Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning.

Background: Machine learning (ML) is a valuable tool with the potential to aid clinical decision making. Adoption of ML to this end requires data that reliably correlates with the clinical outcome of interest; the advantage of ML is that it can model these correlations from complex multiparameter data sets that can be difficult to interpret conventionally. While currently available clinical data can be used in ML for this purpose, there exists the potential to discover new "biomarkers" that will enhance the effectiveness of ML in clinical decision making.

Irradiation immunity interactions.

The immune system can influence cancer development by both impeding and/or facilitating tumour growth and spread. A better understanding of this complex relationship is fundamental to optimise current and future cancer therapeutic strategies. Although typically regarded as a localised and immunosuppressive anti-cancer treatment modality, radiation therapy has been associated with generating profound systemic effects beyond the intended target volume.

Machine learning predicts cancer subtypes and progression from blood immune signatures.

Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapies. Better understanding of an individual's immune landscape may play an important role in treatment personalisation.

Ghrelin restoration of function in vitro in somatotropes from male mice lacking the Janus kinase (JAK)-binding site of the leptin receptor.

Deletion of the signaling domain of leptin receptors selectively in somatotropes, with Cre-loxP technology, reduced the percentage of immunolabeled GH cells and serum GH. We hypothesized that the deficit occurred when leptin's postnatal surge failed to stimulate an expansion in the cell population. To learn more about the deficiency in GH cells, we tested their expression of GHRH receptors and GH mRNA and the restorative potential of secretagogue stimulation in vitro.