EXTRACT HtrA1 mini
- Known as:
- EXTRACT HtrA1 Minimum
- Catalog number:
- 30501001
- Product Quantity:
- EUR
- Category:
- -
- Supplier:
- BioTeZ
- Gene target:
- EXTRACT HtrA1 mini
Related genes to: EXTRACT HtrA1 mini
- Gene:
- HTRA1 NIH gene
- Name:
- HtrA serine peptidase 1
- Previous symbol:
- PRSS11
- Synonyms:
- HtrA, IGFBP5-protease, ARMD7
- Chromosome:
- 10q26.13
- Locus Type:
- gene with protein product
- Date approved:
- 1997-07-25
- Date modifiied:
- 2016-10-05
Related products to: EXTRACT HtrA1 mini
Related articles to: EXTRACT HtrA1 mini
- High temperature requirement protease A1 (HtrA1) serves as a regulator of fibroblast growth factor (FGF) signaling by degrading proteoglycans attached to FGF ligands on the cell surface. This degradation releases FGF ligands into the extracellular space, facilitating their binding to cognate receptors and subsequent neural induction, as demonstrated in embryos. Whether HtrA1 plays a similar function in mammalian neural development remains unknown. As a first step toward addressing this, the present study aims to quantify expression before and after neural differentiation in mammalian cells, establishing whether neural differentiation is accompanied by changes in at the mRNA level. - Source: PubMed
Publication date: 2026/09/20
Farrokhi FatemehMohammad Zadeh-Vardin MohammadPanahi YasinAghvami Tehrani AzadehSagha MohsenNamjoo Zeinab - Hereditary cerebral small vessel disease (cSVD) comprises a heterogeneous group of monogenic disorders affecting small cerebral arteries, arterioles, capillaries, and venules, leading to stroke, intracerebral hemorrhage, and vascular cognitive impairment, often at a young age. Despite their rarity, these conditions offer critical insights into the molecular mechanisms underlying microvascular brain injury and are frequently underdiagnosed due to phenotypic overlap with sporadic cSVD. Key diagnostic clues include early onset, disproportionate MRI burden, positive family history, and systemic manifestations. Distinct genetic entities, such as CADASIL, HTRA1-related arteriopathies, COL4A1/2-associated microangiopathies, RVCL-S, Fabry disease, and hereditary cerebral amyloid angiopathies, exhibit characteristic clinical, radiological, and extracerebral features. Accurate recognition is essential to guide genetic testing, avoid inappropriate treatments, enable targeted monitoring, and identify the few conditions with disease-modifying therapies. Early diagnosis also allows appropriate genetic counseling and risk stratification for affected families. - Source: PubMed
Publication date: 2026/09/22
Gonçalves Trajano Aguiar PiresGoulart Thiago OscarBacchiega Iago BlancoFrezatti Rodrigo Siqueira SoaresFrezatti Tomásia Oliveira de Holanda MonteiroZanon Zotin Maria ClaraMartins Filho Rui Kleber do ValeRodrigues Guilherme Gustavo RiccioppoLosa MattiaMarques WilsonTomaselli Pedro JoséPontes-Neto Octavio M - Osteoporosis is characterized by reduced bone mineral density (BMD) and an increased risk of fractures, but the relationships between plasma proteins and site-specific BMD phenotypes remain unclear. We aimed to investigate the potential causal associations of plasma proteins on BMD using a Mendelian randomization (MR). - Source: PubMed
Publication date: 2026/09/21
Lv KuiFang JialiuWang ShengyouXing XingZhu Rui - Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss worldwide and is characterized by substantial clinical, imaging, and molecular heterogeneity that complicates disease prediction and therapeutic management. Recent advances in artificial intelligence (AI) and precision therapeutics have created new opportunities for more individualized and data-driven AMD care. AI models trained on multimodal datasets-including fundus photography, optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), genetic susceptibility loci (e.g., CFH, ARMS2/HTRA1, C3, CFI, and APOE), and longitudinal clinical information-have demonstrated promising capability in early disease detection, progression forecasting, biomarker identification, and prediction of treatment response. These developments align closely with emerging precision therapeutic strategies, including optimized anti-vascular endothelial growth factor (anti-VEGF) regimens, complement-targeted therapies, gene-based interventions, and stem cell-associated regenerative approaches. This review provides a translational overview of AI-enabled precision therapeutics in AMD, with emphasis on multimodal biomarker integration, individualized therapeutic stratification, longitudinal disease monitoring, and clinically interpretable AI systems. Importantly, we further propose a Five-Level Clinical Readiness and Translational Utility Framework for AI in AMD Precision Therapeutics, categorizing AI applications according to evidence strength, clinical maturity, validation status, interpretability, and real-world implementation potential. The framework distinguishes near-reference-standard imaging AI systems, advanced clinical decision-support tools, emerging multimodal precision therapeutic AI, supportive workflow-oriented AI systems, and currently limited or unsuitable AI applications. Despite substantial progress, important translational barriers remain, including limited external validation, retrospective study designs, dataset heterogeneity, domain shift, insufficient explainability, regulatory uncertainty, and challenges related to workflow integration and real-world clinical deployment. Future advances in multimodal longitudinal AI, explainable AI, federated learning, digital health platforms, and multi-omics integration may facilitate a transition from reactive disease management toward more proactive, predictive, and personalized ophthalmic care. Collectively, AI-enabled precision therapeutics may help establish a more scalable and clinically integrated framework for individualized AMD management and future precision ophthalmology. - Source: PubMed
Publication date: 2026/08/17
Wang Mini HanLee Simon Ming YuenWang YapengAlves José CXie RuitaoHe YaqingHou GuanghuiFang XiaoxiaoYu YangCai XiaodongZheng ShuaiLiu JinCheang ChoninKuok Kai IanQin Shuai - Cerebral small vessel disease (CSVD) describes a range of neurological diseases affecting the small arteries, veins, and capillaries which supply the white matter and deep grey matter structures of the brain. They are the most common form of cerebrovascular disease, accounting for almost half of vascular dementia cases and approximately 20% of stroke incidence globally. Genetic testing is a routine diagnostic tool for monogenic CSVDs; however, less than 20% of patients have a causal variant in a known gene. Genetic testing for these disorders focuses on single nucleotide variants and short insertions or deletions, with larger genomic variation often unexplored as a cause of disease. In this study we performed whole-exome sequencing (WES) on 111 patients suspected of familial CSVD that had previously tested negative for pathogenic variants in seven known CSVD genes (NOTCH3, HTRA1, COL4A1, COL4A2, TREX1, GLA, and FOXC1). Bioinformatic analysis of WES data, multiplex ligation-dependent probe amplification, quantitative real-time polymerase chain reaction assays, and Nanopore long-read sequencing were used to identify suspected copy number variants. This work identified four candidate CNVs across NOTCH3, LMNB1, and COL4A2 which are potential causes of CSVD and highlights the need for further investigation of more complex forms of genetic variation and their potential roles as causal of CSVD. - Source: PubMed
Publication date: 2026/08/27
Guyler Solomon KMaksemous NevenLea Rodney ASmith Robert ASutherland Heidi GGriffiths Lyn R