Showing 8,061 - 8,080 results of 18,297 for search 'significant ((((gap decrease) OR (((a decrease) OR (nn decrease))))) OR (mean decrease))', query time: 0.86s Refine Results
  1. 8061

    Data Sheet 1_Efficacy of cartilage-targeted IGF-1 in a mouse model of growth hormone insensitivity.docx by Krishma Tailor (13730582)

    Published 2025
    “…Here, we studied CV1574-1 in a second mouse model, C57BL/6 wild-type mice treated with pegvisomant to induce GH resistance. …”
  2. 8062
  3. 8063

    Effects of extracorporeal shock wave therapy on motor function in patients with cerebral palsy: a systematic review and meta-analysis by Hui-Hui Peng (21503736)

    Published 2025
    “…In addition, spasticity significantly decreases, particularly with focused ESWT or ESWT targeting the upper limbs.…”
  4. 8064
  5. 8065

    Table 1_Association between life’s crucial 9 and sarcopenia: estimated glucose disposal rate as a key mediator.doc by Xialian Tang (22319332)

    Published 2025
    “…In crude models, each 10-point increase in LC9 was associated with a 4.9% decrease in sarcopenia odds (OR: 0.951, p < 0.001). …”
  6. 8066

    Table 4_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

    Published 2025
    “…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”
  7. 8067

    Table 6_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

    Published 2025
    “…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”
  8. 8068

    Table 5_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

    Published 2025
    “…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”
  9. 8069

    Table 1_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

    Published 2025
    “…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”
  10. 8070

    Image 1_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.tif by Xinyi Song (2207233)

    Published 2025
    “…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”
  11. 8071

    Table 3_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

    Published 2025
    “…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”
  12. 8072

    Table 2_Impact of temperature trend-defined seasonality on psoriasis treatment outcomes: a multicenter longitudinal study.docx by Xinyi Song (2207233)

    Published 2025
    “…</p>Objective<p>To assess the impact of a novel temperature trend-defined seasonality on psoriasis treatment responses at 2 and 3 months.…”
  13. 8073
  14. 8074

    Parameters of VMR separated by light or dark periods run at 6 dpf. by Morgan Barnes (7876373)

    Published 2025
    “…(E-H) VMR parameters in dnVDRa induced zebrafish at 48 hpf. There is a significant decrease in distance moved in the dark (p < 0.01) and light (p < 0.05) periods, a significant decrease in velocity in the dark (p < 0.05) and light (p < 0.01) periods, a significant decrease in activity state in the dark (p < 0.05) and light (p < 0.01) periods and a significant increase in distance to point in the light period (p < 0.01) in the 48 + fish. …”
  15. 8075

    PCA-CGAN model parameter settings. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  16. 8076

    MIT-BIH dataset proportion analysis chart. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  17. 8077

    Wavelet transform preprocessing results. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  18. 8078

    PCAECG_GAN. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  19. 8079

    MIT dataset expansion quantities and Proportions. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”
  20. 8080

    Experimental hardware and software environment. by Chao Tang (10925)

    Published 2025
    “…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”