We report results of analysis of a month-long measurement of indoor air and environment quality parameters in one gym during sporting activities such as football, basketball, volleyball, badminton, boxing, and fitness. We have determined an average single person's contribution to the increase of temperature, humidity, and dust concentration in the gym air volume of 12500 m(3) : during 90-min exercise performed at an average heart rate of 143 ± 10 bpm, a single person evaporated 0.94 kg of water into the air by sweating, contributed 0.03 K to the air temperature rise and added 1.5 μg/m(3) and 5 ng/m(3) to the indoor concentration of inhalable particles (PM10 ) and Ca concentration, respectively. As the breathing at the observed exercise intensity was about three times faster with respect to the resting condition and as the exercise-induced PM10 concentration was about two times larger than outdoors, a sportsman in the gym would receive about a sixfold higher dose of PM10 inside than he/she would have received at rest outside.

Background Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported from a standardised source over the next one to four weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results Over 52 weeks we collected and combined up to 28 forecast models for 32 countries. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 84% of participating models’ forecasts of incident cases (with a total N=862), and 92% of participating models’ forecasts of deaths (N=746). Across a one to four week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over four weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than two weeks. Code and data availability All data and code are publicly available on Github: covid19-forecast-hub-europe/euro-hub-ensemble.

No abstract

No abstract

No abstract

Acute kidney injury (AKI) represents frequent complication after cardiac surgery using cardiopulmonary bypass (CPB). In the hope to enhance earlier more reliable characterization of AKI, we tested the utility of neutrophil gelatinase-associated lipocalin (NGAL) and cystatin C (CysC) in addition to standard creatinine for early determination of AKI after cardiac surgery using CPB. Forty-one patients met the inclusion criteria. Arterial blood samples collected after induction of general anesthesia were used as baseline, further sampling occurred at CPB termination, 2 h after CPB, on the first and second day after surgery. According to AKIN classification 18 patients (44%) developed AKI (AKI1-2 groups) and 23 (56%) did not (non-AKI group). Groups were similar regarding demographics and operative characteristics. CysC levels differed already preoperatively (non-AKI vs. AKI2; P = 0.045; AKI1 vs. AKI2; P = 0.011), while postoperatively AKI2 group differed on the first day and AKI1 on the second regarding non-AKI group (P = 0.004; P = 0.021, respectively). NGAL and creatinine showed significant difference already 2 h after CPB between groups AKI2 and non-AKI and later on the first postoperative day between groups AKI1 and AKI2 (P = 0.028; P = 0.014, respectively). This study shows similar performance of early plasma creatinine and NGAL in patients with preserved preoperative renal function. It demonstrates that creatinine, as well as NGAL, differentiate subsets of patients developing AKI of clinically more advanced grade early after 2 h, also when used single and uncombined.

MTX therapy probably does not produce a chronic increase in erythrocyte ZMP or urinary AICAR concentrations. Collectively, our data do not support the hypothesis that MTX improves glucose homeostasis through chronic accumulation of ZMP.

AbstractMathematical modelling can be useful for predicting how infectious diseases progress, enabling us to show the likely outcome of an epidemic and help inform public health interventions. Different modelling techniques have been used to predict and simulate the spread of COVID-19, but they have not always been useful for epidemiologists and decision-makers. To improve the reliability of the modelling results, it is very important to critically evaluate the data used and to check whether or not due regard has been paid to the different ways in which the disease spreads through the population. As building an epidemiological model that is reliable enough and suits the current epidemiological situation within a country or region, certain criteria must be met in the modelling process. It might be necessary to use a combination of two or more different types of models in order to cover all aspects of epidemic modelling. If we want epidemiological models to be a useful tool in combating the epidemic, we need to engage experts from epidemiology, data science and statistics.