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Background: Macrophages are major effectors in regulating immune response and inflammation. The pro-inflammatory phenotype (M1) is induced by the activation of the Toll-like receptor 4 (TLR4) on the macrophage surface, which recognizes lipopolysaccharide (LPS), a component of Gram-negative bacterial wall, and by the binding of interferon-gamma (IFNγ), a cytokine released by activated T lymphocytes, to its receptor (IFNGR). Among the pathways activated by LPS/IFNγ is the Notch pathway, which promotes the M1 phenotype. Conversely, 17β-estradiol (E2) has been shown to blunt LPS-mediated inflammatory response. While it has been shown that E2 regulates the activity of the Notch1 receptor in human endothelial cells, there is no evidence of estrogen-mediated regulation of Notch1 in macrophages., Methods and Results: In this study, RAW 264.7 cells were stimulated with LPS/IFNγ in the presence or absence of E2 and/or N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT), an inhibitor of γ-secretase, the enzyme involved in Notch activation. The effects of treatment on inducible nitric oxide synthase (iNOS), on components of the Notch pathway, and MAPK (mitogen-activated protein kinase) were assessed by quantitative PCR and Western blotting. We found that E2, through a mechanism involving the inhibition of p38 phosphorylation, reduces the activation of Notch1 induced by LPS/IFNγ. On the contrary, Notch1 exerts a negative control on the estrogen receptor α (ERα) since Notch1 inhibition increases the protein levels of this receptor., Conclusion: In conclusion, we report for the first time a Notch-ERα interaction in macrophages. Our data suggest that E2 may reduce LPS/IFNγ-mediated M1 pro-inflammatory phenotype in macrophages by inhibiting Notch1. This finding encourages further studies on Notch1 inhibitors as novel treatments for inflammation-related diseases., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Calcification of the aortic valve (CAVDS) is a major cause of aortic stenosis (AS) leading to loss of valve function which requires the substitution by surgical aortic valve replacement (SAVR) or transcatheter aortic valve intervention (TAVI). These procedures are associated with high post-intervention mortality, then the corresponding risk assessment is relevant from a clinical standpoint. This study compares the traditional Cox Proportional Hazard (CPH) against Machine Learning (ML) based methods, such as Deep Learning Survival (DeepSurv) and Random Survival Forest (RSF), to identify variables able to estimate the risk of death one year after the intervention, in patients undergoing either to SAVR or TAVI. We found that with all three approaches the combination of six variables, named albumin, age, BMI, glucose, hypertension, and clonal hemopoiesis of indeterminate potential (CHIP), allows for predicting mortality with a c-index of approximately 80 % . Importantly, we found that the ML models have a better prediction capability, making them as effective for statistical analysis in medicine as most state-of-the-art approaches, with the additional advantage that they may expose non-linear relationships. This study aims to improve the early identification of patients at higher risk of death, who could then benefit from a more appropriate therapeutic intervention., (© 2024. The Author(s).)