Acanthamoeba castellanii trophozoites need oxygen for normal functioning and lipids are their preferred substrate, offering new possibilities for treatment.

Acanthamoebae, pathogenic free-living amoebae, can cause Granulomatous Amoebic Encephalitis (GAE) and keratitis, and for both types of infection, no adequate treatment options are available. As the metabolism of pathogens is an attractive treatment target, we set out to examine the energy metabolism of Acanthamoeba castellanii and studied the aerobic and anaerobic capacities of the trophozoites. Under anaerobic conditions, or in the presence of inhibitors of the electron-transport chain, A.

Early activation of hepatic stellate cells induces rapid initiation of retinyl ester breakdown while maintaining lecithin:retinol acyltransferase (LRAT) activity.

Lecithin:retinol acyltransferase (LRAT) is the main enzyme producing retinyl esters (REs) in quiescent hepatic stellate cells (HSCs). When cultured on stiff plastic culture plates, quiescent HSCs activate and lose their RE stores in a process similar to that in the liver following tissue damage, leading to fibrosis. Here we validated HSC cultures in soft gels to study RE metabolism in stable quiescent HSCs and investigated RE synthesis and breakdown in activating HSCs.

A Real-world Toxicity Atlas Shows that Adverse Events of Combination Therapies Commonly Result in Additive Interactions.

Purpose: Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting.

Experimental Design: We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid.

Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures.

Background: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays.

Screening of predicted synergistic multi-target therapies in glioblastoma identifies new treatment strategies.

Background: IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients.

Methods: We performed the largest drug combination screen to date in GBM, using a high-throughput effort where we selected 90 drug combinations for their activity onto 25 patient-derived GBM cultures.