PLGF 2
- Known as:
- PLGF 2
- Catalog number:
- CYT-420
- Product Quantity:
- 2µg
- Category:
- -
- Supplier:
- Prospecbio
- Gene target:
- PLGF 2
Ask about this productRelated genes to: PLGF 2
- 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: PLGF 2
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- Stillbirth remains a major global health challenge, particularly in low- and middle-income countries. Angiogenic imbalance, characterized by elevated soluble fms-like tyrosine kinase-1 (sFlt-1) and reduced placental growth factor (PlGF), is implicated in placental dysfunction, yet few studies have examined the association between the sFlt-1:PlGF ratio and stillbirth. - Source: PubMed
Publication date: 2026/04/01
Lee Mi-Sun SEum Ki-DoChristiani David C - The peri-implantation period in pigs is critical for establishing pregnancy and is tightly regulated by metabolic and hormonal cues. We hypothesised that adiponectin (ADPN), an adipokine involved in energy homeostasis and insulin sensitivity, modulates the secretion of endometrial prostaglandin (PG) E and F and the expression of apoptotic factors in a pregnancy stage-dependent manner. Additionally, we explored potential interactions between ADPN and insulin (INS) in the regulation of these processes. Endometrial explants from gilts on days 10-11, 12-13, 15-16, and 27-28 of pregnancy, and days 10-12 of the oestrous cycle were cultured with ADPN (1 or 10 µg/mL) and/or INS (10 ng/mL). PG concentrations in culture media and apoptotic markers in tissue homogenates were measured using ELISA. Gene and protein expression of PG synthesis enzymes (COX-2, mPGES-1, AKR1C3, and CBR1) were analysed via qPCR and Western blot, respectively. ADPN modulated PG secretion in a pregnancy stage-dependent manner, stimulating PGE and PGF production during implantation and placentation (days 15-28), likely through the upregulation of COX-2 and/or mPGES-1. In earlier stages, ADPN reduced COX-2 expression but increased the levels of other enzymes without significantly affecting PG output. INS modified the effects of ADPN in a stage-specific manner, suggesting a complex hormonal cross-talk between those hormones. Overall, ADPN exhibited anti-apoptotic properties, although it increased Fas levels at higher concentrations. In conclusion, ADPN regulates PG synthesis and apoptotic signalling in the porcine endometrium in a stage-specific manner, supporting its role in endometrial remodelling, embryo implantation, and early pregnancy maintenance. - Source: PubMed
Publication date: 2026/04/10
Kiezun MartaDobrzyn KamilKaminski TadeuszSmolinska Nina - Placental growth factor (PlGF) is a key biomarker for diagnosing and predicting preeclampsia (PE). While serum-based PlGF assays are well established, urine has emerged as a promising non-invasive alternative matrix. However, the absence of urinary PlGF stability data remains a major preanalytical limitation. This study aimed to assess urinary PlGF stability under common preanalytical conditions, including refrigerated storage and a double freeze-thaw cycle. - Source: PubMed
Publication date: 2026/04/15
Martinez-Marzo EvaPeran MariaLerma-Irureta JuanMedel-Martinez AnaPaules CristinaOros DanielFabre Marta - Porcine gastrointestinal fluids offer biorelevant alternatives to human fluids for assessing probiotic viability due to their similar pH, enzymes, and bile salts. Despite extensive research on synthetic fluids, porcine fluids remain underexplored. This study evaluated commercial probiotic survival in porcine gastric fluid (PGF) and porcine intestinal fluid (PIF), bridging in vitro simulations with human and veterinary applications. - Source: PubMed
Publication date: 2026/04/10
Fredua-Agyeman MansaGaisford Simon - Biochemical reactions are inherently stochastic, with their kinetics commonly described by chemical master equations (CMEs). However, the discrete nature of molecular states renders likelihood-based parameter inference from CMEs computationally intensive. Here, we introduce an inference method that leverages analytical solutions in the probability generating function (PGF) space and systematically evaluate its efficiency, accuracy, and robustness. Across both steady-state and time-resolved count data, our numerical experiments demonstrate that the PGF-based method consistently outperforms existing approaches in terms of both computational efficiency and inference accuracy, even under data contamination. These favorable properties further enable the extension of the PGF-based framework to model selection-a task typically considered computationally prohibitive. Using time-resolved data, we show that the method can correctly identify complex gene expression models with more than three gene states, a task that cannot be reliably achieved using steady-state data alone. - Source: PubMed
Publication date: 2026/04/10
Li ShiyueWang YilingShu ZhanpengGrima RamonJiang QingchaoCao Zhixing