Valorization of Winery By-Products as Bio-Fillers for Biopolymer-Based Composites.

Grape seeds (GS), wine lees (WL), and grape pomace (GP) are common winery by-products, used as bio-fillers in this research with two distinct biopolymer matrices-poly(butylene adipate-co-terephthalate) (PBAT) and polybutylene succinate (PBS)-to create fully bio-based composite materials. Each composite included at least 30 v% bio-filler, with a sample reaching 40 v%, as we sought to determine a composition that could be economically and environmentally effective as a substitute for a pure biopolymer matrix. The compounding process employed a twin-screw extruder followed by an injection molding procedure to fabricate the specimens. An acetylation treatment assessed the specimen's efficacy in enhancing matrix-bio-filler affinity, particularly for WL and GS. The fabricated bio-composites underwent an accurate characterization, revealing no alteration in thermal properties after compounding with bio-fillers. Moreover, hygroscopic measurements indicated increased water-affinity in bio-composites compared to neat biopolymer, most significantly with GP, which exhibited a 7-fold increase. Both tensile and dynamic mechanical tests demonstrated that bio-fillers not only preserved, but significantly enhanced, the stiffness of the neat biopolymer across all samples. In this regard, the most promising results were achieved with the PBAT and acetylated GS sample, showing a 162% relative increase in Young's modulus, and the PBS and WL sample, which exhibited the highest absolute values of Young's modulus and storage modulus, even at high temperatures. These findings underscore the scientific importance of exploring the interaction between bio-fillers derived from winery by-products and three different biopolymer matrices, showcasing their potential for sustainable material development, and advancing polymer science and bio-sourced material processing. From a practical standpoint, the study highlighted the tangible benefits of using by-product bio-fillers, including cost savings, waste reduction, and environmental advantages, thus paving the way for greener and more economically viable material production practices.

Fibroblast Growth Factor 21 in Chronic Hepatitis C: A Potential Non-Invasive Biomarker of Liver Status upon Viral Eradication.

Fibroblast growth factor 21 (FGF-21), previously recognized as a marker of liver damage and a potential drug target in non-alcoholic fatty liver disease (NAFLD), has unclear implications in hepatitis C virus (HCV) infections. This study aimed to investigate the relationship between FGF-21 levels and liver health in patients with HCV undergoing direct-acting antiviral (DAA) treatment. Forty-five patients were assessed for liver stiffness, blood chemistry, and other relevant metrics before and after achieving sustained viral response (SVR), defined as the absence of detectable HCV-RNA after 24 weeks of treatment. Post-treatment, all patients showed a decrease in liver stiffness and improved liver enzyme levels (AST and ALT), alongside an increase in FGF-21 levels. Interestingly, the increase in FGF-21 correlated negatively with liver stiffness but showed no correlation with hepatic steatosis. The observed elevation in FGF-21 levels at SVR following DAA therapy for chronic HCV infection can be attributed to the restoration of hepatic function, including its synthetic capabilities. Specifically, the mitigation of liver fibrosis post-HCV eradication is expected to lead to improvements in liver function, such as enhanced albumin and FGF-21 production. This improvement in synthetic function likely drives the increase in FGF-21 levels, rather than changes in liver fat content. We suggest a potential role of FGF-21 as a marker of fibrosis and hepatic cytotoxicity and as a drug target beyond NAFLD, to be confirmed by additional studies.

Rapid Weight Loss, Central Obesity Improvement and Blood Glucose Reduction Are Associated with a Stronger Adaptive Immune Response Following COVID-19 mRNA Vaccine.

Obesity is associated with a poor COVID-19 prognosis, and it seems associated with reduced humoral response to vaccination. Public health campaigns have advocated for weight loss in subjects with obesity, hoping to eliminate this risk. However, no evidence proves that weight loss leads to a better prognosis or a stronger immune response to vaccination. We aimed to investigate the impact of rapid weight loss on the adaptive immune response in subjects with morbid obesity. Twenty-one patients followed a hypocaloric, very-low-carbohydrate diet one week before to one week after the two mRNA vaccine doses. The diet's safety and efficacy were assessed, and the adaptive humoral (anti-SARS CoV-2 S antibodies, Abs) and cell-mediated responses (IFNγ secretion on stimulation with two different SARS CoV-2 peptide mixes, IFNγ-1 and IFNγ-2) were evaluated. The patients lost ~10% of their body weight with metabolic improvement. A high baseline BMI correlated with a poor immune response (R -0.558, p = 0.013 for IFNγ-1; R -0.581, p = 0.009 for IFNγ-2; R -0.512, p = 0.018 for Abs). Furthermore, there was a correlation between weight loss and higher IFNγ-2 (R 0.471, p = 0.042), and between blood glucose reduction and higher IFNγ-1 (R 0.534, p = 0.019), maintained after weight loss and waist circumference reduction adjustment. Urate reduction correlated with higher Abs (R 0.552, p = 0.033). In conclusion, obesity is associated with a reduced adaptive response to a COVID-19 mRNA vaccine, and weight loss and metabolic improvement may reverse the effect.

Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome.

The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management of this urgent public health issue. This aim could be accomplished by exploiting the potential of machine learning (ML) technology. In a single-centre study (n = 2567), we used an ML analysis to cluster patients with metabolically healthy (MHO) or metabolically unhealthy (MUO) obesity, based on several clinical and biochemical variables. The first model provided by ML was able to predict the presence/absence of MHO with an accuracy of 66.67% and 72.15%, respectively, and included the following parameters: HOMA-IR, upper body fat/lower body fat, glycosylated haemoglobin, red blood cells, age, alanine aminotransferase, uric acid, white blood cells, insulin-like growth factor 1 (IGF-1) and gamma-glutamyl transferase. For each of these parameters, ML provided threshold values identifying either MUO or MHO. A second model including IGF-1 zSDS, a surrogate marker of IGF-1 normalized by age and sex, was even more accurate with a 71.84% and 72.3% precision, respectively. Our results demonstrated high IGF-1 levels in MHO patients, thus highlighting a possible role of IGF-1 as a novel metabolic health parameter to effectively predict the development of MUO using ML technology.