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primary antibodies against gal  (Bioss)


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    Bioss primary antibodies against gal
    Targeted additional analysis of the three key IR-DEGs (A) Chromosomal positions of the key IR-DEGs are presented. (B) A PCA plot illustrates the distribution of samples based on the expression profiles of the 3 key IR-DEGs. The x axis and y axis correspond to the first two principal components (PC1 and PC2), respectively, and the percentage of total variance explained by each component is indicated in parentheses adjacent to the axis labels. (C) Comparative expression levels of three crucial IR-DEGs in FGR, which integrates datasets GSE24129 , GSE100415 , and GSE147776 . (D) ROC curves were used to validate the efficacy of three crucial IR-DEGs in predicting FGR, quantifying the diagnostic performance of each gene for FGR identification. (E) A nomogram for predicting the risk of FGR is constructed based on three IR-DEGs, <t>specifically</t> <t>F2R,</t> <t>GAL,</t> and CXCL10. For each of these genes, a corresponding point value is assigned according to its expression level; the total point score is calculated by summing the individual gene points, and this total score is further converted to the predicted risk of developing FGR. (F) A calibration curve for the nomogram is shown, comparing the nomogram-predicted risk of FGR ( x axis) with the actually observed risk ( y axis). The diagonal line represents an ideal prediction scenario where predicted and observed risks are identical. The dashed line (“Apparent”) denotes the model’s performance before bias correction, while the solid line (“Bias-corrected”) represents performance after bias correction.
    Primary Antibodies Against Gal, supplied by Bioss, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/primary antibodies against gal/product/Bioss
    Average 94 stars, based on 1 article reviews
    primary antibodies against gal - by Bioz Stars, 2026-04
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    Images

    1) Product Images from "GAL and F2R as immune diagnostic biomarkers for fetal growth restriction"

    Article Title: GAL and F2R as immune diagnostic biomarkers for fetal growth restriction

    Journal: iScience

    doi: 10.1016/j.isci.2026.115228

    Targeted additional analysis of the three key IR-DEGs (A) Chromosomal positions of the key IR-DEGs are presented. (B) A PCA plot illustrates the distribution of samples based on the expression profiles of the 3 key IR-DEGs. The x axis and y axis correspond to the first two principal components (PC1 and PC2), respectively, and the percentage of total variance explained by each component is indicated in parentheses adjacent to the axis labels. (C) Comparative expression levels of three crucial IR-DEGs in FGR, which integrates datasets GSE24129 , GSE100415 , and GSE147776 . (D) ROC curves were used to validate the efficacy of three crucial IR-DEGs in predicting FGR, quantifying the diagnostic performance of each gene for FGR identification. (E) A nomogram for predicting the risk of FGR is constructed based on three IR-DEGs, specifically F2R, GAL, and CXCL10. For each of these genes, a corresponding point value is assigned according to its expression level; the total point score is calculated by summing the individual gene points, and this total score is further converted to the predicted risk of developing FGR. (F) A calibration curve for the nomogram is shown, comparing the nomogram-predicted risk of FGR ( x axis) with the actually observed risk ( y axis). The diagonal line represents an ideal prediction scenario where predicted and observed risks are identical. The dashed line (“Apparent”) denotes the model’s performance before bias correction, while the solid line (“Bias-corrected”) represents performance after bias correction.
    Figure Legend Snippet: Targeted additional analysis of the three key IR-DEGs (A) Chromosomal positions of the key IR-DEGs are presented. (B) A PCA plot illustrates the distribution of samples based on the expression profiles of the 3 key IR-DEGs. The x axis and y axis correspond to the first two principal components (PC1 and PC2), respectively, and the percentage of total variance explained by each component is indicated in parentheses adjacent to the axis labels. (C) Comparative expression levels of three crucial IR-DEGs in FGR, which integrates datasets GSE24129 , GSE100415 , and GSE147776 . (D) ROC curves were used to validate the efficacy of three crucial IR-DEGs in predicting FGR, quantifying the diagnostic performance of each gene for FGR identification. (E) A nomogram for predicting the risk of FGR is constructed based on three IR-DEGs, specifically F2R, GAL, and CXCL10. For each of these genes, a corresponding point value is assigned according to its expression level; the total point score is calculated by summing the individual gene points, and this total score is further converted to the predicted risk of developing FGR. (F) A calibration curve for the nomogram is shown, comparing the nomogram-predicted risk of FGR ( x axis) with the actually observed risk ( y axis). The diagonal line represents an ideal prediction scenario where predicted and observed risks are identical. The dashed line (“Apparent”) denotes the model’s performance before bias correction, while the solid line (“Bias-corrected”) represents performance after bias correction.

    Techniques Used: Expressing, Diagnostic Assay, Construct

    An evaluation focusing on immune infiltration related to two IR-DEGs (A) Stacked bar plot showing the relative abundance of 22 immune cell subtype proportions between FGR and AGA samples. (B) A boxplot is employed to visualize the differentiation in ratios of 22 immune cell types, with a specific focus on comparisons between FGR and AGA. (C) A Spearman correlation network is constructed to illustrate the correlative relationships between two IR-DEGs (namely GAL and F2R) and infiltrating immune cells in FGR. This network visualization explicitly displays how each of the four target IR-DEGs correlates with the 22 types of infiltrating immune cells, facilitating intuitive recognition of positive or negative correlation patterns between the genes and immune cell subsets. (D) Expression levels of F2R are significantly higher in FGR tissues ( n = 11) compared to AGA samples ( n = 25). Data are presented as mean ± SEM. (E) Expression levels of GAL are significantly elevated in FGR tissues ( n = 11) relative to AGA specimens ( n = 25). Data are presented as mean ± SEM. (F) qRT-PCR analysis demonstrates that F2R mRNA expression is significantly upregulated in FGR placental tissues compared to AGA controls ( p = 0.0019). Data are presented as mean ± SEM. (G) qRT-PCR analysis reveals a significant downregulation of GAL mRNA in FGR placental tissues compared to AGA controls ( p = 0.0015). Data are presented as mean ± SEM. Additionally, representative images of IHC staining for F2R and GAL in FGR and AGA patients are presented, illustrating both high and low expression levels of the two genes. All staining images are shown at magnifications of ×40 and ×200, with scale bars clearly indicated for reference. Statistical p values were calculated via the chi-square test, where ∗ p < 0.05 and ∗∗ p < 0.01.
    Figure Legend Snippet: An evaluation focusing on immune infiltration related to two IR-DEGs (A) Stacked bar plot showing the relative abundance of 22 immune cell subtype proportions between FGR and AGA samples. (B) A boxplot is employed to visualize the differentiation in ratios of 22 immune cell types, with a specific focus on comparisons between FGR and AGA. (C) A Spearman correlation network is constructed to illustrate the correlative relationships between two IR-DEGs (namely GAL and F2R) and infiltrating immune cells in FGR. This network visualization explicitly displays how each of the four target IR-DEGs correlates with the 22 types of infiltrating immune cells, facilitating intuitive recognition of positive or negative correlation patterns between the genes and immune cell subsets. (D) Expression levels of F2R are significantly higher in FGR tissues ( n = 11) compared to AGA samples ( n = 25). Data are presented as mean ± SEM. (E) Expression levels of GAL are significantly elevated in FGR tissues ( n = 11) relative to AGA specimens ( n = 25). Data are presented as mean ± SEM. (F) qRT-PCR analysis demonstrates that F2R mRNA expression is significantly upregulated in FGR placental tissues compared to AGA controls ( p = 0.0019). Data are presented as mean ± SEM. (G) qRT-PCR analysis reveals a significant downregulation of GAL mRNA in FGR placental tissues compared to AGA controls ( p = 0.0015). Data are presented as mean ± SEM. Additionally, representative images of IHC staining for F2R and GAL in FGR and AGA patients are presented, illustrating both high and low expression levels of the two genes. All staining images are shown at magnifications of ×40 and ×200, with scale bars clearly indicated for reference. Statistical p values were calculated via the chi-square test, where ∗ p < 0.05 and ∗∗ p < 0.01.

    Techniques Used: Construct, Expressing, Quantitative RT-PCR, Immunohistochemistry, Staining



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    Image Search Results


    Targeted additional analysis of the three key IR-DEGs (A) Chromosomal positions of the key IR-DEGs are presented. (B) A PCA plot illustrates the distribution of samples based on the expression profiles of the 3 key IR-DEGs. The x axis and y axis correspond to the first two principal components (PC1 and PC2), respectively, and the percentage of total variance explained by each component is indicated in parentheses adjacent to the axis labels. (C) Comparative expression levels of three crucial IR-DEGs in FGR, which integrates datasets GSE24129 , GSE100415 , and GSE147776 . (D) ROC curves were used to validate the efficacy of three crucial IR-DEGs in predicting FGR, quantifying the diagnostic performance of each gene for FGR identification. (E) A nomogram for predicting the risk of FGR is constructed based on three IR-DEGs, specifically F2R, GAL, and CXCL10. For each of these genes, a corresponding point value is assigned according to its expression level; the total point score is calculated by summing the individual gene points, and this total score is further converted to the predicted risk of developing FGR. (F) A calibration curve for the nomogram is shown, comparing the nomogram-predicted risk of FGR ( x axis) with the actually observed risk ( y axis). The diagonal line represents an ideal prediction scenario where predicted and observed risks are identical. The dashed line (“Apparent”) denotes the model’s performance before bias correction, while the solid line (“Bias-corrected”) represents performance after bias correction.

    Journal: iScience

    Article Title: GAL and F2R as immune diagnostic biomarkers for fetal growth restriction

    doi: 10.1016/j.isci.2026.115228

    Figure Lengend Snippet: Targeted additional analysis of the three key IR-DEGs (A) Chromosomal positions of the key IR-DEGs are presented. (B) A PCA plot illustrates the distribution of samples based on the expression profiles of the 3 key IR-DEGs. The x axis and y axis correspond to the first two principal components (PC1 and PC2), respectively, and the percentage of total variance explained by each component is indicated in parentheses adjacent to the axis labels. (C) Comparative expression levels of three crucial IR-DEGs in FGR, which integrates datasets GSE24129 , GSE100415 , and GSE147776 . (D) ROC curves were used to validate the efficacy of three crucial IR-DEGs in predicting FGR, quantifying the diagnostic performance of each gene for FGR identification. (E) A nomogram for predicting the risk of FGR is constructed based on three IR-DEGs, specifically F2R, GAL, and CXCL10. For each of these genes, a corresponding point value is assigned according to its expression level; the total point score is calculated by summing the individual gene points, and this total score is further converted to the predicted risk of developing FGR. (F) A calibration curve for the nomogram is shown, comparing the nomogram-predicted risk of FGR ( x axis) with the actually observed risk ( y axis). The diagonal line represents an ideal prediction scenario where predicted and observed risks are identical. The dashed line (“Apparent”) denotes the model’s performance before bias correction, while the solid line (“Bias-corrected”) represents performance after bias correction.

    Article Snippet: IHC staining was performed according to previously established protocols using primary antibodies against GAL (Bioss, Beijing, China, catalog number: bs-0017M, RRID: AB_10855141) and F2R (Bioss, Beijing, China, catalog number: bs-0828R, RRID: AB_10857704).

    Techniques: Expressing, Diagnostic Assay, Construct

    An evaluation focusing on immune infiltration related to two IR-DEGs (A) Stacked bar plot showing the relative abundance of 22 immune cell subtype proportions between FGR and AGA samples. (B) A boxplot is employed to visualize the differentiation in ratios of 22 immune cell types, with a specific focus on comparisons between FGR and AGA. (C) A Spearman correlation network is constructed to illustrate the correlative relationships between two IR-DEGs (namely GAL and F2R) and infiltrating immune cells in FGR. This network visualization explicitly displays how each of the four target IR-DEGs correlates with the 22 types of infiltrating immune cells, facilitating intuitive recognition of positive or negative correlation patterns between the genes and immune cell subsets. (D) Expression levels of F2R are significantly higher in FGR tissues ( n = 11) compared to AGA samples ( n = 25). Data are presented as mean ± SEM. (E) Expression levels of GAL are significantly elevated in FGR tissues ( n = 11) relative to AGA specimens ( n = 25). Data are presented as mean ± SEM. (F) qRT-PCR analysis demonstrates that F2R mRNA expression is significantly upregulated in FGR placental tissues compared to AGA controls ( p = 0.0019). Data are presented as mean ± SEM. (G) qRT-PCR analysis reveals a significant downregulation of GAL mRNA in FGR placental tissues compared to AGA controls ( p = 0.0015). Data are presented as mean ± SEM. Additionally, representative images of IHC staining for F2R and GAL in FGR and AGA patients are presented, illustrating both high and low expression levels of the two genes. All staining images are shown at magnifications of ×40 and ×200, with scale bars clearly indicated for reference. Statistical p values were calculated via the chi-square test, where ∗ p < 0.05 and ∗∗ p < 0.01.

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    Article Title: GAL and F2R as immune diagnostic biomarkers for fetal growth restriction

    doi: 10.1016/j.isci.2026.115228

    Figure Lengend Snippet: An evaluation focusing on immune infiltration related to two IR-DEGs (A) Stacked bar plot showing the relative abundance of 22 immune cell subtype proportions between FGR and AGA samples. (B) A boxplot is employed to visualize the differentiation in ratios of 22 immune cell types, with a specific focus on comparisons between FGR and AGA. (C) A Spearman correlation network is constructed to illustrate the correlative relationships between two IR-DEGs (namely GAL and F2R) and infiltrating immune cells in FGR. This network visualization explicitly displays how each of the four target IR-DEGs correlates with the 22 types of infiltrating immune cells, facilitating intuitive recognition of positive or negative correlation patterns between the genes and immune cell subsets. (D) Expression levels of F2R are significantly higher in FGR tissues ( n = 11) compared to AGA samples ( n = 25). Data are presented as mean ± SEM. (E) Expression levels of GAL are significantly elevated in FGR tissues ( n = 11) relative to AGA specimens ( n = 25). Data are presented as mean ± SEM. (F) qRT-PCR analysis demonstrates that F2R mRNA expression is significantly upregulated in FGR placental tissues compared to AGA controls ( p = 0.0019). Data are presented as mean ± SEM. (G) qRT-PCR analysis reveals a significant downregulation of GAL mRNA in FGR placental tissues compared to AGA controls ( p = 0.0015). Data are presented as mean ± SEM. Additionally, representative images of IHC staining for F2R and GAL in FGR and AGA patients are presented, illustrating both high and low expression levels of the two genes. All staining images are shown at magnifications of ×40 and ×200, with scale bars clearly indicated for reference. Statistical p values were calculated via the chi-square test, where ∗ p < 0.05 and ∗∗ p < 0.01.

    Article Snippet: IHC staining was performed according to previously established protocols using primary antibodies against GAL (Bioss, Beijing, China, catalog number: bs-0017M, RRID: AB_10855141) and F2R (Bioss, Beijing, China, catalog number: bs-0828R, RRID: AB_10857704).

    Techniques: Construct, Expressing, Quantitative RT-PCR, Immunohistochemistry, Staining