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cd44  (Bioss)


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    Bioss cd44
    Validation of the specific ligand–receptor pairs between the implants and bone-marrow cells. (A) Schematic diagram of the rat model of intramedullary femoral implantation and bulk RNA sequencing workflow. (B) Violin plots showing the expression levels of Col6a2 , <t>Cd44</t> , Col1a1 , and Sdc1 . (C and D) Hierarchical clustering heatmaps of pairwise DEGs among the sham, Ti, and ZrO 2 groups. (E to G) Identification of the gene modules associated with Ti and ZrO 2 via weighted gene coexpression network analysis. (H) Overlapping genes between module-related genes and DEGs in each group. (I) Contributions of Ti and ZrO 2 -associated feature genes to predictive outcomes, assessed using least absolute shrinkage and selection operator (LASSO) regression analysis. (J and K) Temporal expression patterns and correlations of Col6a2 , Cd44 , Col1a1 , and Sdc1 . Statistical significance was assessed using the t test.
    Cd44, supplied by Bioss, used in various techniques. Bioz Stars score: 94/100, based on 2 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/cd44/product/Bioss
    Average 94 stars, based on 2 article reviews
    cd44 - by Bioz Stars, 2026-04
    94/100 stars

    Images

    1) Product Images from "Mapping Immune-Inflammatory Niches on Zirconia Bone Implants: Single-Cell Transcriptomic Profiling"

    Article Title: Mapping Immune-Inflammatory Niches on Zirconia Bone Implants: Single-Cell Transcriptomic Profiling

    Journal: Research

    doi: 10.34133/research.1162

    Validation of the specific ligand–receptor pairs between the implants and bone-marrow cells. (A) Schematic diagram of the rat model of intramedullary femoral implantation and bulk RNA sequencing workflow. (B) Violin plots showing the expression levels of Col6a2 , Cd44 , Col1a1 , and Sdc1 . (C and D) Hierarchical clustering heatmaps of pairwise DEGs among the sham, Ti, and ZrO 2 groups. (E to G) Identification of the gene modules associated with Ti and ZrO 2 via weighted gene coexpression network analysis. (H) Overlapping genes between module-related genes and DEGs in each group. (I) Contributions of Ti and ZrO 2 -associated feature genes to predictive outcomes, assessed using least absolute shrinkage and selection operator (LASSO) regression analysis. (J and K) Temporal expression patterns and correlations of Col6a2 , Cd44 , Col1a1 , and Sdc1 . Statistical significance was assessed using the t test.
    Figure Legend Snippet: Validation of the specific ligand–receptor pairs between the implants and bone-marrow cells. (A) Schematic diagram of the rat model of intramedullary femoral implantation and bulk RNA sequencing workflow. (B) Violin plots showing the expression levels of Col6a2 , Cd44 , Col1a1 , and Sdc1 . (C and D) Hierarchical clustering heatmaps of pairwise DEGs among the sham, Ti, and ZrO 2 groups. (E to G) Identification of the gene modules associated with Ti and ZrO 2 via weighted gene coexpression network analysis. (H) Overlapping genes between module-related genes and DEGs in each group. (I) Contributions of Ti and ZrO 2 -associated feature genes to predictive outcomes, assessed using least absolute shrinkage and selection operator (LASSO) regression analysis. (J and K) Temporal expression patterns and correlations of Col6a2 , Cd44 , Col1a1 , and Sdc1 . Statistical significance was assessed using the t test.

    Techniques Used: Biomarker Discovery, RNA Sequencing, Expressing, Selection



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    Validation of the specific ligand–receptor pairs between the implants and bone-marrow cells. (A) Schematic diagram of the rat model of intramedullary femoral implantation and bulk RNA sequencing workflow. (B) Violin plots showing the expression levels of Col6a2 , <t>Cd44</t> , Col1a1 , and Sdc1 . (C and D) Hierarchical clustering heatmaps of pairwise DEGs among the sham, Ti, and ZrO 2 groups. (E to G) Identification of the gene modules associated with Ti and ZrO 2 via weighted gene coexpression network analysis. (H) Overlapping genes between module-related genes and DEGs in each group. (I) Contributions of Ti and ZrO 2 -associated feature genes to predictive outcomes, assessed using least absolute shrinkage and selection operator (LASSO) regression analysis. (J and K) Temporal expression patterns and correlations of Col6a2 , Cd44 , Col1a1 , and Sdc1 . Statistical significance was assessed using the t test.
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    Validation of the specific ligand–receptor pairs between the implants and bone-marrow cells. (A) Schematic diagram of the rat model of intramedullary femoral implantation and bulk RNA sequencing workflow. (B) Violin plots showing the expression levels of Col6a2 , <t>Cd44</t> , Col1a1 , and Sdc1 . (C and D) Hierarchical clustering heatmaps of pairwise DEGs among the sham, Ti, and ZrO 2 groups. (E to G) Identification of the gene modules associated with Ti and ZrO 2 via weighted gene coexpression network analysis. (H) Overlapping genes between module-related genes and DEGs in each group. (I) Contributions of Ti and ZrO 2 -associated feature genes to predictive outcomes, assessed using least absolute shrinkage and selection operator (LASSO) regression analysis. (J and K) Temporal expression patterns and correlations of Col6a2 , Cd44 , Col1a1 , and Sdc1 . Statistical significance was assessed using the t test.
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    Validation of the specific ligand–receptor pairs between the implants and bone-marrow cells. (A) Schematic diagram of the rat model of intramedullary femoral implantation and bulk RNA sequencing workflow. (B) Violin plots showing the expression levels of Col6a2 , <t>Cd44</t> , Col1a1 , and Sdc1 . (C and D) Hierarchical clustering heatmaps of pairwise DEGs among the sham, Ti, and ZrO 2 groups. (E to G) Identification of the gene modules associated with Ti and ZrO 2 via weighted gene coexpression network analysis. (H) Overlapping genes between module-related genes and DEGs in each group. (I) Contributions of Ti and ZrO 2 -associated feature genes to predictive outcomes, assessed using least absolute shrinkage and selection operator (LASSO) regression analysis. (J and K) Temporal expression patterns and correlations of Col6a2 , Cd44 , Col1a1 , and Sdc1 . Statistical significance was assessed using the t test.
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    Image Search Results


    3D light sheet and 2D multi-cyclic imaging data comparison (Mouse Glioblastoma) (A) Imaris 3D surface rendering of autofluorescence (cyan) and glioblastoma target cells stained with anti-GFP-Alexa Fluor 647 nanobody (red). (B) Imaris 3D surface rendering of autofluorescence (cyan) and glioblastoma target cells stained with anti-GFP-Alexa Fluor 647 nanobody (red) with target plane in yellow. (C) Optical section of target plane of interest. (D) Fluorescence image of physical cryosection. (E) MICS image of section shown in D. (F) MICS image indicating anti-GFP-Alexa Fluor 647 nanobody (red) staining. (G) Magnified merged four color multiparameter MICS image with anti-EGFR (magenta), anti-GFAP (green), anti-NeuN (blue), anti-CD146 (yellow). (H–P) Nine exemplary MICS images with merges of anti-GFP-Alexa Fluor 647 nanobody staining (red) and antibody-conjugates against EGFR (H), Neurofilament (I), Nestin (J), GFAP (K), CD44 (L), CD146 (M), NeuN (N), EphA2 (O) and GLAST (P) (gray) (see “Antibodies”). Scale bars: (A–F) 500 μm; (G) 50 μm; (H–P) 500 μm.

    Journal: STAR Protocols

    Article Title: Protocol for 3D-guided sectioning and deep cell phenotyping via light sheet imaging and 2D spatial multiplexing

    doi: 10.1016/j.xpro.2025.104296

    Figure Lengend Snippet: 3D light sheet and 2D multi-cyclic imaging data comparison (Mouse Glioblastoma) (A) Imaris 3D surface rendering of autofluorescence (cyan) and glioblastoma target cells stained with anti-GFP-Alexa Fluor 647 nanobody (red). (B) Imaris 3D surface rendering of autofluorescence (cyan) and glioblastoma target cells stained with anti-GFP-Alexa Fluor 647 nanobody (red) with target plane in yellow. (C) Optical section of target plane of interest. (D) Fluorescence image of physical cryosection. (E) MICS image of section shown in D. (F) MICS image indicating anti-GFP-Alexa Fluor 647 nanobody (red) staining. (G) Magnified merged four color multiparameter MICS image with anti-EGFR (magenta), anti-GFAP (green), anti-NeuN (blue), anti-CD146 (yellow). (H–P) Nine exemplary MICS images with merges of anti-GFP-Alexa Fluor 647 nanobody staining (red) and antibody-conjugates against EGFR (H), Neurofilament (I), Nestin (J), GFAP (K), CD44 (L), CD146 (M), NeuN (N), EphA2 (O) and GLAST (P) (gray) (see “Antibodies”). Scale bars: (A–F) 500 μm; (G) 50 μm; (H–P) 500 μm.

    Article Snippet: CD44 antibody, anti-mouse, APC, REAfinity , Miltenyi Biotec B.V. & Co. KG , Cat# 130-119-121 RRID: AB_2751628.

    Techniques: Imaging, Comparison, Staining, Fluorescence

    Validation of the specific ligand–receptor pairs between the implants and bone-marrow cells. (A) Schematic diagram of the rat model of intramedullary femoral implantation and bulk RNA sequencing workflow. (B) Violin plots showing the expression levels of Col6a2 , Cd44 , Col1a1 , and Sdc1 . (C and D) Hierarchical clustering heatmaps of pairwise DEGs among the sham, Ti, and ZrO 2 groups. (E to G) Identification of the gene modules associated with Ti and ZrO 2 via weighted gene coexpression network analysis. (H) Overlapping genes between module-related genes and DEGs in each group. (I) Contributions of Ti and ZrO 2 -associated feature genes to predictive outcomes, assessed using least absolute shrinkage and selection operator (LASSO) regression analysis. (J and K) Temporal expression patterns and correlations of Col6a2 , Cd44 , Col1a1 , and Sdc1 . Statistical significance was assessed using the t test.

    Journal: Research

    Article Title: Mapping Immune-Inflammatory Niches on Zirconia Bone Implants: Single-Cell Transcriptomic Profiling

    doi: 10.34133/research.1162

    Figure Lengend Snippet: Validation of the specific ligand–receptor pairs between the implants and bone-marrow cells. (A) Schematic diagram of the rat model of intramedullary femoral implantation and bulk RNA sequencing workflow. (B) Violin plots showing the expression levels of Col6a2 , Cd44 , Col1a1 , and Sdc1 . (C and D) Hierarchical clustering heatmaps of pairwise DEGs among the sham, Ti, and ZrO 2 groups. (E to G) Identification of the gene modules associated with Ti and ZrO 2 via weighted gene coexpression network analysis. (H) Overlapping genes between module-related genes and DEGs in each group. (I) Contributions of Ti and ZrO 2 -associated feature genes to predictive outcomes, assessed using least absolute shrinkage and selection operator (LASSO) regression analysis. (J and K) Temporal expression patterns and correlations of Col6a2 , Cd44 , Col1a1 , and Sdc1 . Statistical significance was assessed using the t test.

    Article Snippet: The following antibodies were applied sequentially: CD44 (Bioss, bsm-54767R, 1:300, China), CD68 (Proteintech, 66231-2-Ig, 1:500, China), CD206 (Bioss, bsm-55604R, 1:300, China), NOS2 (Bioss, bs-0162R, 1:300, China), COL6A2 (Bioss, bs-13963R, 1:300, China), S100A4 (Bioss, bs-3759R, 1:300, China), COL1A1 (Bioss, bs-10423R, 1:300, China), and SDC1 (Bioss, bs-1309R, 1:300, China), followed by nuclear counterstaining with 4′,6-diamidino-2-phenylindole.

    Techniques: Biomarker Discovery, RNA Sequencing, Expressing, Selection