Showing 19,821 - 19,840 results of 132,472 for search '(( a e decrease ) OR ( 5 ((fold decrease) OR (((mean decrease) OR (a decrease)))) ))', query time: 2.02s Refine Results
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    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  6. 19826

    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  7. 19827

    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  8. 19828

    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  9. 19829

    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
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    Onset-treatment with HBPDS decreases the mRNA expression of inflammatory mediators in the spinal cord from EAE mice. by Jong Hee Choi (804357)

    Published 2015
    “…The mRNA expression levels of TNF-a (A), IL-1b (B), IL–6 (C), iNOS (D), MCP–1 (E), MIP–1α (F), and RANTES (G) were significantly decreased in the spinal cord of EAE mice by onset-treatment with HBPDS (30% ethanol extracted). …”
  12. 19832

    Investigating the shared genetics of non-syndromic cleft lip/palate and facial morphology by Laurence J. Howe (5579186)

    Published 2018
    “…Where evidence was found of genetic overlap, we used bidirectional Mendelian randomization (MR) to test the hypothesis that genetic liability to nsCL/P is causally related to implicated facial phenotypes. Across 5,804 individuals of European ancestry from two studies, we found strong evidence, using PRS, of genetic overlap between nsCL/P and philtrum width; a 1 S.D. increase in nsCL/P PRS was associated with a 0.10 mm decrease in philtrum width (95% C.I. 0.054, 0.146; P = 2x10<sup>-5</sup>). …”
  13. 19833

    VEGF overexpression in OU culture increased OU size and altered stem/progenitor cell gene expression. by Christopher R. Schlieve (3179439)

    Published 2016
    “…(C) Significant increase in Bmi1 and Atoh1 expression and decrease in EphB2 expression was observed in doxycycline-treated VEGF OU compared to controls at 5 days (*p<0.05). …”
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    A photomicrograph of sections of the ovary in different groups [ki67 immunostaining x200]. by Rasha Atta (21608582)

    Published 2025
    “…<p>A) The control group shows ki67 immunoreaction in the granulosa (G) cells of different ovarian follicles, the unilaminar primary (UF), the multilaminar primary (MF), and the secondary (SF) follicle. …”
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