COVID-19 pandemic continues to be a global health crisis. The gut microbiome critically affects the immune system, and some respiratory infections are associated with changes in the gut microbiome; here, we evaluated the role of nutritional and lifestyle habits that modulate gut microbiota on COVID-19 outcomes in a longitudinal cohort study that included 200 patients infected with COVID-19. Of these, 122 cases were mild and seventy-eight were moderate, according to WHO classification. After detailed explanation by a consultant in clinical nutrition, participants responded to a written questionnaire on daily sugar, prebiotic intake in food, sleeping hours, exercise duration and antibiotic prescription, during the past 1 year before infection. Daily consumption of prebiotic-containing foods, less sugar, regular exercise, adequate sleep and fewer antibiotic prescriptions led to a milder disease and rapid virus clearance. Additionally, data on these factors were compiled into a single score, the ESSAP score (Exercise, Sugar consumption, Sleeping hours, Antibiotics taken, and Prebiotics consumption; 0-11 points), median ESSAP score was 5 for both mild and moderate cases; however, the range was 4-8 in mild cases, but 1-6 in moderate (P = 0·001, OR: 4·2, 95 % CI 1·9, 9·1); our results showed a negative correlation between regular consumption of yogurt containing probiotics and disease severity (P = 0·007, OR: 1·6, 95 % CI 1·1, 2·1). Mild COVID-19 disease was associated with 10-20 min of daily exercise (P = 0·016), sleeping at least 8 h daily, prescribed antibiotics less than 5 times per year (P = 0·077) and ate plenty of prebiotic-containing food.

The long-term cardiorespiratory function in burn-injured children can be jeopardized due to complications brought on by the injury. This study sought to assess the cardio-respiratory responses to maximal exercise in children who sustained a burn injury and explore the relationships among cardio-respiratory response, physical activity levels (PALs), and health-related quality of life (HRQL). Forty-five burn-injured children (age:13.89±2.43 years; duration since burn-injury: 3.13±0.93 years) and 52 age- and gender-matched healthy children (14.15±2.27 years) participated in this study. Both cohorts were evaluated for the maximal exercise capacity [defined by peak oxygen uptake (VO2peak), maximum heart rate (HRmax), minute ventilation (VE), ventilatory equivalent (VEq), respiratory rate (RR), and respiratory exchange ratio (RER)], PALs, and HRQL. The burn-injured children had significantly lower VO2peak (P=.0001) and VE (P=.003) and higher VEq (P<.0001) and RR (P=.007) than their healthy controls, indicating less efficient cardio-respiratory capacity. However, the HRmax (P=.092) and RER (P=.251) were similar. The burn-injured children reported significantly lower PALs (P=.014) and HRQL (P<.0001). The PALs [r (95%CI) = 0.411 (0.132 to 0.624); P = .005] and HRQL [r (95%CI) = 0.536 (0.284 to 0.712); P = .0001] were significantly correlated with the cardio-respiratory capacity represented by VO2peak in burn-injured group. The variations in VO2peak explained ⁓ 17% and 28.7% of the variations in PALs and HRQL, respectively. In conclusion, the cardio-respiratory efficiency of the burn-injured children may remain limited, even up to a few years following the injury. The limited cardio-respiratory capacity account in part for the reduced PALs and HRQL.

This paper addresses a bi-objective dynamic multiprocessor open shop scheduling problem in which the simultaneous objectives of minimizing both the mean weighted flow time and the makespan are considered. This problem is commonly encountered in maintenance and healthcare diagnostic systems. Since it is NP-hard for both objectives, efficient heuristics are needed to quickly generate a set of non-dominated solutions that a decision maker would choose from. For this sake, two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective grey wolf optimizer (MOGWO) are developed in this paper. Both metaheuristics are hybridized with simulated annealing (SA) local search. Parameter tuning computational experiments are conducted first on a set of 30 small instances from the literature for which Pareto optimal solutions are known. Then, computational experiments on large randomly generated instances are conducted. Computational results for small instances show that the NSGA-II is capable of generating non-dominated solutions that are very close to the optimal Pareto front. Results also reveal that the performance of the NSGA-II is better in most of the cases compared to the MOGWO under different settings of the studied problem for both small and large instances. However, for large instances with large number of workstations and jobs, low loading level and high percentage of busy machines at the beginning of the schedule, the difference in performance between both metaheuristics is minor.