Neuraminidase-mediated enhancement of colonization is associated with altered mucus characteristics and distribution.

Unlabelled: Upon entry into the upper respiratory tract (URT), (Spn) upregulates neuraminidases (NA) that cleave sialic acid (SA) from host glycans. Because sialylation is thought to contribute to the physical properties that determine mucus function, we posited that Spn directly alters host mucus through NA activity. By directly imaging the colonized URT, we demonstrated NA-mediated alterations to the characteristics and distribution of mucus along the respiratory epithelium, where colonizing bacteria are found.

Paracrine regulations of IFN-γ secreting CD4 T cells by lumican and biglycan are protective in allergic contact dermatitis.

The extracellular matrix (ECM) is known to regulate innate immune cells but its role in T cell functions is poorly understood. Here, we show a protective role for ECM proteoglycans, lumican and biglycan in hapten-induced contact dermatitis that is achieved through limiting proinflammatory CD4 T cells. Lumican and biglycan-null mice develop significant inflammation with greater numbers of CD4 T cells in hapten-challenged ear pinnae, while their draining lymph nodes show increased T-bet-STAT1 signaling, Th1 commitment, and IFN-γ secreting CD4 T cell proliferation.

AI-based antibody discovery platform identifies novel, diverse, and pharmacologically active therapeutic antibodies against multiple SARS-CoV-2 strains.

Background: We are entering a new era of antibody discovery and optimization where machine learning (ML) processes will become indispensable for the design and development of therapeutics.

Methods: We have constructed a Humanoid Antibody Library for the discovery of therapeutics that is an initial step towards leveraging the utility of artificial intelligence and ML. We describe how we began our validation of the library for antibody discovery by isolating antibodies against a target of pandemic concern, SARS-CoV-2.

Characterization of tumor heterogeneity through segmentation-free representation learning.

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images.

MetFinder: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models.

As efforts to study the mechanisms of melanoma metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate the effects on tumor burden resulting from specific interventions. We show that automated quantification of tumor content from whole slide images is a compelling solution to assess in vivo experiments. In order to increase the outflow of data collection from preclinical studies, we assembled a large dataset with annotations and trained a deep neural network for the quantitative analysis of melanoma tumor content on histopathological sections of murine models.