Mouse anti-Human CD56, FITC Conjugated mAb
- Known as:
- Mouse (anti-) to-Human CD56, fluorecein Conjugated mAb
- Catalog number:
- 28176
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Signalway
- Gene target:
- Mouse anti-Human CD56 FITC Conjugated mAb
Related genes to: Mouse anti-Human CD56, FITC Conjugated mAb
- Gene:
- NCAM1 NIH gene
- Name:
- neural cell adhesion molecule 1
- Previous symbol:
- -
- Synonyms:
- NCAM, CD56
- Chromosome:
- 11q23.2
- Locus Type:
- gene with protein product
- Date approved:
- 2001-06-22
- Date modifiied:
- 2014-11-19
Related products to: Mouse anti-Human CD56, FITC Conjugated mAb
Related articles to: Mouse anti-Human CD56, FITC Conjugated mAb
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Zakurazhnaya ValeriaZorkina YanaAbramova OlgaRiabinina DariaOchneva AlexandraUshakova ValeriyaReznik AlexanderKostyuk Georgy PMorozova Anna - Neuroendocrine prostate cancer (NEPC) is an aggressive treatment-associated lineage state emerging with potent androgen receptor (AR) pathway inhibition. Although treatment-emergent NEPC is increasingly recognized, the transcriptional consequences of sustained AR suppression remain incompletely defined. It remains unclear to what extent AR-targeted therapies reshape cellular identity and engage neuroendocrine-associated transcriptional programs without full lineage-defining differentiation. - Source: PubMed
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Watanabe RyutaChosei MamiArai HarunaMiura NoriyoshiKikugawa TadahikoSaika Takashi - Stem cell based approaches are increasingly studied as models for understanding neurodegenerative diseases. Despite progress, a unified mechanistic understanding of human iPSCs to neural commitment remains incomplete, due to variability in methodologies and experimental conditions, highlighting the need for a more standardized mechanistic framework. This study investigates the transcriptional landscape and regulatory mechanisms underlying the differentiation of induced pluripotent stem cells (iPSCs) towards neural commitment using three orthogonal methodologies across four scRNA seq datasets. In brief, each dataset was processed through a standard Seurat pipeline followed by identification of differentially expressed genes (DEGs) via the Wilcoxon rank-sum test. The significant transcription factors (TFs) were cross validated against the human TF catalogue and ranked accordingly. We identified 15 TFs up-regulated across datasets, and 54 TFs upregulated in three out of four datasets. Among these, ASCL1 emerged as the top candidate. The downregulation of LIN28A and SOX2 is consistent with silencing of the pluripotency network. The second part of the study involved the construction of a co -expression network through a consensus of the four datasets. The network yielded 24 co-expression modules which prioritised hub transcription factors of neural commitment. Module trait correlations were computed with WGCNA, identifying NCAM1 module as top hub. RTN regulon interference and master regulator analysis was applied as a third analysis method. The intersection resulted in identification of LCOR, MEIS3 and EBF1 as the top intramodular hub genes regulators. Current study offers a comprehensive map of key transcriptional dynamics and regulatory nodes involved in iPSC-to-NSC differentiation. This study offers insights into neural development and identifies prioritised candidates for experimental follow-up. - Source: PubMed
Publication date: 2026/07/29
Gupta AyushiSingh Sangeeta - Neuropathic pain affects an estimated 7%-10% of the global population and imposes an annual economic burden exceeding $600 billion in the United States alone. It lacks robust objective biomarkers; current diagnosis relies heavily on subjective reporting and heterogeneous phenotypes. Currently utilized pain assessment tools include the brief pain inventory (BPI), numerical rating scales (0-10 pain scores), and the visual analog score (VAS), which depend on patient-reported outcomes and are influenced by social, psychological and contextual factors. This subjectivity contributes to heterogeneous phenotyping and variability (>30%) towards the treatment response. Emerging transcriptomic and epitranscriptomic evidence suggests that RNA-based biomarkers may offer a biologically sound and objective approach to understanding and managing pain by capturing underlying molecular mechanisms. Therefore, the present clinical review focused on RNA biomarker classes (mRNA, miRNA, lncRNA, RNA editing, RNA modifications) and proposes a clinically deployable testing system for diagnosis, stratification, and treatment monitoring, since there are no FDA-approved RNA-based biomarkers for pain. Therefore, this review synthesizes evidence from immune-cell transcriptomic meta-analysis (TCL1A/ERAP2), dorsal root ganglion (DRG) and central nervous system gene expression patterns (EFNB2, GABBR1, NCAM1, SCN11A)/brain genetic architecture via single-cell omics integration, and atlas-driven frameworks, like iPain single-cell atlas of pain chronification and nociceptor senescence. Additional sources include studies on RNA editing mediator adenosine deaminase acting on RNA2 (ADAR2), clinical and translational evidence supporting miRNA biomarkers, and lncRNA axes (NEAT1/miR-183-5p; H19/miR-141) as tissue-specific regulatory nodes. Additionally, m6A epitranscriptomic modifications regulated by the METTL3/METTL14 writer complex and FTO/ALKBH5 erasers, with site-specific methylation of GRIN2B mRNA shown to upregulate GluN2B in dorsal horn neurons and augment central sensitization. These biomarkers also demonstrate potential utility as pharmacodynamic readouts in drug and neuro-modulation trials. Additionally, an emerging RNA workflow technology pathway leveraging rapid low-input RNA based assays was also explained. All evidence supports the idea that these biomarkers can provide complementary insight into the mechanisms underlying pain. Although current evidence supports the feasibility of RNA-based biomarkers as indicators of key biological processes, however, the current pain biology score remains at the theoretical model stage and has not been validated through , or clinical trials. - Source: PubMed
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