17β-estradiol effects on human coronaries and grafts employed in myocardial revascularization: a preliminary study

Background: This study was undertaken to compare the in vitro effects of 17β-estradiol on human epicardial coronary arteries, resistance coronary arteries and on arterial vessels usually employed as grafts in surgical myocardial revascularization. Methods: Coronary artery rings (descending coronary artery, right coronary artery, circumflex coronary artery, first septal branch) and arterial graft rings (internal thoracic artery, gastro-epiploic artery) obtained from human heart donors with heart not suitable to cardiac transplantation were connected to force transducer for isometric force recording. Precontracted specimens with and without endothelium were exposed to increasing concentration of 17β-estradiol (3–30–300–3000 nmol/l) and to vehicle (0.1% v/v ethanol). We also evaluated the effects of 17β-estradiol on vessels before and 20 minutes after exposure to L-monomethyl-arginine and indomethacin. Results: 17β-estradiol induced a significant relaxation in all precontracted vessels (mean maximum effect: 78,6% ± 8,5). This effect was not different among the different rings and was not related to the presence of endothelium. N-monomethyl-L-arginine and indomethacin did not modify 17β-estradiol relaxant effect. Conclusion: The vasodilator action of the 17β-estradiol is similar on coronary arteries, resistance coronary arteries and arterial vessels usually employed as grafts in myocardial revascularization

Project CAPTIVE e-manual - suggestions for an ‘ideal’ multicultural system to support migrant women victims-survivors of S-GBV

UNHCR data1 shows that we are currently witnessing the highest levels of displacement on record, with 68.5 million forcibly displaced people worldwide and 44,400 people forced to flee their homes each day because of conflict and persecution. Many of these people are internally displaced and are living in IDP camps in their countries of origin, others have travelled to neighbouring countries, and others still have journeyed to Europe. Among them is a rising number of women and girls, who are not only exposed to various forms of sexual and gender-based violence in their homelands, but also along the way and upon arrival in Europe. Their experiences of violence differ in many ways from those of local women; accordingly, the support offered by services in the host country should be tailored to the specific needs of this target group.Project reference CAPTIVE/Just/2015/ RDAP/AG/VICT/9243 C.A.P.T.I.V.E. Cultural Agent Promoting & Targeting Interventions vs Violence & Enslavement JUSTICE Programme – RIGHTS, EQUALITY and CITIZENSHIP – DAPHNE Strandpeer-reviewe

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 1018.5, eV and 1020 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration