NbVO₅ Mesoporous Thin Films by Evaporation Induced Micelles Packing: Pore Size Dependence of the Mechanical Stability upon Thermal Treatment and Li Insertion/Extraction by Natacha Krins (2066203)

“…In order to investigate the potentialities and limits of the soft-templating approach in the case of complex transition metal oxide networks, we deliberately selected a “difficult” compound: NbVO₅ was chosen because it combines a challenging synthesis with reported severe structural distortions during the first lithium insertion in the bulk material. …”

VEGF and sFlt-1 mutant phenotypes. VEGF mutant overexpression of VEGF by RT-PCR and ELISA. sFlt-1 mutant expression of sFlt-1 by RT-PCR. by Christopher R. Schlieve (3179439)

Marker expression in the surgical safety margin of LGD, HGD and T1 OSCC. by Zahra Abdalla (4669927)

CircMED12L Protects Against Hydrogen Peroxide-induced Apoptotic and Oxidative Injury in Human Lens Epithelial Cells by miR-34a-5p/ALCAM axis by Baohua Wu (2124334)

“…MiR-34a-5p was increased, while ALCAM was decreased in ARC patients and H₂O₂-induced HLECs. …”

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)

Effect of <i>CrBV3-31</i> interference on lipid metabolism in FAW larvae. by Xian Li (201841)

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

Figure 5 by Victor M. Rodriguez-Molina (379001)

“…For EPSCs with slow dynamics (τ_rise/τ_decay 5/50 and 5/200 ms), STP was reduced in the pre-noise condition by 82.5±4.3% and by 182.7±8.5% in the noise condition. …”

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)

VEGF and sFlt-1 mutants exhibit changes in stem cell marker gene expression by RT-qPCR. by Christopher R. Schlieve (3179439)

“…<p>(A) VEGF mutants demonstrated 0.55-fold reduction in Lgr5 expression compared to littermates (*p = 0.04). …”

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)

Effect of <i>CrBV3-31</i> interference on the performance of <i>Cotesia ruficrus</i> offspring. by Xian Li (201841)

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