Safety and efficacy of PfSPZ Vaccine against malaria in healthy adults and women anticipating pregnancy in Mali: two randomised, double-blind, placebo-controlled, phase 1 and 2 trials.

Background: Plasmodium falciparum parasitaemia during pregnancy causes maternal, fetal, and infant mortality. Poor pregnancy outcomes are related to blood-stage parasite sequestration and the ensuing inflammatory response in the placenta, which decreases over successive pregnancies. A radiation-attenuated, non-replicating, whole-organism vaccine based on P falciparum sporozoites (PfSPZ Vaccine) has shown efficacy at preventing infection in African adults.

Learning debiased graph representations from the OMOP common data model for synthetic data generation.

Background: Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or intervention. We propose a highly available method which enables synthetic data generation from real patient records in a privacy preserving and compliant fashion, is interpretable and allows for expert intervention.

Methods: Our approach ties together two established tools in medical informatics, namely OMOP as a data standard for electronic health records and Synthea as a data synthetization method.

Kismet/CHD7/CHD8 affects gut microbiota, mechanics, and the gut-brain axis in Drosophila melanogaster.

The gut microbiome affects brain and neuronal development and may contribute to the pathophysiology of neurodevelopmental disorders. However, it is unclear how risk genes associated with such disorders affect gut physiology in a manner that could impact microbial colonization and how the mechanical properties of the gut tissue might play a role in gut-brain bidirectional communication. To address this, we used Drosophila melanogaster with a null mutation in the gene kismet, an ortholog of chromodomain helicase DNA-binding protein (CHD) family members CHD7 and CHD8.

Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi-country corn kernels via NIR spectroscopy.

Differences in moisture and protein content impact both nutritional value and processing efficiency of corn kernels. Near-infrared (NIR) spectroscopy can be used to estimate kernel composition, but models trained on a few environments may underestimate error rates and bias. We assembled corn samples from diverse international environments and used NIR with chemometrics and partial least squares regression (PLSR) to determine moisture and protein.