Safety In Research Institutions: How to Better Communicate the Risks using Numerical Simulations

What are the risks for the personnel following a gas leak in a laboratory? How effective are the safety measures (gas detection) put in place? Are users aware of how fast they can be exposed? These questions have been approached with the intent to better communicate to the users the risks involved in their daily activities by showing how a gas is dispersed in their laboratory. In order to do so, a software tool performing Computation Fluid Dynamics (CFD) simulations of a pollutant gas dispersion in a room has been developed by the Chemical and Physical Safety Group (GSCP) at EPFL in collaboration with Optimad Engineering srl. The software and its physical model have been based on experimental data obtained by the GSCP used as a benchmark for the numerical simulation results. As side deliverables, these results are been used to place gas leak detectors in the most efficient location, thus optimizing the cost of safety measures. Furthermore, the simulation results have then been used to create videos showing the pollutant gas dispersion in the laboratory. A real case is presented in order to reveal the power of the used dual methodology: modelling as a mean of communication for an effective training/teaching of the personnel. The visual representation of the risks has a greater impact than any other means of communication availabl

Beneficial Effects of Table Grape Use on Serum Levels of Omega-3 Index and Liver Function: A Randomized Controlled Clinical Trial

This clinical trial was aimed to investigate the effects of fresh table grape intake on the serum levels of the Omega-3 index, defined as the sum of eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) levels. Forty consecutive healthy subjects were randomly assigned to the control group, receiving only dietary recommendations, and the grape group receiving a daily dose of 5 g of fresh table grape per kg of body weight, for 21 days. Compared with baseline, the grape treatment produced no significant difference in the serum levels of glucose, liver transaminase, and triglycerides, with the exception of cholesterol value, which was significantly reduced in both control and grape group (180.5 ± 20.32 vs. 196.1 ± 30.0 and 181.4 ± 21.9 vs. 194.3 ± 37.5, respectively). After 4 weeks from the end of grape treatment, the analysis of single fatty acids showed a significant increase in oleic acid content (14.15 ± 1.8 vs. 12.85 ± 1.6, p p p p < 0.001). The data suggest that the intake of fresh grape improves the Omega-3 index in the serum and exerts beneficial effects on liver function through the overexpression of adiponectin and the reduction in FGF21 levels

Neurological diseases and COVID-19: prospective analyses using the UK Biobank

COVID-19 (Coronavirus disease-19) may present with neurological signs, but whether people already affected by neurological conditions are at a higher risk of contracting COVID-19 is still not known. We, therefore, aimed to investigate the association of previously diagnosed neurological conditions with COVID-19. 502,536 community-dwelling UK Biobank participants (54.4% male, mean age 56.6 ± 10.3 years) were included. Among these, 57,463 participants had a diagnosis of neurological conditions (11.43%) and a total of 1326 COVID-19-positive cases were identified (0.26%). Neurological conditions were identified through medical history and linkage to data on hospital admissions (ICD-10 code G00-G99). COVID-19 presence was diagnosed using the data provided by Public Health England. The association of previous diagnosis of neurological conditions with COVID-19 was evaluated through logistic regressions, adjusted for potential confounders, reported as odds ratios (ORs) with their 95% confidence intervals (CIs). Nerve, nerve root and plexus disorders (G50-G59) were the most common conditions identified. The presence of COVID-19 was almost doubled in neurological conditions compared to the general population (0.45 vs. 0.24%, p < 0.0001). Previously diagnosed neurological conditions were associated with 60% higher odds of COVID-19 positive in the multivariable-adjusted model (OR = 1.6, 95% CI 1.4-1.8). Other degenerative diseases of the nervous system, extrapyramidal and movement disorders, polyneuropathies and other disorders of the peripheral nervous system, cerebral palsy and other paralytic syndromes were significantly associated with a higher odds of COVID-19. The presence of neurological conditions was associated with a significantly higher likelihood of COVID-19 compared to the general population

Adherence to a healthy lifestyle and Multiple Sclerosis: a case-control study from the UK Biobank

Background: Multiple sclerosis (MS) is a common and disabling condition. The importance of healthy lifestyle for this disease is poorly explored. Objective: To test whether adherence to healthier lifestyle patterns is associated with a lower presence of multiple sclerosis (MS). Methods: By using a case–control design, we investigated the combined association of four healthy lifestyle-related factors (no current smoking, healthy diet, exercising regularly, body mass index <30 kg/m2) and the prevalence of MS. A logistic regression analysis, adjusted for potential confounders, was used and data reported as odds ratios (ORs) with their 95% confidence intervals (CIs). Results: 728 participants with MS were matched with healthy controls (n = 2,912) using a propensity score approach. In a multivariable analysis, compared to those who scored low in the composite lifestyle score (0–1 healthy lifestyle factors), people who adopted all four low risk lifestyle factors showed a 71% lower odds of having MS (OR = 0.29; 95% CI: 0.15–0.56). Moreover, there was a strong linear trend, suggesting that the higher number of healthy lifestyle behaviors was associated with lower odds of having MS. Conclusion: Following a healthy lifestyle is associated with a lower prevalence of MS. This association should be explored further in cohort studies

Non-alcoholic fatty liver disease is associated with higher metabolic expenditure in overweight and obese subjects : a case-control study

Non-alcoholic fatty liver disease (NAFLD) is a common condition in Western countries. However, their metabolic characteristics are poorly known even though they could be important. Therefore, the objective of this study was to measure resting metabolic parameters in overweight/obese adults with hepatic steatosis compared to controls, matched for age, sex, and obesity level. Hepatic steatosis was diagnosed with liver ultrasound. Energy metabolism was measured with indirect calorimetry: energy expenditure (REE), predicted REE, the ratio between REE and the predicted REE, and the respiratory quotient (RQ) were reported. We measured some anthropometric, body composition, and bio-humoral parameters; 301 participants with NAFLD were matched for age, sex, and obesity level with 301 participants without NAFLD. People with NAFLD showed significantly higher REE (1523 ± 238 vs. 1464 ± 212 kcal, p = 0.005), REE/REE predicted ratio (98.2 ± 9.4 vs. 95.7 ± 8.1, p = 0.002), and RQ (0.88 ± 0.08 vs. 0.85 ± 0.07, p = 0.03). Moreover, the NAFLD group had significantly higher inflammatory and insulin-resistance parameters compared to controls. In conclusion, NAFLD is associated with a significantly higher metabolic expenditure, as measured with indirect calorimetry, compared to a similar cohort of individuals without this condition. Higher inflammatory levels in patients with NAFLD can probably explain our findings, even if other research is needed on this issue.peer-reviewe

Long-term follow up of metastatic melanoma patients treated with Thymosin alpha-1: investigating immune checkpoints synergy

Background: Immune checkpoint blockade antibodies (imAbs), such as the anti Cytotoxic T Lymphocyte Antigen-4 (CTLA-4) ipilimumab (IPI) raised overall survival (OS) in metastatic melanoma (MM). Further, long-term OS is a crucial endpoint in MM. Thymosin alpha-1 (Tα1) with dacarbazine (DTIC) showed activity in a phase II trial and a compassionate use program (EAP). We report on long-term follow-up of patients treated with Tα1 to investigate the preconditioning role of Tα1 in imAbs-treated patients. Methods: Records of patients with melanoma treated with Tα1 within a phase II trial and EAP program were reviewed comparing median OS among patients that sequentially received anti-CTLA-4 imAb and Tα1. Further, the effect of Tα1 on IPI long-term survivor patients was investigated. Results: Among patients treated with Tα1, 21/61 patients received sequentially even anti CTLA-4 imAbs. Median OS at the data cut-off was 57.8 and 7.4 months in patients treated sequentially with anti-CTLA-4 imAbs or not, respectively. Moreover, pretreatment with Tα1 in all (95) IPI-evaluable patients confirmed a significant increase in long-term OS. Conclusion: This is the first report on long-term follow-up of Tα1-treated patients. Moreover, an advantage in OS in patients sequentially treated with Tα1 and IPI was seen that suggests a synergistic effect

Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study.

Background & aimsLiver ultrasound scan (US) use in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) causes costs and waiting lists overloads. We aimed to compare various Machine learning algorithms with a Meta learner approach to find the best of these as a predictor of NAFLD.MethodsThe study included 2970 subjects, 2920 constituting the training set and 50, randomly selected, used in the test phase, performing cross-validation. The best predictors were combined to create three models: 1) FLI plus GLUCOSE plus SEX plus AGE, 2) AVI plus GLUCOSE plus GGT plus SEX plus AGE, 3) BRI plus GLUCOSE plus GGT plus SEX plus AGE. Eight machine learning algorithms were trained with the predictors of each of the three models created. For these algorithms, the percent accuracy, variance and percent weight were compared.ResultsThe SVM algorithm performed better with all models. Model 1 had 68% accuracy, with 1% variance and an algorithm weight of 27.35; Model 2 had 68% accuracy, with 1% variance and an algorithm weight of 33.62 and Model 3 had 77% accuracy, with 1% variance and an algorithm weight of 34.70. Model 2 was the most performing, composed of AVI plus GLUCOSE plus GGT plus SEX plus AGE, despite a lower percentage of accuracy.ConclusionA Machine Learning approach can support NAFLD diagnosis and reduce health costs. The SVM algorithm is easy to apply and the necessary parameters are easily retrieved in databases