Discovery of potent Plasmodium falciparum protein kinase 6 (PfPK6) inhibitors with a type II inhibitor pharmacophore.

Malaria is a devastating disease that causes significant global morbidity and mortality. The rise of drug resistance against artemisinin-based combination therapy demonstrates the necessity to develop alternative antimalarials with novel mechanisms of action. We report the discovery of Ki8751 as an inhibitor of essential kinase PfPK6.

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.

To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters.

Identification of Novel 2,4,5-Trisubstituted Pyrimidines as Potent Dual Inhibitors of Plasmodial GSK3/PK6 with Activity against Blood Stage Parasites In Vitro.

Essential plasmodial kinases GSK3 and PK6 are considered novel drug targets to combat rising resistance to traditional antimalarial therapy. Herein, we report the discovery of as a dual GSK3/PK6 inhibitor active against blood stage 3D7 parasites. To establish structure-activity relationships for PK6 and GSK3, 52 analogues were synthesized and assessed for the inhibition of GSK3 and PK6, with potent inhibitors further assessed for activity against blood and liver stage parasites.

Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images.

Background: Tumor programmed death-ligand 1 (PD-L1) status is useful in determining which patients may benefit from programmed death-1 (PD-1)/PD-L1 inhibitors. However, little is known about the association between PD-L1 status and tumor histopathological patterns. Using deep learning, we predicted PD-L1 status from hematoxylin and eosin (H and E) whole-slide images (WSIs) of nonsmall cell lung cancer (NSCLC) tumor samples.