Cascaded 3D UNet architecture for segmenting the COVID-19 infection from lung CT volume.

World Health Organization (WHO) declared COVID-19 (COronaVIrus Disease 2019) as pandemic on March 11, 2020. Ever since then, the virus is undergoing different mutations, with a high rate of dissemination. The diagnosis and prognosis of COVID-19 are critical in bringing the situation under control.

An insight into the dual fluorescence of 3,6-dihydroxybenzene-1,2,4,5-tetracarboxylic acid tetraethyl ester - An experimental and theoretical study.

The Excited State Intramolecular Proton Transfer (ESIPT) phenomenon involving photo-induced keto-enol tautomerization is known to cause significant variations in the excited state structures and photophysical properties of certain molecules. Here, the dual emission exhibited by 3,6-dihydroxybenzene-1,2,4,5-tetracarboxylic acid tetraethyl ester has been studied both experimentally and theoretically and it is concluded that the second emission is due to ESIPT in polar protic solvents, while it is due to dianion formation in solvents like DMSO and DMF.

Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction.

The inhibition of abnormal amyloid β (Aβ) aggregation has been regarded as a good target to control Alzheimer's disease. The present study adopted 2D-QSAR, HQSAR and 3D QSAR (CoMFA & CoMSIA) modeling approaches to identify the structural and physicochemical requirements for the potential Aβ aggregation inhibition. A structure-based molecular docking technique is utilized to approve the features that are obtained from the ligand-based techniques on 30 curcumin derivatives.

Exploration of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one derivatives as JAK inhibitors using various in silico techniques.

This study focuses on understanding the structural features of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one (dpp) derivatives to computationally identify new JAK inhibiting compounds. For the purpose, a novel virtual screening strategy, with 2D and 3D-QSAR (CoMFA and CoMSIA), data mining, pharmacophore modeling, ADMET prediction, multi-targeted protein-based docking and inverse QSAR, was employed. The 2D-QSAR equations developed for the JAK3, JAK2 and JAK1 involved five physicochemical descriptors.