Characterizing Sensitivity to Vincristine, Irinotecan, and Telomerase-targeted Therapy in Diffuse Anaplastic Wilms Tumor Patient-derived Xenografts.

Background: Patients with diffuse anaplastic Wilms tumor (DAWT) experience relatively poor oncologic outcomes. Previous work has described mechanisms of telomerase upregulation in DAWT, posing a potential therapeutic target.

Methods: We assessed in vitro sensitivity to vincristine, irinotecan, and telomerase-targeting drug 6-thio-2'-deoxyguanosine (6 dG) in DAWT cell lines WiT49 and PDM115 and in spheroids derived from cell lines and four DAWT patient-derived xenografts (PDX).

PhiSiCal-Checkup: A Bayesian framework to validate amino acid conformations within experimental protein structures.

As structural biology and drug discovery depend on high-quality protein structures, assessment tools are essential. We describe a new method for validating amino-acid conformations: "PhiSiCal ([Formula: see text]al) Checkup." Twenty new joint probability distributions in the form of statistical mixture models explain the empirical distributions of dihedral angles [Formula: see text] of canonical amino acids in experimental protein structures.

How Should Meaningful Evidence Be Generated From Datasets?

Datasets are often considered "ideal" when they are large and contain longitudinal and representative data. But even research that uses ideal datasets might not generate high-quality evidence. This article emphasizes the roles that transparency plays in enhancing observational epidemiological findings' credibility and relevance and argues that epidemiological research can produce high-quality evidence even when datasets are not ideal.

Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators.

In silico trials for drug safety assessment require many high-fidelity 3D cardiac simulations to predict drug-induced QT interval prolongation, which is often computationally prohibitive. To streamline this process, we developed sex-specific emulators for a fast prediction of QT interval, trained on a dataset of 900 simulations. Our results show significant differences between 3D and 0D single-cell models as risk levels increase, underscoring the ability of 3D modeling to capture more complex cardiac responses.