF in subgroup analysis. Recipients with good graft function demonstrated higher 3-year overall survival (OS) rate compared to PGF cohorts (78.1% vs. 50.6%). Notably, secondary PGF cases exhibited superior 3-year OS relative to primary PGF cases (52.0% vs. 46.2%), and the primary PGF group showed a higher cumulative incidence of NRM than secondary PGF group. Restricted cubic spline analysis demonstrated a dose-dependent relationship between infused stem cell doses and the risk of PGF. Both CD34 cell dose and MNC dose showed inverse associations with the predicted probability of PGF. To assess the relative contribution of each covariate to PGF risk stratification, we constructed a predictive model based on the nomogram framework. Ten clinically relevant predictors were incorporated into the model, which demonstrated strong predictive performance and excellent calibration. The model also exhibited robust discriminative capacity (C-statistics = 0.782) and provided meaningful clinical utility. Importantly, this predictive tool facilitates identification of patients at high risk of PGF who could benefit from preventive measures. - Source: PubMed
Publication date: 2026/06/04
Gao MingTang YaqiongQi JiaqianSong XiaofeiWang PengWu DepeiHan Yue