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human haptoglobin elisa kit  (Assaypro)


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    Assaypro human haptoglobin elisa kit
    Human Haptoglobin Elisa Kit, supplied by Assaypro, used in various techniques. Bioz Stars score: 94/100, based on 37 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/elisa+kit+human+haptoglobin+elisa+kit/Human+Haptoglobin+AssayMax+ELISA+Kit/10__17816_slash_pmj431111___123-53-38-42
    Average 94 stars, based on 37 article reviews
    human haptoglobin elisa kit - by Bioz Stars, 2026-11
    94/100 stars

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    Enzyme-linked Immunosorbent Assay:

    Article Title: Serum haptoglobin as a novel molecular biomarker predicting colorectal cancer hepatic metastasis.
    Article Snippet: .. Measurement of HP level in human blood samples A commercially available ELISA kit Human Haptoglobin ELISA Kit(Assaypro EH1003-1)was used to measure HP level. ..



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    Multi‐omics combined with Mendelian randomization for robust biomarker discovery in tuberculosis . (A) Volcano plot of differentially expressed genes (DEGs) between TB patients ( n = 46) and healthy controls ( n = 37) from GEO dataset GSE28623 . A total of 290 DEGs were identified (177 upregulated and 113 downregulated) under the criteria |log2FC| > 1.2 or < 0.83 and adjusted p < 0.05. (B) Integration pipeline: By combining genes related to PC(18:2/18:2) and PC(14:0/20:4) (PC‐gene), MR results for 4907 proteins (MR‐protein), proteomic differential expression results (Proteomics), and GEO transcriptomic data (GEO), we identified <t>haptoglobin</t> (HP) as a target biomarker. (C) Violin plot showing differential expression of HP between TB patients and healthy controls in GSE28623 . (D) Violin plot showing differential expression of HP between TB patients ( n = 8) and healthy controls ( n = 18) in GSE34608 . (E) ROC curve assessing the diagnostic performance of HP expression for discriminating TB patients from healthy controls in the GSE34608 dataset. AUC = 0.965 (95% CI: 0.875–1.000). (F) Confusion matrix for HP‐based classification of TB patients versus healthy controls in the GSE34608 dataset (“Yes” represents tuberculosis patients and “No” represents healthy people). (G) Violin plot showing differential expression of HP between TB patients ( n = 8) and healthy controls ( n = 8) in <t>ELISA.</t> (H) ROC curve assessing the diagnostic performance of HP expression for discriminating TB patients from healthy controls in the ELISA. AUC = 0.969 (95% CI: 0.903–1.000). (I) Confusion matrix for HP‐based classification of TB patients versus healthy controls in the ELISA (“Yes” represents tuberculosis patients and “No” represents healthy people).
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    Fig. 8. Validation of HP and FBN1 levels using ELISAs and ROC curve analysis. A, B. The <t>ELISA</t> results showed that the expression of HP was significantly upregulated and that FBN1 expression was significantly downregulated in the DME group, consistent with the results obtained using the label-free quantitative proteomic analysis (n = 17–20/group). C. ROC curves depicting the classification power of HP, FBN1 and the combination of HP and FBN1.
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    Image Search Results


    Multi‐omics combined with Mendelian randomization for robust biomarker discovery in tuberculosis . (A) Volcano plot of differentially expressed genes (DEGs) between TB patients ( n = 46) and healthy controls ( n = 37) from GEO dataset GSE28623 . A total of 290 DEGs were identified (177 upregulated and 113 downregulated) under the criteria |log2FC| > 1.2 or < 0.83 and adjusted p < 0.05. (B) Integration pipeline: By combining genes related to PC(18:2/18:2) and PC(14:0/20:4) (PC‐gene), MR results for 4907 proteins (MR‐protein), proteomic differential expression results (Proteomics), and GEO transcriptomic data (GEO), we identified haptoglobin (HP) as a target biomarker. (C) Violin plot showing differential expression of HP between TB patients and healthy controls in GSE28623 . (D) Violin plot showing differential expression of HP between TB patients ( n = 8) and healthy controls ( n = 18) in GSE34608 . (E) ROC curve assessing the diagnostic performance of HP expression for discriminating TB patients from healthy controls in the GSE34608 dataset. AUC = 0.965 (95% CI: 0.875–1.000). (F) Confusion matrix for HP‐based classification of TB patients versus healthy controls in the GSE34608 dataset (“Yes” represents tuberculosis patients and “No” represents healthy people). (G) Violin plot showing differential expression of HP between TB patients ( n = 8) and healthy controls ( n = 8) in ELISA. (H) ROC curve assessing the diagnostic performance of HP expression for discriminating TB patients from healthy controls in the ELISA. AUC = 0.969 (95% CI: 0.903–1.000). (I) Confusion matrix for HP‐based classification of TB patients versus healthy controls in the ELISA (“Yes” represents tuberculosis patients and “No” represents healthy people).

    Journal: MedComm

    Article Title: Robust Diagnostic and Therapeutic Biomarkers for Tuberculosis Identified Through Multi‐Omics and Mendelian Randomization Analysis

    doi: 10.1002/mco2.70559

    Figure Lengend Snippet: Multi‐omics combined with Mendelian randomization for robust biomarker discovery in tuberculosis . (A) Volcano plot of differentially expressed genes (DEGs) between TB patients ( n = 46) and healthy controls ( n = 37) from GEO dataset GSE28623 . A total of 290 DEGs were identified (177 upregulated and 113 downregulated) under the criteria |log2FC| > 1.2 or < 0.83 and adjusted p < 0.05. (B) Integration pipeline: By combining genes related to PC(18:2/18:2) and PC(14:0/20:4) (PC‐gene), MR results for 4907 proteins (MR‐protein), proteomic differential expression results (Proteomics), and GEO transcriptomic data (GEO), we identified haptoglobin (HP) as a target biomarker. (C) Violin plot showing differential expression of HP between TB patients and healthy controls in GSE28623 . (D) Violin plot showing differential expression of HP between TB patients ( n = 8) and healthy controls ( n = 18) in GSE34608 . (E) ROC curve assessing the diagnostic performance of HP expression for discriminating TB patients from healthy controls in the GSE34608 dataset. AUC = 0.965 (95% CI: 0.875–1.000). (F) Confusion matrix for HP‐based classification of TB patients versus healthy controls in the GSE34608 dataset (“Yes” represents tuberculosis patients and “No” represents healthy people). (G) Violin plot showing differential expression of HP between TB patients ( n = 8) and healthy controls ( n = 8) in ELISA. (H) ROC curve assessing the diagnostic performance of HP expression for discriminating TB patients from healthy controls in the ELISA. AUC = 0.969 (95% CI: 0.903–1.000). (I) Confusion matrix for HP‐based classification of TB patients versus healthy controls in the ELISA (“Yes” represents tuberculosis patients and “No” represents healthy people).

    Article Snippet: Plasma HP concentrations were measured using the QuicKey Pro Human HP (Haptoglobin) ELISA Kit (Catalog No. E‐OSEL‐H0044; Elabscience) according to the manufacturer's instructions.

    Techniques: Biomarker Discovery, Quantitative Proteomics, Diagnostic Assay, Expressing, Enzyme-linked Immunosorbent Assay

    Fig. 8. Validation of HP and FBN1 levels using ELISAs and ROC curve analysis. A, B. The ELISA results showed that the expression of HP was significantly upregulated and that FBN1 expression was significantly downregulated in the DME group, consistent with the results obtained using the label-free quantitative proteomic analysis (n = 17–20/group). C. ROC curves depicting the classification power of HP, FBN1 and the combination of HP and FBN1.

    Journal: Experimental eye research

    Article Title: Proteomic analysis of aqueous humor reveals novel regulators of diabetic macular edema.

    doi: 10.1016/j.exer.2023.109724

    Figure Lengend Snippet: Fig. 8. Validation of HP and FBN1 levels using ELISAs and ROC curve analysis. A, B. The ELISA results showed that the expression of HP was significantly upregulated and that FBN1 expression was significantly downregulated in the DME group, consistent with the results obtained using the label-free quantitative proteomic analysis (n = 17–20/group). C. ROC curves depicting the classification power of HP, FBN1 and the combination of HP and FBN1.

    Article Snippet: A Human HP/haptoglobin Quantikine ELISA Kit (Cat.#: DHAPG0; R&D Systems) and Human FBN1/F/Fibrillin 1 ELISA Kit (Cat.#: LS-F26348-1; LSBio, Washington, USA) were used to measure the concentrations of AH proteins in the 4 different groups according to the manufacturer’s instructions, and all samples were measured in triplicate.

    Techniques: Biomarker Discovery, Enzyme-linked Immunosorbent Assay, Expressing