MuscleJ2: a rebuilding of MuscleJ with new features for high-content analysis of skeletal muscle immunofluorescence slides.

Histological analysis of skeletal muscle is of major interest for understanding its behavior in different pathophysiological conditions, such as the response to different environments or myopathies. In this context, many software programs have been developed to perform automated high-content analysis. We created MuscleJ, a macro that runs in ImageJ/Fiji on batches of images.

Deep learning in image-based phenotypic drug discovery.

Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation.

Distinct single-component adjuvants steer human DC-mediated T-cell polarization via Toll-like receptor signaling toward a potent antiviral immune response.

The COVID-19 pandemic highlights the importance of efficient and safe vaccine development. Vaccine adjuvants are essential to boost and tailor the immune response to the corresponding pathogen. To allow for an educated selection, we assessed the effect of different adjuvants on human monocyte-derived dendritic cells (DCs) and their ability to polarize innate and adaptive immune responses.

Marine dissolved organic matter: a vast and unexplored molecular space.

Marine dissolved organic matter (DOM) comprises a vast and unexplored molecular space. Most of it resided in the oceans for thousands of years. It is among the most diverse molecular mixtures known, consisting of millions of individual compounds.