The mass fragmentation spectrum for the F₅ sub-fraction obtained via reversed-phase high-performance liquid chromatography (see Fig 1): (a) DY-8, and (b) IK-6. by Kitjasit Promsut (18823872)

Comparison between fitted model (solid lines) and data (markers) at the whole lung scale (a) and in three example grids from row 11, columns 3, 5, and 8, which are at different ele... by Monica E. Shapiro (22622240)

Effect of parasitism and PDV injection on lipid droplet size in the fat body of FAW larvae. by Xian Li (201841)

Characterisation of the antibodies used in this study. by Zahra Abdalla (4669927)

SclB regulates the oxidative stress response in <i>A</i>. <i>nidulans</i> in the presence of H₂O₂. by Karl G. Thieme (5558369)

“…<p>A) Conidiospores of Δ<i>sclB</i>, Δ<i>vosA</i> and Δvos<i>A</i>Δ<i>sclB</i> strains show decreased survival in the presence of H₂O₂ compared to spores of wildtype (WT), <i>sclB</i> comp and <i>sclB</i> OE strains. …”

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)

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%. …”