NLS-tagged RIG-I stimulates immune responses and decreased viral RNA levels in HCV-infected cells. by Christopher J. Neufeldt (478659)

Colistin-phage combinations decrease antibiotic resistance in <i>Acinetobacter baumannii</i> via changes in envelope architecture by Xiaoqing Wang (514179)

SDG decreases iron-induced oxidative stress in H9c2 cells. by Stephanie Puukila (568109)

VPA treated prostate cancer cells show decreased repair capacity and γ-H2AX clearance. by Sushant K. Kachhap (273170)

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. …”

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

Loss of epithelial markers is an early event in oral dysplasia and is observed within the safety margin of dysplastic and T1 OSCC biopsies by Zahra Abdalla (4669927)

“…<div><p>Oral squamous cell carcinoma (OSCC) is a highly aggressive cancer that is associated with poor 5-year patient survival. …”

T and B cells are markedly decreased in the VAT of CD11c-MyD88 KO DIO mice. by Manikandan Subramanian (789856)

<i>S.</i> Typhimurium-infected mice have tissue inflammation and thrombosis, increased hematopoiesis, and decreased splenic iron. by Diane E. Brown (160264)

“…(B) Mouse spleen, 3 weeks post-infection; extramedullary hematopoiesis (EMH; arrow, megakaryocytes), histiocytic infiltration (I) throughout the red pulp, and thrombus (T). H&E stain (A, B). (C) Spleen, mock-infected (left) and infected mouse (right), 3 weeks post-infection; markedly decreased ferric iron staining in red pulp. …”

Agonist-induced currents in both TRPV1- and TRPM8-transfected neurons are decreased after prolonged exposure. by Devon C. Crawford (256278)

“…Similar to (A), menthol was added to the bath recording solution. …”

KCl pulse decreased membrane, but increased nuclear, detection of NHE1 in b.End3 endothelial cells. by Erika Liktor-Busa (3328125)

Improvement of the eye morphology and the motor performance phenotypes of FRDA flies by decreased expression of genes implicated in iron homeostasis. by Sirena Soriano (348274)

Inhibition of PKC or Rho protein kinase decreases Ad5 wild type replication <i>in vitro</i>. by Alice Liu (4814415)

Primary cilia frequency is decreased on both CK5+ and CK5- cancer cells. by Nadia B. Hassounah (429199)

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

“…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)

“…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)

“…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)

“…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)

“…A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”

Liability threshold model for nsCL/P. by Laurence J. Howe (5579186)