Increasing Accessibility of Bayesian Network-Based Defined Approaches for Skin Sensitisation Potency Assessment.

Skin sensitisation is a critical adverse effect assessed to ensure the safety of compounds and materials exposed to the skin. Alongside the development of new approach methodologies (NAMs), defined approaches (DAs) have been established to promote skin sensitisation potency assessment by adopting and integrating standardised in vitro, in chemico, and in silico methods with specified data analysis procedures to achieve reliable and reproducible predictions. The incorporation of additional NAMs could help increase accessibility and flexibility.

In-depth analysis of human virus-specific CD8 T cells delineates unique phenotypic signatures for T cell specificity prediction.

Following viral infection, the human immune system generates CD8 T cell responses to virus antigens that differ in specificity, abundance, and phenotype. A characterization of virus-specific T cell responses allows one to assess infection history and to understand its contribution to protective immunity. Here, we perform in-depth profiling of CD8 T cells binding to CMV-, EBV-, influenza-, and SARS-CoV-2-derived antigens in peripheral blood samples from 114 healthy donors and 55 cancer patients using high-dimensional mass cytometry and single-cell RNA sequencing.

Evaluation of new alternative methods for the identification of estrogenic, androgenic and steroidogenic effects: a comparative in vitro/in silico study.

A suite of in vitro assays and in silico models were evaluated to identify which best detected the endocrine-disrupting (ED) potential of 10 test chemicals according to their estrogenic, androgenic and steroidogenic (EAS) potential compared to the outcomes from ToxCast. In vitro methods included receptor-binding, CALUX transactivation, H295R steroidogenesis, aromatase activity inhibition and the Yeast oestrogen (YES) and Yeast androgen screen (YAS) assays. The impact of metabolism was also evaluated.

Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers.

The rapid growth of high-throughput technologies has transformed biomedical research. With the increasing amount and complexity of data, scalability and reproducibility have become essential not just for experiments, but also for computational analysis. However, transforming data into information involves running a large number of tools, optimizing parameters, and integrating dynamically changing reference data.

Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors.

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays.