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Showing 1 - 20 results of 53 for search '(( significant decrease decrease ) OR ( ((significant genes) OR (significant inverse)) decrease ))~', query time: 0.56s Refine Results
  1. 1
    Differential gene sets associated with the transcriptional host response to SARS-CoV-2 infection across serially collected samples.

    Differential gene sets associated with the transcriptional host response to SARS-CoV-2 infection across serially collected samples. by Vasanthi Avadhanula (259546)

    Published 2025
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  2. 2
    Differential expression patterns and functional gene groups associated with SARS-CoV-2 viral load across serially collected samples.

    Differential expression patterns and functional gene groups associated with SARS-CoV-2 viral load across serially collected samples. by Vasanthi Avadhanula (259546)

    Published 2025
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  3. 3
    EF-24 treatment modulates gene expression in THP-1 cells.

    EF-24 treatment modulates gene expression in THP-1 cells. by Ajeet P. Singh (22298432)

    Published 2025
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  4. 4
    The contribution of O-ag length and the Vi capsule to serum resistance is serovar dependent.

    The contribution of O-ag length and the Vi capsule to serum resistance is serovar dependent. by Fermin E. Guerra (6084545)

    Published 2025
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  5. 5
    Supplementary Table 1–9.

    Supplementary Table 1–9. by Yu Wang (12152)

    Published 2025
    “…Moreover, genetic mimicry near the ANGPTL3 and LPL gene suggested that reducing the activity of ANGPTL3 and LPL (mimicking antisense anti-ANGPTL3 and LPL agents) was forecasted to decrease sepsis risk.…”
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  6. 6
    STROBE-MR checklist.

    STROBE-MR checklist. by Yu Wang (12152)

    Published 2025
    “…Moreover, genetic mimicry near the ANGPTL3 and LPL gene suggested that reducing the activity of ANGPTL3 and LPL (mimicking antisense anti-ANGPTL3 and LPL agents) was forecasted to decrease sepsis risk.…”
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  7. 7
    Instrumental Variables details of lipids.

    Instrumental Variables details of lipids. by Yu Wang (12152)

    Published 2025
    “…Moreover, genetic mimicry near the ANGPTL3 and LPL gene suggested that reducing the activity of ANGPTL3 and LPL (mimicking antisense anti-ANGPTL3 and LPL agents) was forecasted to decrease sepsis risk.…”
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  8. 8
    Table 1_Mendelian randomization and bioinformatics unveil potential links between gut microbial genera and colorectal cancer.xlsx

    Table 1_Mendelian randomization and bioinformatics unveil potential links between gut microbial genera and colorectal cancer.xlsx by Long Wu (655081)

    Published 2024
    “…Background<p>Colorectal cancer (CRC) poses a significant global health burden, with high incidence and mortality rates. …”
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  9. 9
    Table 4_Mendelian randomization and bioinformatics unveil potential links between gut microbial genera and colorectal cancer.xlsx

    Table 4_Mendelian randomization and bioinformatics unveil potential links between gut microbial genera and colorectal cancer.xlsx by Long Wu (655081)

    Published 2024
    “…Background<p>Colorectal cancer (CRC) poses a significant global health burden, with high incidence and mortality rates. …”
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  10. 10
    Table 2_Mendelian randomization and bioinformatics unveil potential links between gut microbial genera and colorectal cancer.xlsx

    Table 2_Mendelian randomization and bioinformatics unveil potential links between gut microbial genera and colorectal cancer.xlsx by Long Wu (655081)

    Published 2024
    “…Background<p>Colorectal cancer (CRC) poses a significant global health burden, with high incidence and mortality rates. …”
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  11. 11
    Table 3_Mendelian randomization and bioinformatics unveil potential links between gut microbial genera and colorectal cancer.xlsx

    Table 3_Mendelian randomization and bioinformatics unveil potential links between gut microbial genera and colorectal cancer.xlsx by Long Wu (655081)

    Published 2024
    “…Background<p>Colorectal cancer (CRC) poses a significant global health burden, with high incidence and mortality rates. …”
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  12. 12
    Table2_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx

    Table2_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

    Published 2024
    “…</p>Methods<p>This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. …”
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  13. 13
    Table13_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx

    Table13_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

    Published 2024
    “…</p>Methods<p>This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. …”
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  14. 14
    Table12_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx

    Table12_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

    Published 2024
    “…</p>Methods<p>This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. …”
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  15. 15
    Table3_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx

    Table3_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

    Published 2024
    “…</p>Methods<p>This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. …”
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  16. 16
    Table9_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx

    Table9_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

    Published 2024
    “…</p>Methods<p>This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. …”
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  17. 17
    Image1_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.tif

    Image1_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.tif by Yifang He (18126257)

    Published 2024
    “…</p>Methods<p>This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. …”
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  18. 18
    Table7_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx

    Table7_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

    Published 2024
    “…</p>Methods<p>This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. …”
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  19. 19
    Table6_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx

    Table6_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

    Published 2024
    “…</p>Methods<p>This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. …”
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  20. 20
    Table11_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx

    Table11_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

    Published 2024
    “…</p>Methods<p>This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. …”
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