Toxicity evaluation of the contaminated area of Crotone from biological indicators: a multispecies approach.

Contamination of terrestrial and aquatic ecosystems by toxic industrial waste has become a major issue in many countries. Of particular concern is the reuse of toxic hazardous waste in construction materials. This paper examined for the first time the chemical and radiation ecotoxicity of site-specific Technological Enhanced Naturally Occurring Radioactive Materials (TENORM) residues from phosphate processing industry in soil environmental matrices through bioindicators. The area under investigation was the former industrial district of Crotone (Calabria, Italy), recently included within the Sites of National Interest (SIN), comprising the 42 Italian national priority contaminated sites. Major biological exposure pathways considered were absorption and bioaccumulation. The marine bacterium Vibrio fischeri and the freshwater crustacean Daphnia magna were employed as aquatic bioindicators, while for the soil ecosystem, the seeds of Sorghum saccharatum and Lepidium sativum were used. Selection of test species aimed at assessing the toxicity of wastes in soil as well as in freshwater or marine systems. Results indicated V. fischeri as the most sensitive of all the species tested (5.56 g/L), while D. magna was found to be affected at 94.27 g/L. An overall inhibition was observed in seedling growth as compared to control at the highest concentration of the pollutants (100 g/L), while seed germination was not adversely affected by the pollutant. At this preliminary level, data indicated a potential risk for biodiversity of the area. In fact, the measured toxicity thresholds, even if above 100 mg/L, are comparable to concentrations of the toxicants spread all over the territory of Crotone.

COVID-19 mortality risk assessment: An international multi-center study.

Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.