csk antibody Search Results


93
Proteintech csk
FIGURE 5. pDCs lack the activating adaptor molecules but express inhibitory adaptor molecules of SLAM receptors. (A) IFN-a production by RNA-IC–stimulated pDCs after cross-linking of CD319, CD229, or BDCA-2 by plate-bound mAbs. IFN-a levels were determined in 20-h cell culture supernatants by an immunoassay and normalized to the IFN-a production in cultures without plate-bound mAb. The dashed line indicates 100%. Data (mean + SD) are from three donors from two independent experiments. Expression <t>of</t> <t>EAT2</t> and SAP (B) and SHIP-1, SHP-1, SHP- 2, and <t>CSK</t> (C) in cell lysates from isolated pDCs or NK cells from two healthy donors was determined by Western blot. Expression of b-actin was used as control.
Csk, supplied by Proteintech, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Santa Cruz Biotechnology anti csk
FIGURE 5. pDCs lack the activating adaptor molecules but express inhibitory adaptor molecules of SLAM receptors. (A) IFN-a production by RNA-IC–stimulated pDCs after cross-linking of CD319, CD229, or BDCA-2 by plate-bound mAbs. IFN-a levels were determined in 20-h cell culture supernatants by an immunoassay and normalized to the IFN-a production in cultures without plate-bound mAb. The dashed line indicates 100%. Data (mean + SD) are from three donors from two independent experiments. Expression <t>of</t> <t>EAT2</t> and SAP (B) and SHIP-1, SHP-1, SHP- 2, and <t>CSK</t> (C) in cell lysates from isolated pDCs or NK cells from two healthy donors was determined by Western blot. Expression of b-actin was used as control.
Anti Csk, supplied by Santa Cruz Biotechnology, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Proteintech pag1
Development and validation of the nomogram-based predictive model (A) Nomogram diagnostic prediction model constructed using G0S2, ITM2A, <t>PAG1,</t> and GZMA. (B) Calibration curves assessing consistency between predicted and actual observed values. (C) Decision Curve Analysis (DCA) evaluating the clinical net benefit of the nomogram. The x-axis represents the threshold probability for diagnosing sepsis, while the y-axis represents the net benefit. The curve for our model (red line) shows a greater net benefit across a wide range of clinically relevant thresholds compared to the strategies of treating all patients (dashed gray line) or no patients (solid blue line), indicating its potential for clinical utility. (D) Clinical impact curve illustrating the practical application of the nomogram. At any given risk threshold on the x-axis, the red curve shows the number of individuals predicted to be high-risk, while the blue curve shows the number of true positive cases within that group. The close approximation of the two curves demonstrates the model’s strong performance in accurately identifying patients at high risk of sepsis in a clinical setting. (E–H) ROC curve analyses evaluating predictive accuracy of the model in the training and multiple validation datasets.
Pag1, supplied by Proteintech, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Atlas Antibodies rabbit anti csk
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MedChemExpress phosphorus reduction goal
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Becton Dickinson mouse monoclonal anti-csk antibody
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Upstate Biotechnology Inc rabbit anti-csk polyclonal antibody
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Image Search Results


FIGURE 5. pDCs lack the activating adaptor molecules but express inhibitory adaptor molecules of SLAM receptors. (A) IFN-a production by RNA-IC–stimulated pDCs after cross-linking of CD319, CD229, or BDCA-2 by plate-bound mAbs. IFN-a levels were determined in 20-h cell culture supernatants by an immunoassay and normalized to the IFN-a production in cultures without plate-bound mAb. The dashed line indicates 100%. Data (mean + SD) are from three donors from two independent experiments. Expression of EAT2 and SAP (B) and SHIP-1, SHP-1, SHP- 2, and CSK (C) in cell lysates from isolated pDCs or NK cells from two healthy donors was determined by Western blot. Expression of b-actin was used as control.

Journal: Journal of immunology (Baltimore, Md. : 1950)

Article Title: Systemic lupus erythematosus immune complexes increase the expression of SLAM family members CD319 (CRACC) and CD229 (LY-9) on plasmacytoid dendritic cells and CD319 on CD56(dim) NK cells.

doi: 10.4049/jimmunol.1301022

Figure Lengend Snippet: FIGURE 5. pDCs lack the activating adaptor molecules but express inhibitory adaptor molecules of SLAM receptors. (A) IFN-a production by RNA-IC–stimulated pDCs after cross-linking of CD319, CD229, or BDCA-2 by plate-bound mAbs. IFN-a levels were determined in 20-h cell culture supernatants by an immunoassay and normalized to the IFN-a production in cultures without plate-bound mAb. The dashed line indicates 100%. Data (mean + SD) are from three donors from two independent experiments. Expression of EAT2 and SAP (B) and SHIP-1, SHP-1, SHP- 2, and CSK (C) in cell lysates from isolated pDCs or NK cells from two healthy donors was determined by Western blot. Expression of b-actin was used as control.

Article Snippet: Rabbit polyclonal Abs to Ewing’s sarcoma-activated transcript 2 (EAT2), SHIP-1, SHP-2, CSK (Proteintech Group, Chicago, IL), and a rabbit mAb to SHP-1 (EPR5519; Epitomics, Burlingame, CA), followed by HRP-conjugated goat anti-rabbit IgG (H+L) (Invitrogen), were used to detect the indicated signaling molecules.

Techniques: Cell Culture, Expressing, Isolation, Western Blot, Control

Development and validation of the nomogram-based predictive model (A) Nomogram diagnostic prediction model constructed using G0S2, ITM2A, PAG1, and GZMA. (B) Calibration curves assessing consistency between predicted and actual observed values. (C) Decision Curve Analysis (DCA) evaluating the clinical net benefit of the nomogram. The x-axis represents the threshold probability for diagnosing sepsis, while the y-axis represents the net benefit. The curve for our model (red line) shows a greater net benefit across a wide range of clinically relevant thresholds compared to the strategies of treating all patients (dashed gray line) or no patients (solid blue line), indicating its potential for clinical utility. (D) Clinical impact curve illustrating the practical application of the nomogram. At any given risk threshold on the x-axis, the red curve shows the number of individuals predicted to be high-risk, while the blue curve shows the number of true positive cases within that group. The close approximation of the two curves demonstrates the model’s strong performance in accurately identifying patients at high risk of sepsis in a clinical setting. (E–H) ROC curve analyses evaluating predictive accuracy of the model in the training and multiple validation datasets.

Journal: Frontiers in Pharmacology

Article Title: Decoding monocyte heterogeneity in sepsis: a single-cell apoptotic signature for immune stratification and guiding precision therapy

doi: 10.3389/fphar.2025.1675887

Figure Lengend Snippet: Development and validation of the nomogram-based predictive model (A) Nomogram diagnostic prediction model constructed using G0S2, ITM2A, PAG1, and GZMA. (B) Calibration curves assessing consistency between predicted and actual observed values. (C) Decision Curve Analysis (DCA) evaluating the clinical net benefit of the nomogram. The x-axis represents the threshold probability for diagnosing sepsis, while the y-axis represents the net benefit. The curve for our model (red line) shows a greater net benefit across a wide range of clinically relevant thresholds compared to the strategies of treating all patients (dashed gray line) or no patients (solid blue line), indicating its potential for clinical utility. (D) Clinical impact curve illustrating the practical application of the nomogram. At any given risk threshold on the x-axis, the red curve shows the number of individuals predicted to be high-risk, while the blue curve shows the number of true positive cases within that group. The close approximation of the two curves demonstrates the model’s strong performance in accurately identifying patients at high risk of sepsis in a clinical setting. (E–H) ROC curve analyses evaluating predictive accuracy of the model in the training and multiple validation datasets.

Article Snippet: The PVDF membranes were incubated overnight at 4 °C with primary antibodies targeting G0S2 (Abcam, ab236113; 1:1000), GZMA (Proteintech, 11288-1-AP; 1:1000), ITM2A (Proteintech, 14407-1-AP; 1:1000), and PAG1 (Proteintech, 25029-1-AP; 1:1000).

Techniques: Biomarker Discovery, Diagnostic Assay, Construct

KEY RESOURCES TABLE

Journal: Cell reports

Article Title: GDF6-CD99 Signaling Regulates Src and Ewing Sarcoma Growth

doi: 10.1016/j.celrep.2020.108332

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: The following antibodies were used: rabbit anti-FLI-1 (ab15289, Abcam); rabbit anti-GDF6 (Novus, NBP 1-91934); rabbit anti-caspase 3 (9665, Cell Signaling Technology); rabbit anti-PARP (sc-7150, Santa Cruz Biotechnology); mouse anti-CD99 (MS-1633, Lab Vision); sheep anti-CD99L2 (AF5185, R&D Systems); mouse anti-p21 (BD PharMingen, 556430); mouse anti-tubulin (Developmental Studies Hybridoma Bank); rabbit anti-GAPDH (sc-25778, Santa Cruz Biotechnology); rabbit anti-CSK (HPA028425, Atlas Antibodies); mouse anti-GST (MS-707-P0, Lab Vision); mouse anti-GFP (sc-9996, Santa Cruz Biotechnology); rabbit anti-actin (8457, Cell Signaling Technology); rabbit anti-Src (2123, Cell Signaling Technology); rabbit anti-phospho-Src (2101, Cell Signaling Technology); rabbit anti-phospho-Cortactin (4569, Cell Signaling Technology); rabbit anti-phospho-p130 Cas (4011, Cell Signaling Technology); rabbit anti-phospho-STAT3 (9145, Cell Signaling Technology); rabbit anti-phospho-Smad1/5 (9516, Cell Signaling Technology); rabbit anti-HA (3724, Cell Signaling Technology); and mouse anti-FLAG (F1804, Sigma-Aldrich).

Techniques: Control, Virus, Recombinant, Transfection, Reverse Transcription, SYBR Green Assay, In Situ, Labeling, Plasmid Preparation