Dark Chocolate Intake Positively Modulates Redox Status and Markers of Muscular Damage in Elite Football Athletes: A Randomized Controlled Study

Intensive physical exercise may cause increase oxidative stress and muscular injury in elite football athletes. The aim of this study was to exploit the effect of cocoa polyphenols on oxidative stress and muscular injuries induced by intensive physical exercise in elite football players. Oxidant/antioxidant status and markers of muscle damage were evaluated in 24 elite football players and 15 controls. Furthermore, the 24 elite football players were randomly assigned to either a dark chocolate (>85% cocoa) intake (n = 12) or a control group (n = 12) for 30 days in a randomized controlled trial. Oxidative stress, antioxidant status, and muscle damage were assessed at baseline and after 30 days of chocolate intake. Compared to controls, elite football players showed lower antioxidant power and higher oxidative stress paralleled by an increase in muscle damage markers. After 30 days of dark chocolate intake, an increased antioxidant power was found in elite athletes assuming dark chocolate. Moreover, a significant reduction in muscle damage markers (CK and LDH, p < 0.001) was observed. In the control group, no changes were observed with the exception of an increase of sNox2-dp, H2O2, and myoglobin. A simple linear regression analysis showed that sNox2-dp was associated with a significant increase in muscle damage biomarker release (p = 0.001). An in vitro study also confirmed that polyphenol extracts significantly decreased oxidative stress in murine myoblast cell line C2C12-derived. These results indicate that polyphenol-rich nutrient supplementation by means of dark chocolate positively modulates redox status and reduced exercise-induced muscular injury biomarkers in elite football athletes. This trial is registered with NCT03288623

Efficient photoinduced charge separation in a BODIPY-C60 dyad

A donor-acceptor dyad composed of a BF2-chelated dipyrromethene (BODIPY) and a C60 fullerene has been newly synthesized and characterized. The two moieties are linked by direct addition of an azido substituted BODIPY on the C60, producing an imino-fullerene-BODIPY adduct. The photoinduced charge transfer process in this system was studied by ultrafast transient absorption spectroscopy. Electron transfer toward the fullerene was found to occur selectively exciting both the BODIPY chromophore at 475 nm and the C60 unit at 266 nm on a time scale of a few picoseconds, but the dynamics of charge separation was different in the two cases. Eletrochemical studies provided information on the redox potentials of the involved species and spectroelectrochemical measurements allowed to unambiguously assign the absorption band of the oxidized BODIPY moiety, which helped in the interpretation of the transient absorption spectra. The experimental studies were complemented by a theoretical analysis based on DFT computations of the excited state energies of the two components and their electronic couplings, which allowed identification of the charge transfer mechanism and rationalization of the different kinetic behavior observed by changing the excitation conditions

A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03&nbsp;L/min, when the minimum number of sensors was considered, to 9.97&nbsp;L/min for the complete setting (target flow range: 69.231&nbsp;±&nbsp;21.477&nbsp;L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r&nbsp;=&nbsp;0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE&nbsp;=&nbsp;10.99&nbsp;L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications

Management of Acute and Chronic Pancreatitis

Pancreatitis is a major public health issue worldwide. There is geographical variation in the burden of acute and chronic pancreatitis (CP). Globally, the age-standardized prevalence rate increased from 1990 to 2017. Acute pancreatitis (AP) is now one of the most common reasons for hospitalization with a gastrointestinal condition. The essential requirements for the management of AP are accurate diagnosis, appropriate triage, high-quality supportive care, monitoring for and treatment of complications, and prevention of relapse. Clinicians should be aware of the time course and the best management of AP, identifying which patient will have a severe course allowing earlier triage to an intensive care unit and earlier initiation of effective therapy. CP is a pathologic fibroinflammatory syndrome of the pancreas in individuals with genetic, environmental, and other risk factors who develop persistent pathologic responses to parenchymal injury or stress. Diagnosing the underlying pathologic process early in the disease course and managing the syndrome to change the natural course of disease and minimize adverse disease effects are the managing paradigm. In this review, we consider recent changes in the management of acute and CP, as well as common misunderstandings and areas of ongoing controversy

Stigmatization and resilience in inflammatory bowel disease patients at one-year follow-up

IntroductionInflammatory bowel disease (IBD), namely ulcerative colitis and Crohn’s disease, is a chronic relapsing immune-mediated condition that may cause an impairment of social functions due to stigmatisation. Resilience instead is associated with an improvement in coping with adversities and thus may counteract the detrimental effects of stigmatisation. We herein sought to determine the fluctuation of stigmatisation and resilience in a cohort of patients with IBD at 1-year follow-up.MethodsThis is a prospective, monocentric study conducted in a tertiary referral centre. All patients with IBD were assessed at enrolment and at oneyear follow-up. Several clinical and demographic variables were collected. Stigmatisation was assessed through a validated Italian version of the Perceived Stigma Scale for IBD (PSS-IBD), while resilience was assessed through the 25-item Connor Davidson Resilience Scale (CD-RISC25). Also, self-efficacy (SEF) and self-esteem (SES) scales were assessed.ResultsIn this study, 105 patients were included (46 Crohn’s disease, 59 ulcerative colitis; overall mean age 47 years ±11, M:F ratio 1:1.2). None of the 4 scales showed a statistically significant variation at one year compared to baseline (median CD-RISC25 64 at baseline vs 61 at follow-up; SEF 31 vs 30; SES 32.5 vs 32; PSS-IBD 0.45 vs 0.45). A statistically significant and inverse correlation was found between CD-RISC25 and PSS-IBD (rho -0.222, p=0.01), SEF and PSS-IBD (rho -0.219, p= 0.01), SES and PSS-IBD (-0.316, p=0.003). CD-RISC25 was found to be positively associated with inactive IBD (p=0.05).DiscussionIn this prospective study we have shown for the first time that stigmatisation, resilience, SEF and SEM did not change over a one-year time span, suggesting that, based on the information gathered, these characteristics may be independent from IBD severity or IBD flares. Furthermore, we found an inverse correlation of stigma with resilience, SEF and SES, suggesting an important role that these variables may have on preventing stigmatisation

Exploring changes in children’s well-being due to COVID-19 restrictions: the Italian EpaS-ISS study

BackgroundWhile existing research has explored changes in health behaviours among adults and adolescents due to the COVID-19 outbreak, the impact of quarantine on young children's well-being is still less clear. Moreover, most of the published studies were carried out on small and non-representative samples. The aim of the EpaS-ISS study was to describe the impact of the COVID-19 pandemic on the habits and behaviours of a representative sample of school children aged mainly 8-9 years and their families living in Italy, exploring the changes in children's well-being during the COVID-19 pandemic compared to the immediately preceding time period.MethodsData were collected using a web questionnaire. The target population was parents of children attending third-grade primary schools and living in Italy. A cluster sample design was adopted. A Well-Being Score (WBS) was calculated by summing the scores from 10 items concerning the children's well-being. Associations between WBS and socio-demographic variables and other variables were analysed.ResultsA total of 4863 families participated. The children's WBS decreased during COVID-19 (median value from 31 to 25; p = 0.000). The most statistically significant variables related to a worsening children's WBS were: time of school closure, female gender, living in a house with only a small and unliveable outdoor area, high parents' educational level and worsening financial situation.ConclusionsAccording to parents ' perception, changes in daily routine during COVID-19 negatively affected children's well-being. This study has identified some personal and contextual variables associated with the worsening of children's WBS, which should be considered in case of similar events

The interplay among psychopathology, personal resources, context-related factors and real-life functioning in schizophrenia: stability in relationships after 4 years and differences in network structure between recovered and non-recovered patients

Improving real-life functioning is the main goal of the most advanced integrated treatment programs in people with schizophrenia. The Italian Network for Research on Psychoses previously explored, by using network analysis, the interplay among illness-related variables, personal resources, context-related factors and real-life functioning in a large sample of patients with schizophrenia. The same research network has now completed a 4-year follow-up of the original sample. In the present study, we used network analysis to test whether the pattern of relationships among all variables investigated at baseline was similar at follow-up. In addition, we compared the network structure of patients who were classified as recovered at follow-up versus those who did not recover. Six hundred eighteen subjects recruited at baseline could be assessed in the follow-up study. The network structure did not change significantly from baseline to follow-up, and the overall strength of the connections among variables increased slightly, but not significantly. Functional capacity and everyday life skills had a high betweenness and closeness in the network at follow-up, as they had at baseline, while psychopathological variables remained more peripheral. The network structure and connectivity of non-recovered patients were similar to those observed in the whole sample, but very different from those in recovered subjects, in which we found few connections only. These data strongly suggest that tightly coupled symptoms/dysfunctions tend to maintain each other's activation, contributing to poor outcome in schizophrenia. Early and integrated treatment plans, targeting variables with high centrality, might prevent the emergence of self-reinforcing networks of symptoms and dysfunctions in people with schizophrenia

The interplay among psychopathology, personal resources, context-related factors and real-life functioning in schizophrenia: stability in relationships after 4 years and differences in network structure between recovered and non-recovered patients

Improving real-life functioning is the main goal of the most advanced integrated treatment programs in people with schizophrenia. The Italian Network for Research on Psychoses previously explored, by using network analysis, the interplay among illness-related variables, personal resources, context-related factors and real-life functioning in a large sample of patients with schizophrenia. The same research network has now completed a 4-year follow-up of the original sample. In the present study, we used network analysis to test whether the pattern of relationships among all variables investigated at baseline was similar at follow-up. In addition, we compared the network structure of patients who were classified as recovered at follow-up versus those who did not recover. Six hundred eighteen subjects recruited at baseline could be assessed in the follow-up study. The network structure did not change significantly from baseline to follow-up, and the overall strength of the connections among variables increased slightly, but not significantly. Functional capacity and everyday life skills had a high betweenness and closeness in the network at follow-up, as they had at baseline, while psychopathological variables remained more peripheral. The network structure and connectivity of non-recovered patients were similar to those observed in the whole sample, but very different from those in recovered subjects, in which we found few connections only. These data strongly suggest that tightly coupled symptoms/dysfunctions tend to maintain each other's activation, contributing to poor outcome in schizophrenia. Early and integrated treatment plans, targeting variables with high centrality, might prevent the emergence of self-reinforcing networks of symptoms and dysfunctions in people with schizophrenia

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 &lt; 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&nbsp;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

Disease-Modifying Therapies and Coronavirus Disease 2019 Severity in Multiple Sclerosis

Objective: This study was undertaken to assess the impact of immunosuppressive and immunomodulatory therapies on the severity of coronavirus disease 2019 (COVID-19) in people with multiple sclerosis (PwMS). Methods: We retrospectively collected data of PwMS with suspected or confirmed COVID-19. All the patients had complete follow-up to death or recovery. Severe COVID-19 was defined by a 3-level variable: mild disease not requiring hospitalization versus pneumonia or hospitalization versus intensive care unit (ICU) admission or death. We evaluated baseline characteristics and MS therapies associated with severe COVID-19 by multivariate and propensity score (PS)-weighted ordinal logistic models. Sensitivity analyses were run to confirm the results. Results: Of 844 PwMS with suspected (n = 565) or confirmed (n = 279) COVID-19, 13 (1.54%) died; 11 of them were in a progressive MS phase, and 8 were without any therapy. Thirty-eight (4.5%) were admitted to an ICU; 99 (11.7%) had radiologically documented pneumonia; 96 (11.4%) were hospitalized. After adjusting for region, age, sex, progressive MS course, Expanded Disability Status Scale, disease duration, body mass index, comorbidities, and recent methylprednisolone use, therapy with an anti-CD20 agent (ocrelizumab or rituximab) was significantly associated (odds ratio [OR] = 2.37, 95% confidence interval [CI] = 1.18-4.74, p = 0.015) with increased risk of severe COVID-19. Recent use (&lt;1 month) of methylprednisolone was also associated with a worse outcome (OR = 5.24, 95% CI = 2.20-12.53, p = 0.001). Results were confirmed by the PS-weighted analysis and by all the sensitivity analyses. Interpretation: This study showed an acceptable level of safety of therapies with a broad array of mechanisms of action. However, some specific elements of risk emerged. These will need to be considered while the COVID-19 pandemic persists