Showing 1,281 - 1,300 results of 9,300 for search 'significantly ((((((lower decrease) OR (teer decrease))) OR (we decrease))) OR (linear decrease))', query time: 0.45s Refine Results
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    Primer sequences for RT-qPCR. by Wenlong Shen (9313937)

    Published 2025
    “…Data from TCGA showed that NCOA4 shows greater downgrade in tumor tissues than in non-tumor tissues and the overall survival (OS) of patients with low NCOA4 expression was significantly shorter than that of patients with high NCOA4 expression.The qPCR results showed that NCOA4 was expressed at low levels in cholangiocarcinoma tissue specimens; the mRNA expression of NCOA4 decreased after knocking down NCOA4 in cells. …”
  18. 1298

    siRNA sequences and negative controls sequences. by Wenlong Shen (9313937)

    Published 2025
    “…Data from TCGA showed that NCOA4 shows greater downgrade in tumor tissues than in non-tumor tissues and the overall survival (OS) of patients with low NCOA4 expression was significantly shorter than that of patients with high NCOA4 expression.The qPCR results showed that NCOA4 was expressed at low levels in cholangiocarcinoma tissue specimens; the mRNA expression of NCOA4 decreased after knocking down NCOA4 in cells. …”
  19. 1299

    Antibodies used in the study. by Wenlong Shen (9313937)

    Published 2025
    “…Data from TCGA showed that NCOA4 shows greater downgrade in tumor tissues than in non-tumor tissues and the overall survival (OS) of patients with low NCOA4 expression was significantly shorter than that of patients with high NCOA4 expression.The qPCR results showed that NCOA4 was expressed at low levels in cholangiocarcinoma tissue specimens; the mRNA expression of NCOA4 decreased after knocking down NCOA4 in cells. …”
  20. 1300

    Some examples of selected Chinese characters. by Weijia Zhu (65481)

    Published 2025
    “…Our model shows clear enhancements in structural accuracy (SSIM improved to 0.91), pixel-level fidelity (RMSE reduced to 2.68), perceptual quality aligned with human vision (LPIPS reduced to 0.07), and stylistic realism (FID decreased to 13.87). It reduces the model size to 100M parameters, cuts training time to just 1.3 hours, and lowers inference time to only 21 minutes. …”