CD138 Mouse-Mono
- Known as:
- CD138 Mouse-Mono
- Catalog number:
- 413881F
- Product Quantity:
- 6ml
- Category:
- -
- Supplier:
- Nichereion
- Gene target:
- CD138 Mouse-Mono
Related genes to: CD138 Mouse-Mono
- Gene:
- SDC1 NIH gene
- Name:
- syndecan 1
- Previous symbol:
- SDC
- Synonyms:
- CD138, syndecan, SYND1
- Chromosome:
- 2p24.1
- Locus Type:
- gene with protein product
- Date approved:
- 1991-03-18
- Date modifiied:
- 2014-11-19
Related products to: CD138 Mouse-Mono
Related articles to: CD138 Mouse-Mono
- Interferon-responsive tumor-cell states can simultaneously increase immune visibility and induce immune-regulatory programs, yet their epithelial-cell distribution and treatment responsiveness in bladder cancer remain incompletely defined. We sought to resolve a reproducible epithelial interferon-response state across patients, characterize its immune context, and determine whether its transcriptional components respond to ionizing radiation . - Source: PubMed
Publication date: 2026/09/11
Ji YangZhang YangyangLiu XiaodongLv ZhuoyuanWang YakaiWu ShixuanWang Lei - Endothelial injury is central to sepsis, and altered copper homeostasis may be involved in inflammatory and oxidative stress responses. This study examined the association between plasma copper (Cu) concentrations, endothelial injury markers, inflammatory burden, illness severity, and 28-day mortality in patients with sepsis. - Source: PubMed
Publication date: 2026/09/24
Wang HaoyanChen HuiZhang HuixiangLi LiYin HuaZhao KunZhang ManliLi XuanTong Fei - To explore the relationships between serum Sterol Sulfotransferase, Recombinant Angiopoietin Like Protein 8 (ANGPTL8), and Recombinant Syndecan (SDC1) and liver function in patients with intrahepatic cholestasis of pregnancy (ICP), as well as their influence on perinatal outcomes. - Source: PubMed
Wang QixinWang MingWu Wenqi - Quantifying blood-brain barrier (BBB) integrity from fluorescence microscopy remains limited by subjective scoring and categorical classification methods that lack reproducibility. For reproducible BBB phenotyping, we present two semi-automated image-analysis pipelines that replace manual scoring with quantitative, continuous-variable measurements. Our in vitro pipeline, implemented in Python, quantifies the connectivity of tight junction structures by measuring discrete ZO-1 fragment objects within manually traced junction regions. It outputs continuous metrics including average fragment area, total junctional area, and a junctional fragmentation ratio that captures degree of ZO‑1 continuity. In human brain microvascular endothelial cells subjected to glycocalyx component knockdown, the pipeline detected significantly reduced fragment area (37% decrease for both CD44 and syndecan-1 (SDC1) knockdown, p = 0.0148 and 0.0084) and junctional fragmentation ratio (p = 0.0061 and 0.0137). Our in vivo pipeline integrates ilastik-based pixel classification with FIJI macro automation to quantify vascular marker colocalization and to separate vessel signal from microglial contamination within a single fluorescence channel, eliminating the need for dedicated counterstains. Applied across four mouse cohorts [young, aged, Alzheimer's, traumatic brain injury (TBI)] and three brain regions [prefrontal cortex (PFC), hippocampus, midbrain], the pipeline detected concurrent ZO-1 loss and ICAM-1 elevation in the PFC and hippocampus of aged and Alzheimer's cohorts, which were statistically indistinguishable, with eNOS nearly doubling in the Alzheimer's PFC (p = 0.0013). TBI mice showed persistent ZO-1 loss with transient ICAM-1 and eNOS changes. Both deterministic pipelines are available on GitHub and designed for adoption beyond the specific markers and systems analyzed here. - Source: PubMed
Publication date: 2026/09/17
Peck Benjamin DO'Hare Nicholas RFerris Craig FPinals Rebecca LEbong Eno E - The placenta continuously remodels in response to maternal and fetal signals, with proteins dynamically regulated across placental regions. However, methods to evaluate proteins within these distinct regions are limited. To address this, we developed a placental tissue classifier to segment regions corresponding to the villous core, villous trophoblast (VT) and the intervillous space (IVS) using HALO AI imaging analysis. Tissue sections of biopsies from human term placentas embedded in OCT or paraffin (FFPE) were stained by immunofluorescence with antibodies to placental alkaline phosphatase (PLAP), syndecan-1 (SDC-1), vimentin or E-cadherin. The placental tissue classifier was trained using PLAP and DAPI staining with distinct image-feature patterns to distinguish VT from the villous core and the IVS on stained sections. The accuracy of HALO to measure area and intensity of staining in classified regions was demonstrated by staining for SDC-1 on VT and vimentin on stromal cells. As expected, SDC-1 staining was low in the villous core, and higher on VT than in the IVS, whereas vimentin staining was only detected in the villous core. By applying this analysis method to images collected by whole slide scanning microscopy, the area of staining was increased 245 times compared to a single field of view, which increases the probability of detecting pathological changes in different regions of the placenta. The advantages of using this validated classifier are the speed and accuracy of analysis across large tissue areas, the flexibility to detect other cell types, and to expand to single villi analysis. - Source: PubMed
Publication date: 2026/09/17
Reif RebeccaYanow Stephanie KHemmings Denise G