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Proteintech rabbit polyclonal anti chop ddit3

Incorporating various machine learning techniques and protein-protein interaction (PPI) networks into the comprehensive screening of osteoblastic ARGs (A) The interplay relationships within the PPI network of relevant genes. (B) Sorting of the top 20 crucial genes for DEGREE, CLOSENESS, and EPC algorithm from Cytoscape software. (C) Mean decrease Impurity score and mean decrease Accuracy coefficient of each marker in Extra Tree Classifier model. (D) The proportion of Boruta algorithm decision across training dataset and testing dataset. (E) The optimal number of features that ensures the best predictive performance through lasso regression analysis. (F) The SVM-RFE method further compresses the variable features. (G) The most significant features <t>DDIT3,</t> JUN and VEGFA are jointly determined by the PPI network analysis and multiple machine learning algorithms.

Rabbit Polyclonal Anti Chop Ddit3, supplied by Proteintech, used in various techniques. Bioz Stars score: 96/100, based on 765 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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rabbit polyclonal anti chop ddit3 - by Bioz Stars, 2026-03

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1) Product Images from "Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis"

Article Title: Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis

Journal: iScience

doi: 10.1016/j.isci.2024.110130

Figure Legend Snippet: Incorporating various machine learning techniques and protein-protein interaction (PPI) networks into the comprehensive screening of osteoblastic ARGs (A) The interplay relationships within the PPI network of relevant genes. (B) Sorting of the top 20 crucial genes for DEGREE, CLOSENESS, and EPC algorithm from Cytoscape software. (C) Mean decrease Impurity score and mean decrease Accuracy coefficient of each marker in Extra Tree Classifier model. (D) The proportion of Boruta algorithm decision across training dataset and testing dataset. (E) The optimal number of features that ensures the best predictive performance through lasso regression analysis. (F) The SVM-RFE method further compresses the variable features. (G) The most significant features DDIT3, JUN and VEGFA are jointly determined by the PPI network analysis and multiple machine learning algorithms.

Techniques Used: Software, Marker

The assessment of diagnostic efficiency within the GSE51588 , GSE114007 and GSE82107 dataset for the random forest model prediction (A) The confusion matrix displays the predictive efficacy of the osteoblastic ARGs model across Training ( GSE51588 ) dataset, GSE114007 and GSE82107 dataset. Sample of True negative (top left), true positive (bottom right), false negative(bottom left) and false positive(top right) have been shown. Red represents less number; purple represents more. (B) The ROC curve of 3-gene random forest model to training group and another 2 external validation groups. (C) The ROC analyses exhibit DDIT3, VEGFA, and JUN respective OA diagnostic performance within training and validation datasets. (D) Western blot analysis the expression of JUN, VEGFA, DDIT3 in 20% mechanical loading of osteoblasts. (E) Quantification of the expression of JUN, VEGFA, DDIT3 in 20% mechanical loading of osteoblasts. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ns, p > 0.05).

Figure Legend Snippet: The assessment of diagnostic efficiency within the GSE51588 , GSE114007 and GSE82107 dataset for the random forest model prediction (A) The confusion matrix displays the predictive efficacy of the osteoblastic ARGs model across Training ( GSE51588 ) dataset, GSE114007 and GSE82107 dataset. Sample of True negative (top left), true positive (bottom right), false negative(bottom left) and false positive(top right) have been shown. Red represents less number; purple represents more. (B) The ROC curve of 3-gene random forest model to training group and another 2 external validation groups. (C) The ROC analyses exhibit DDIT3, VEGFA, and JUN respective OA diagnostic performance within training and validation datasets. (D) Western blot analysis the expression of JUN, VEGFA, DDIT3 in 20% mechanical loading of osteoblasts. (E) Quantification of the expression of JUN, VEGFA, DDIT3 in 20% mechanical loading of osteoblasts. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ns, p > 0.05).

Techniques Used: Diagnostic Assay, Biomarker Discovery, Western Blot, Expressing

Osteoblastic autophagy-related genes were upregulated in mouse OA model (A) Representative images of safranin O/fast green staining (top) and immunofluorescence of LC3 and OCN, DDIT3 and OCN, JUN and OCN, VEGFA and OCN (middle and bottom) co-immunostaining in the tibial subchondral bone of controls and DMM mice. Scale bar = 100 μm. (B) Quantitative analysis of the OARSI score of controls and DMM mice. n = 5 per group. (C–F) Quantitative analysis of the percentage of LC3 + , DDIT3 + , JUN + , and VEGFA + cells in OCN + cells of controls and DMM mice. n = 5 per group. (G) Representative images of safranin O/fast green staining (top) and immunofluorescence of LC3 and OCN, DDIT3 and OCN, JUN and OCN, VEGFA and OCN (middle and bottom) co-immunostaining in the tibial subchondral bone of mice aged 4 and 24 months. Scale bar = 100 μm. (H) Quantitative analysis of the OARSI score of mice aged 4 and 24 months. n = 6 per group. Scale bar = 100 μm. (I–L) Quantitative analysis of the percentage of LC3 + , DDIT3 + , JUN + , VEGFA + cells in OCN + cells of mice aged 4 and 24 months. n = 5 per group. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001; ns, p > 0.05).

Figure Legend Snippet: Osteoblastic autophagy-related genes were upregulated in mouse OA model (A) Representative images of safranin O/fast green staining (top) and immunofluorescence of LC3 and OCN, DDIT3 and OCN, JUN and OCN, VEGFA and OCN (middle and bottom) co-immunostaining in the tibial subchondral bone of controls and DMM mice. Scale bar = 100 μm. (B) Quantitative analysis of the OARSI score of controls and DMM mice. n = 5 per group. (C–F) Quantitative analysis of the percentage of LC3 + , DDIT3 + , JUN + , and VEGFA + cells in OCN + cells of controls and DMM mice. n = 5 per group. (G) Representative images of safranin O/fast green staining (top) and immunofluorescence of LC3 and OCN, DDIT3 and OCN, JUN and OCN, VEGFA and OCN (middle and bottom) co-immunostaining in the tibial subchondral bone of mice aged 4 and 24 months. Scale bar = 100 μm. (H) Quantitative analysis of the OARSI score of mice aged 4 and 24 months. n = 6 per group. Scale bar = 100 μm. (I–L) Quantitative analysis of the percentage of LC3 + , DDIT3 + , JUN + , VEGFA + cells in OCN + cells of mice aged 4 and 24 months. n = 5 per group. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001; ns, p > 0.05).

Techniques Used: Staining, Immunofluorescence, Immunostaining

Osteoblastic autophagy-related genes were upregulated in OA patients (A) Representative images of safranin O/fast green staining (top) and immunofluorescence of LC3 and OCN, DDIT3 and OCN, JUN and OCN, VEGFA and OCN (middle and bottom) co-immunostaining in the tibial subchondral bone of OA patients. Scale bar = 100 μm. (B) Schematic diagram of tibial plateau tissue: in dotted line is the medial, corresponding to the Late-OA(OA group), and the opposite side is the lateral side which as the Early-OA(control group). (C) Quantitative analysis of the OARSI score of early-OA and late-OA. n = 5 per group. (D, E, F and G) Quantitative analysis of the percentage of LC3 + , DDIT3 + , JUN + , and VEGFA + cells in OCN + cells of early-OA and late-OA. n = 5 per group. (H) Western blot analysis the expression of JUN, DDIT3, VEGFA, LC3, COL1A1, RUNX2, and OCN in knee subchondral bone of OA. (I) Quantification of the expression of JUN, DDIT3, VEGFA, LC3, COL1A1, RUNX2, and OCN in knee subchondral bone of OA. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001; ns, p > 0.05).

Figure Legend Snippet: Osteoblastic autophagy-related genes were upregulated in OA patients (A) Representative images of safranin O/fast green staining (top) and immunofluorescence of LC3 and OCN, DDIT3 and OCN, JUN and OCN, VEGFA and OCN (middle and bottom) co-immunostaining in the tibial subchondral bone of OA patients. Scale bar = 100 μm. (B) Schematic diagram of tibial plateau tissue: in dotted line is the medial, corresponding to the Late-OA(OA group), and the opposite side is the lateral side which as the Early-OA(control group). (C) Quantitative analysis of the OARSI score of early-OA and late-OA. n = 5 per group. (D, E, F and G) Quantitative analysis of the percentage of LC3 + , DDIT3 + , JUN + , and VEGFA + cells in OCN + cells of early-OA and late-OA. n = 5 per group. (H) Western blot analysis the expression of JUN, DDIT3, VEGFA, LC3, COL1A1, RUNX2, and OCN in knee subchondral bone of OA. (I) Quantification of the expression of JUN, DDIT3, VEGFA, LC3, COL1A1, RUNX2, and OCN in knee subchondral bone of OA. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001; ns, p > 0.05).

Techniques Used: Staining, Immunofluorescence, Immunostaining, Control, Western Blot, Expressing

Osteoblastic ARGs indirectly affect cartilage metabolism through the regulation of osteoblast activity (A) Schematic diagram of co-culture of osteoblasts and tibial plateau cartilage explants. (B) Western blot analysis the expression of DDIT3, JUN, VEGFA, Runx2, and LC3 in osteoblasts transfected with siRNA. (C) Quantification of the expression of DDIT3, JUN, VEGFA, Runx2, and LC3 in osteoblasts transfected with siRNA. n = 3 per group. (D) Representative images of Safranin O-Fast Green staining (top) and immunohistochemistry (IHC) of COL2 and MMP13 (middle and bottom) in the mouse tibial plateau cartilage explants co-cultured with osteoblasts. Scale bar = 50 μm. (E) Quantitative analysis of Safranin O staining and grade of mouse tibial plateaus cartilage explants co-cultured with osteoblasts. n = 3 per group. (F) Quantitative analysis of COL2 and MMP13 in the mouse tibial plateau cartilage explants co-cultured with osteoblasts. n = 3 per group. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001; ns, p > 0.05).

Figure Legend Snippet: Osteoblastic ARGs indirectly affect cartilage metabolism through the regulation of osteoblast activity (A) Schematic diagram of co-culture of osteoblasts and tibial plateau cartilage explants. (B) Western blot analysis the expression of DDIT3, JUN, VEGFA, Runx2, and LC3 in osteoblasts transfected with siRNA. (C) Quantification of the expression of DDIT3, JUN, VEGFA, Runx2, and LC3 in osteoblasts transfected with siRNA. n = 3 per group. (D) Representative images of Safranin O-Fast Green staining (top) and immunohistochemistry (IHC) of COL2 and MMP13 (middle and bottom) in the mouse tibial plateau cartilage explants co-cultured with osteoblasts. Scale bar = 50 μm. (E) Quantitative analysis of Safranin O staining and grade of mouse tibial plateaus cartilage explants co-cultured with osteoblasts. n = 3 per group. (F) Quantitative analysis of COL2 and MMP13 in the mouse tibial plateau cartilage explants co-cultured with osteoblasts. n = 3 per group. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001; ns, p > 0.05).

Techniques Used: Activity Assay, Co-Culture Assay, Western Blot, Expressing, Transfection, Staining, Immunohistochemistry, Cell Culture

Figure Legend Snippet:

Techniques Used: Software

Image Search Results

Journal: iScience

Article Title: Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis

doi: 10.1016/j.isci.2024.110130

Figure Lengend Snippet: Incorporating various machine learning techniques and protein-protein interaction (PPI) networks into the comprehensive screening of osteoblastic ARGs (A) The interplay relationships within the PPI network of relevant genes. (B) Sorting of the top 20 crucial genes for DEGREE, CLOSENESS, and EPC algorithm from Cytoscape software. (C) Mean decrease Impurity score and mean decrease Accuracy coefficient of each marker in Extra Tree Classifier model. (D) The proportion of Boruta algorithm decision across training dataset and testing dataset. (E) The optimal number of features that ensures the best predictive performance through lasso regression analysis. (F) The SVM-RFE method further compresses the variable features. (G) The most significant features DDIT3, JUN and VEGFA are jointly determined by the PPI network analysis and multiple machine learning algorithms.

Article Snippet: Rabbit polyclonal anti-CHOP (DDIT3) , Proteintech , Cat#15204-1-AP RRID: AB_2292610.

Techniques: Software, Marker

Journal: iScience

Article Title: Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis

doi: 10.1016/j.isci.2024.110130

Figure Lengend Snippet: The assessment of diagnostic efficiency within the GSE51588 , GSE114007 and GSE82107 dataset for the random forest model prediction (A) The confusion matrix displays the predictive efficacy of the osteoblastic ARGs model across Training ( GSE51588 ) dataset, GSE114007 and GSE82107 dataset. Sample of True negative (top left), true positive (bottom right), false negative(bottom left) and false positive(top right) have been shown. Red represents less number; purple represents more. (B) The ROC curve of 3-gene random forest model to training group and another 2 external validation groups. (C) The ROC analyses exhibit DDIT3, VEGFA, and JUN respective OA diagnostic performance within training and validation datasets. (D) Western blot analysis the expression of JUN, VEGFA, DDIT3 in 20% mechanical loading of osteoblasts. (E) Quantification of the expression of JUN, VEGFA, DDIT3 in 20% mechanical loading of osteoblasts. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ns, p > 0.05).

Article Snippet: Rabbit polyclonal anti-CHOP (DDIT3) , Proteintech , Cat#15204-1-AP RRID: AB_2292610.

Techniques: Diagnostic Assay, Biomarker Discovery, Western Blot, Expressing

Journal: iScience

Article Title: Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis

doi: 10.1016/j.isci.2024.110130

Figure Lengend Snippet: Osteoblastic autophagy-related genes were upregulated in mouse OA model (A) Representative images of safranin O/fast green staining (top) and immunofluorescence of LC3 and OCN, DDIT3 and OCN, JUN and OCN, VEGFA and OCN (middle and bottom) co-immunostaining in the tibial subchondral bone of controls and DMM mice. Scale bar = 100 μm. (B) Quantitative analysis of the OARSI score of controls and DMM mice. n = 5 per group. (C–F) Quantitative analysis of the percentage of LC3 + , DDIT3 + , JUN + , and VEGFA + cells in OCN + cells of controls and DMM mice. n = 5 per group. (G) Representative images of safranin O/fast green staining (top) and immunofluorescence of LC3 and OCN, DDIT3 and OCN, JUN and OCN, VEGFA and OCN (middle and bottom) co-immunostaining in the tibial subchondral bone of mice aged 4 and 24 months. Scale bar = 100 μm. (H) Quantitative analysis of the OARSI score of mice aged 4 and 24 months. n = 6 per group. Scale bar = 100 μm. (I–L) Quantitative analysis of the percentage of LC3 + , DDIT3 + , JUN + , VEGFA + cells in OCN + cells of mice aged 4 and 24 months. n = 5 per group. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001; ns, p > 0.05).

Article Snippet: Rabbit polyclonal anti-CHOP (DDIT3) , Proteintech , Cat#15204-1-AP RRID: AB_2292610.

Techniques: Staining, Immunofluorescence, Immunostaining

Journal: iScience

Article Title: Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis

doi: 10.1016/j.isci.2024.110130

Figure Lengend Snippet: Osteoblastic autophagy-related genes were upregulated in OA patients (A) Representative images of safranin O/fast green staining (top) and immunofluorescence of LC3 and OCN, DDIT3 and OCN, JUN and OCN, VEGFA and OCN (middle and bottom) co-immunostaining in the tibial subchondral bone of OA patients. Scale bar = 100 μm. (B) Schematic diagram of tibial plateau tissue: in dotted line is the medial, corresponding to the Late-OA(OA group), and the opposite side is the lateral side which as the Early-OA(control group). (C) Quantitative analysis of the OARSI score of early-OA and late-OA. n = 5 per group. (D, E, F and G) Quantitative analysis of the percentage of LC3 + , DDIT3 + , JUN + , and VEGFA + cells in OCN + cells of early-OA and late-OA. n = 5 per group. (H) Western blot analysis the expression of JUN, DDIT3, VEGFA, LC3, COL1A1, RUNX2, and OCN in knee subchondral bone of OA. (I) Quantification of the expression of JUN, DDIT3, VEGFA, LC3, COL1A1, RUNX2, and OCN in knee subchondral bone of OA. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001; ns, p > 0.05).

Article Snippet: Rabbit polyclonal anti-CHOP (DDIT3) , Proteintech , Cat#15204-1-AP RRID: AB_2292610.

Techniques: Staining, Immunofluorescence, Immunostaining, Control, Western Blot, Expressing

Journal: iScience

Article Title: Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis

doi: 10.1016/j.isci.2024.110130

Figure Lengend Snippet: Osteoblastic ARGs indirectly affect cartilage metabolism through the regulation of osteoblast activity (A) Schematic diagram of co-culture of osteoblasts and tibial plateau cartilage explants. (B) Western blot analysis the expression of DDIT3, JUN, VEGFA, Runx2, and LC3 in osteoblasts transfected with siRNA. (C) Quantification of the expression of DDIT3, JUN, VEGFA, Runx2, and LC3 in osteoblasts transfected with siRNA. n = 3 per group. (D) Representative images of Safranin O-Fast Green staining (top) and immunohistochemistry (IHC) of COL2 and MMP13 (middle and bottom) in the mouse tibial plateau cartilage explants co-cultured with osteoblasts. Scale bar = 50 μm. (E) Quantitative analysis of Safranin O staining and grade of mouse tibial plateaus cartilage explants co-cultured with osteoblasts. n = 3 per group. (F) Quantitative analysis of COL2 and MMP13 in the mouse tibial plateau cartilage explants co-cultured with osteoblasts. n = 3 per group. (∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001; ∗∗∗∗ p < 0.0001; ns, p > 0.05).

Article Snippet: Rabbit polyclonal anti-CHOP (DDIT3) , Proteintech , Cat#15204-1-AP RRID: AB_2292610.

Techniques: Activity Assay, Co-Culture Assay, Western Blot, Expressing, Transfection, Staining, Immunohistochemistry, Cell Culture

Journal: iScience

Article Title: Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis

doi: 10.1016/j.isci.2024.110130

Figure Lengend Snippet:

Article Snippet: Rabbit polyclonal anti-CHOP (DDIT3) , Proteintech , Cat#15204-1-AP RRID: AB_2292610.

Techniques: Software

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