Intracellular images (×20000) of <i>S</i>. <i>cerevisiae</i> strain BH8 before (a) and after (b, c) culturing in MSM with 1.50 mM Cu²⁺ for 48 h; CW: cell wall; N: cell n... by Xiang-yu Sun (748032)

Interpretation of bidirectional MR. by Laurence J. Howe (5579186)

Summary of covariates used to model^** incidence of cardiovascular diseases during 5 years of follow-up in the type 2 diabetes patients in diabetes clinic (2017-2022). by Khadije Maajani (10125692)

An illustration of the subnetwork containing ADRA2A and TBXA2R. by Noël Malod-Dognin (8677110)

Clinical Pharmacogenetics Implementation Consortium list of clinically actionable drugs with a pharmacogenomic indication and their associated genes. by Emma F. Magavern (20947928)

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

“…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%. …”

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

“…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%. …”

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

“…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%. …”

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

“…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%. …”

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

“…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%. …”

mGluR5 biases different intracellular pathways in iSPNs and dSPNs to regulate DLS t-LTD. by Vincent Paget-Blanc (21807763)

Descriptive statistics and bivariate correlations. by Betty Henderson-Matthews (21539999)

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

“…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^-5). …”

Marker expression and fluorescence intensity in oral epithelium biopsies. by Zahra Abdalla (4669927)

Summary of hierarchical linear regression model with spirituality predicting symptoms of anxiety. by Betty Henderson-Matthews (21539999)

Summary of hierarchical linear regression model with spirituality predicting loneliness. by Betty Henderson-Matthews (21539999)

Summary of hierarchical linear regression model with spirituality predicting symptoms of depression. by Betty Henderson-Matthews (21539999)

Mean probability of reporting a specific health status in non-hypertensive and hypertensive patients, with and without related medication, while excluding the effects of other fact... by Jesús Martín-Fernández (406234)

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

“…(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). …”

(a) Schematic of the sample preparation, (b) developed GFRPs samples modified, (c) samples for impact strength tests, (d) samples for compressive strength tests. by Shaban Gul (14309526)