Chemical characterization of the globular cluster NGC 5634 associated to the Sagittarius dwarf spheroidal galaxy

As part of our on-going project on the homogeneous chemical characterization of multiple stellar populations in globular clusters (GCs), we studied NGC 5634, associated to the Sagittarius dwarf spheroidal galaxy, using high-resolution spectroscopy of red giant stars collected with FLAMES@VLT. We present here the radial velocity distribution of the 45 observed stars, 43 of which are member, the detailed chemical abundance of 22 species for the seven stars observed with UVES-FLAMES, and the abundance of six elements for stars observed with GIRAFFE. On our homogeneous UVES metallicity scale we derived a low metallicity [Fe/H]=-1.867 +/-0.019 +/-0.065 dex (+/-statistical +/-systematic error) with sigma=0.050 dex (7 stars). We found the normal anti-correlations between light elements (Na and O, Mg and Al), signature of multiple populations typical of massive and old GCs. We confirm the associations of NGC 5634 to the Sgr dSph, from which the cluster was lost a few Gyr ago, on the basis of its velocity and position and the abundance ratios of alpha and neutron capture elements.Comment: 16 pages, 10 figures, 11 tables; accepted for publication on Astronomy and Astrophysic

Resveratrol Requires Red Wine Polyphenols for Optimum Antioxidant Activity

Objective: There is substantial evidence that a diet rich in fruit and vegetables may reduce the risk of aging and stress oxidative associated diseases. It has been suggested that benefits associated with fruit and red wine consumption could be due to pooled antioxidant microcomponents in diet. The aim of this study was to investigate the antioxidant activities of pure resveratrol (a well known phytoalexin, RSV) and red wine polyphenols (RWP), using UV-B radiated isolated rat hepatocytes as a model of oxidative stress. Methods: Rat hepatocytes were isolated by the collagenase method. The cells were loaded with resveratrol and/or polyphenols at different concentrations. The production of thiobarbituric acid reactive substances (TBARS) released by UV-B radiated cells and the levels of lipid-soluble antioxidants (Dolichol, Vitamin E, Coenzyme Q9 and Q10) were measured. Results: Resveratrol had pro-oxidant or antioxidant effects depending on (lower or higher) dosage. RWP protection from photolipoperoxidation was dose-dependent and increased with dosage. Combination of the two compounds exhibited synergistic antioxidant effect, and made resveratrol effective both at lower and higher dosages. Conclusions: These results suggest that resveratrol requires red wine polyphenols for optimum antioxidant activity

MiRNAs as Potential Prognostic Biomarkers for Metastasis in Thin and Thick Primary Cutaneous Melanomas.

Background/Aim: The identification of novel prognostic biomarkers for melanoma metastasis is essential to improve patient outcomes. To this aim, we characterized miRNA expression profiles in relation to metastasis in melanoma and correlated miRNAs expression with clinicalpathological factors. Materials and Methods: MiR-145-5p, miR-150-5p, miR-182-5p, miR-203-3p, miR-205-5p and miR211-5p expression levels were analyzed in primary cutaneous melanomas, including thin and thick melanomas, and in melanoma metastases by quantitative Real-Time PCR. Results: A significantly lower miR-205-5p expression was found in metastases compared to primary melanomas. Furthermore, a progressive down-regulation of miR-205-5p expression was observed from loco-regional to distant metastasis. Significantly lower miR-145-5p and miR-203-3p expression levels were found in cases with Breslow thickness >1 mm, high Clark level, ulceration and mitotic rate ≥1/mm2. Conclusion: Our findings point to miR-205-5p as potential biomarker of distant metastases and to miR-145-5p and miR-203-3p as markers of aggressiveness in melanoma

Exploring Machine Learning for Untargeted Metabolomics Using Molecular Fingerprints

Background Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. Methods This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups. Results The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study. Conclusion In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models

Insights into Mechanical Behavior and Biological Properties of Chia Seed Mucilage Hydrogels

In this contribution we report insights on the rheological properties of chia (Salvia hispanica) seed mucilage hydrogels. Specifically, we studied the influence of temperature and polymer concentration on the viscoelastic properties of resulting networks. Creep experiments performed in steady-state conditions allowed calculating Newtonian viscosities for chia hydrogels at different polymer concentrations, pointing at inter-chain interactions as the main responsible for the different behavior toward network slipping under constant stress. The combination of oscillatory frequency and stress sweep tests highlighted a moderate effect of temperature in influencing hydrogel mechanics. The latter results prompted us to investigate potential biological functions for this set of biomaterials. Lactate Dehydrogenase assay proved lack of cytotoxicity of chia suspensions toward Human Mesenchymal Stem Cells from adipose tissue here used as cell model. Differentiation experiments were finally undertaken to verify the influence of chia samples on osteo-induction triggered by chemical differentiation factors. Alkaline Phosphatase enzyme activity assay and Alizarin red staining demonstrated that chia mucilage did not alter in vitro stem cell differentiation. Collectively, this set of experiments revealed an almost inert role associated with chia suspensions, indicating a possible application of chia-based networks as scaffold models to study osteogenesis in vitro

Dual-energy CT kidney stone characterization-can diagnostic accuracy be achieved at low radiation dose?

OBJECTIVES To assess the accuracy of low-dose dual-energy computed tomography (DECT) to differentiate uric acid from non-uric acid kidney stones in two generations of dual-source DECT with stone composition analysis as the reference standard. METHODS Patients who received a low-dose unenhanced DECT for the detection or follow-up of urolithiasis and stone extraction with stone composition analysis between January 2020 and January 2022 were retrospectively included. Collected stones were characterized using X-ray diffraction. Size, volume, CT attenuation, and stone characterization were assessed using DECT post-processing software. Characterization as uric acid or non-uric acid stones was compared to stone composition analysis as the reference standard. Sensitivity, specificity, and accuracy of stone classification were computed. Dose length product (DLP) and effective dose served as radiation dose estimates. RESULTS A total of 227 stones in 203 patients were analyzed. Stone composition analysis identified 15 uric acid and 212 non-uric acid stones. Mean size and volume were 4.7 mm × 2.8 mm and 114 mm$^{3}$, respectively. CT attenuation of uric acid stones was significantly lower as compared to non-uric acid stones (p < 0.001). Two hundred twenty-five of 227 kidney stones were correctly classified by DECT. Pooled sensitivity, specificity, and accuracy were 1.0 (95%CI: 0.97, 1.00), 0.93 (95%CI: 0.68, 1.00), and 0.99 (95%CI: 0.97, 1.00), respectively. Eighty-two of 84 stones with a diameter of  ≤ 3 mm were correctly classified. Mean DLP was 162 ± 57 mGy*cm and effective dose was 2.43 ± 0.86 mSv. CONCLUSIONS Low-dose dual-source DECT demonstrated high accuracy to discriminate uric acid from non-uric acid stones even at small stone sizes. KEY POINTS • Two hundred twenty-five of 227 stones were correctly classified as uric acid vs. non-uric acid stones by low-dose dual-energy CT with stone composition analysis as the reference standard. • Pooled sensitivity, specificity, and accuracy for stone characterization were 1.0, 0.93, and 0.99, respectively. • Low-dose dual-energy CT for stone characterization was feasible in the majority of small stones  < 3 mm

Predicting the effect of the Common Agricultural Policy post-2020 using an agent-based model based on PMP methodology.

The objective of this study is to perform an ex-ante assessment of the potential impacts of agro-environmental measures included in the post-2020 Common Agricultural Policy (CAP), by estimating farmers' responsiveness in adopting organic agricultural practices and an eco-scheme that incentivises extensive forage systems. This research is conducted by mean of an Agent-Based Model (ABM), based on Positive Mathematical Programming (PMP), implemented in GAMS. The ABM facilitate the simulation of interaction among farmers, allowing for an analysis of farm heterogeneity. The PMP methodology add a non-rational dimention to the farmers’ economic drivers. The model is calibrated using 2019 Farm Accountancy Data Network (FADN) data specific to the Emilia Romagna region in Italy. Our findings reveal significant impacts on land use, with a notable decrease in cereal cultivation in favour of protein and fodder crops. Moreover, structural shifts are observed, notably a decrease in the number of small-scale farms. We also assess environmental and economic implications, observing a modest reduction in CO2 equivalent emissions per hectare, an increase in water demand, and an overall economic stability among farms, as indicated by changes in gross margin per hectare

Too much tolerance for hyperoxemia in mechanically ventilated patients with SARS-CoV-2 pneumonia? Report from an Italian intensive care unit

Background: In COVID-19 patients requiring mechanical ventilation, the administration of high oxygen (O2) doses for prolonged time periods may be necessary. Although life-saving in most cases, O2 may exert deleterious effects if administered in excessive concentrations. We aimed to describe the prevalence of hyperoxemia and excessive O2 administration in mechanically ventilated patients with SARS-CoV-2 pneumonia and determine whether hyperoxemia is associated with mortality in the Intensive Care Unit (ICU) or the onset of ventilator-associated pneumonia (VAP). Materials and methods: Retrospective single-center study on adult patients with SARS-CoV-2 pneumonia requiring invasive mechanical ventilation for ≥48 h. Patients undergoing extracorporeal respiratory support were excluded. We calculated the excess O2 administered based on the ideal arterial O2 tension (PaO2) target of 55–80 mmHg. We defined hyperoxemia as PaO2 > 100 mmHg and hyperoxia + hyperoxemia as an inspired O2 fraction (FiO2) > 60% + PaO2 > 100 mmHg. Risk factors for ICU-mortality and VAP were assessed through multivariate analyses. Results: One hundred thirty-four patients were included. For each day of mechanical ventilation, each patient received a median excess O2 of 1,121 [829–1,449] L. Hyperoxemia was found in 38 [27–55]% of arterial blood gases, hyperoxia + hyperoxemia in 11 [5–18]% of cases. The FiO2 was not reduced in 69 [62–76]% of cases of hyperoxemia. Adjustments were made more frequently with higher PaO2 or initial FiO2 levels. ICU-mortality was 32%. VAP was diagnosed in 48.5% of patients. Hyperoxemia (OR 1.300 95% CI [1.097–1.542]), time of exposure to hyperoxemia (OR 2.758 [1.406–5.411]), hyperoxia + hyperoxemia (OR 1.144 [1.008–1.298]), and daily excess O2 (OR 1.003 [1.001–1.005]) were associated with higher risk for ICU-mortality, independently of age, Sequential Organ failure Assessment score at ICU-admission and mean PaO2/FiO2. Hyperoxemia (OR 1.033 [1.006–1.061]), time of exposure to hyperoxemia (OR 1.108 [1.018–1.206]), hyperoxia + hyperoxemia (OR 1.038 [1.003–1.075]), and daily excess O2 (OR 1.001 [1.000–1.001]) were identified as risk factors for VAP, independently of body mass index, blood transfusions, days of neuromuscular blocking agents (before VAP), prolonged prone positioning and mean PaO2/FiO2 before VAP. Conclusion: Excess O2 administration and hyperoxemia were common in mechanically ventilated patients with SARS-CoV-2 pneumonia. The exposure to hyperoxemia may be associated with ICU-mortality and greater risk for VAP