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Showing 17,941 - 17,960 results of 20,810 for search '(( significance p decrease ) OR ( significant decrease decrease ))~', query time: 0.44s Refine Results
  1. 17941
    Table 1_Elucidating the causal relationship between 486 genetically predicted blood metabolites and the risk of gastric cancer: a comprehensive Mendelian randomization analysis.xlsx

    Table 1_Elucidating the causal relationship between 486 genetically predicted blood metabolites and the risk of gastric cancer: a comprehensive Mendelian randomization analysis.xls... by Lei Qian (292302)

    Published 2024
    “…Serum samples were collected from both groups for the evaluation of blood-related metabolite expression levels using advanced techniques such as ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and gas chromatography-mass spectrometry (GC-MS/MS).</p>Results<p>The MVMR analysis revealed a significant association between genetically predicted elevated levels of tryptophan (odds ratio [OR] = 0.523, 95% confidence interval [CI] = 0.313–0.872, p = 0.013), nonadecanoate (19:0) (odds ratio [OR] = 0.460, 95% confidence interval [CI] = 0.225–0.943, p = 0.034), and erythritol (odds ratio [OR] = 0.672, 95% confidence interval [CI] = 0.468–0.930, p = 0.016) with a decreased risk of gastric cancer. …”
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  2. 17942
    SNAP-25 (1-197) binds to syntaxin-1A, and the interaction is enhanced by Ser187 phosphorylation.

    SNAP-25 (1-197) binds to syntaxin-1A, and the interaction is enhanced by Ser187 phosphorylation. by Dilara Koc (22431017)

    Published 2025
    “…Syntaxin-1A intensity normalized to SNAP-25 levels in each IP blot was quantified. ns: non-significant; *: P ≤ 0.05; **: P ≤ 0.01; ***: P ≤ 0.001. …”
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  3. 17943
    Minimal test data set

    Minimal test data set by Wenshun Sheng (21485393)

    Published 2025
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  4. 17944
    OSNet network structure.

    OSNet network structure. by Wenshun Sheng (21485393)

    Published 2025
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  5. 17945
    YOLOv8 overall framework.

    YOLOv8 overall framework. by Wenshun Sheng (21485393)

    Published 2025
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  6. 17946
    The performance of YOFGD model on MOT16.

    The performance of YOFGD model on MOT16. by Wenshun Sheng (21485393)

    Published 2025
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  7. 17947
    Network structure of OSA.

    Network structure of OSA. by Wenshun Sheng (21485393)

    Published 2025
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  8. 17948
    The performance of S-YOFEO model on MOT17.

    The performance of S-YOFEO model on MOT17. by Wenshun Sheng (21485393)

    Published 2025
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  9. 17949
    Five multi-target tracking evaluation indexes.

    Five multi-target tracking evaluation indexes. by Wenshun Sheng (21485393)

    Published 2025
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  10. 17950
    Algorithm flowchart of OFEO.

    Algorithm flowchart of OFEO. by Wenshun Sheng (21485393)

    Published 2025
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  11. 17951
    Partial tracking results of MOT17 dataset.

    Partial tracking results of MOT17 dataset. by Wenshun Sheng (21485393)

    Published 2025
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  12. 17952
    Improved detection layer.

    Improved detection layer. by Wenshun Sheng (21485393)

    Published 2025
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  13. 17953
    The performance of S-YOFEO model on MOT16.

    The performance of S-YOFEO model on MOT16. by Wenshun Sheng (21485393)

    Published 2025
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  14. 17954
    The matching process of EIOU.

    The matching process of EIOU. by Wenshun Sheng (21485393)

    Published 2025
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  15. 17955
    Table 1_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx

    Table 1_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

    Published 2025
    “…Background<p>Hypertensive heart disease (HHD) poses a substantial global public health burden, particularly among older adults. …”
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  16. 17956
    Table 4_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx

    Table 4_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

    Published 2025
    “…Background<p>Hypertensive heart disease (HHD) poses a substantial global public health burden, particularly among older adults. …”
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  17. 17957
    Image 1_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.tif

    Image 1_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.tif by Ning An (618997)

    Published 2025
    “…Background<p>Hypertensive heart disease (HHD) poses a substantial global public health burden, particularly among older adults. …”
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  18. 17958
    Table 3_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx

    Table 3_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

    Published 2025
    “…Background<p>Hypertensive heart disease (HHD) poses a substantial global public health burden, particularly among older adults. …”
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  19. 17959
    Table 2_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx

    Table 2_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

    Published 2025
    “…Background<p>Hypertensive heart disease (HHD) poses a substantial global public health burden, particularly among older adults. …”
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  20. 17960
    Principal component analysis of soil parameters.

    Principal component analysis of soil parameters. by Wasihun Mengiste (22470304)

    Published 2025
    “…In terms of soil depth, the sand fraction, TP, Av. P, TN, CEC, OM, extractable micronutrients like Fe, Mn, Cu, and Zn decreased with increasing soil depth, while the clay content, BD, pH, and exchangeable bases like Ca, Mg, Na, and K were increased with increasing soil depth. …”
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