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19821
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19822
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19823
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19824
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19825
Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter
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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19826
Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter
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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19827
Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter
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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19828
Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter
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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19829
Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter
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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19830
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19831
Onset-treatment with HBPDS decreases the mRNA expression of inflammatory mediators in the spinal cord from EAE mice.
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). …”
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19832
Investigating the shared genetics of non-syndromic cleft lip/palate and facial morphology
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>). …”
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19833
VEGF overexpression in OU culture increased OU size and altered stem/progenitor cell gene expression.
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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19834
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19835
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19836
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19837
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19838
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19839
A photomicrograph of sections of the ovary in different groups [ki67 immunostaining x200].
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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19840