Behavior of the Uptake of Ibuprofen in Five Varieties of Horticultural Crops Irrigated with Regenerated Water

The current use of regenerated water in agriculture has led to the emergence of new forms of pollutants, such as pharmaceutical compounds (PCs) which are not fully eliminated in wastewater treatment plants (WWTPs). Therefore, if the effluents of such WWTPs are to be used for agricultural irrigation, the presence of PCs must be analysed and their concentrations determined. The main objective of this study was to evaluate the uptake of ibuprofen (IBP) in horticultural crops irrigated with WWTP effluents and its subsequent effect on human health due to their incorporation into the food chain. The study involved five varieties of crops (lettuce, parsley, cabbage, zucchini and broccoli) grown in a greenhouse and irrigated with WWTP effluent water, in which IBP was analysed. Of the varieties of regenerated water-irrigated horticultural crops, only the leaves of mini-romaine lettuce presented detectable levels of IBP, but without meaning any risk to human health

Analysis of WWTPs technologies based on the removal efficiency of Pharmaceutical Activated Compounds for water reuse purposes. A Fuzzy Multi-Criteria Decision Making approach

Wastewater treatment plants (WWTPs) are designed to remove organic matter, phosphorus, and nitrogen but not other pollutants such as Pharmaceutical Activated Compounds (PhAC). Purpose: This study involves the development of an approach based on a fuzzy version of a Multi-Criteria Decision Making (MCDM) method called TOPSIS which issues such as technology used in each WWTP, atmospheric temperature, hydraulic retention time or population equivalent can help in the decisions to design WWTPs when the efficiency of PhAC removal must be considered. Methodology: Eleven alternatives (WWTPs) located in the Southeast of Spain involving two technologies (MBR and CAS) and considering different ways of quantifying each technology in the TOPSIS ranking were studied. Findings: The resolution of the multi-criteria problem allowed the most efficient WWTPs in the removal of each PhAC to be classified, indicating that the criterion of technology was not overly important. However, some criteria were more relevant than others. Novelty: The current methodology can also be applied to the design of WWTPs to remove different PhAC, considering other criteria with the possibility of reusing wastewater

Prediction of Uptake of Carbamazepine and Diclofenac in Reclaimed Water-Irrigated Lettuces by Machine Learning Techniques

Currently, due to the global shortage of water, the use of reclaimed water from the Wastewater Treatment Plants (WWTPs) for the irrigation of crops is an alternative in areas with water scarcity. However, the use of this reclaimed water for vegetable irrigation is a potential entry of pharmaceutical products into the food chain due to the absorption and accumulation of these contaminants in different parts of the plants. In this work we carried out an analysis of five machine learning techniques (Random Forest, support vector machine, M5 Rules, Gaussian Process and artificial neural network) to predict the uptake of carbamazepine and diclofenac in reclaimed water-irrigated lettuces with the consequent saving of environmental and economic costs. For the different combinations of input and output, the prediction results using the of machine learning techniques proposed on the pharmaceutical components in reclaimed water-irrigated lettuces are satisfactory, being the best technique the Random Forest that obtains a model fit value (R-2) higher than 96.5% using a single input in the model and higher than 97% using two inputs in the model

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

The use and production of chemical compounds are subjected to strong legislative pressure. Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory aspects for a multitude of industries, such as chemical, pharmaceutical, or food, due to direct harm to humans, animals, plants, or the environment. Simultaneously, there are growing demands on the authorities to replace traditional in vivo toxicity tests carried out on laboratory animals (e.g., European Union REACH/3R principles, Tox21 and ToxCast by the U.S. government, etc.) with in silica computational models. This is not only for ethical aspects, but also because of its greater economic and time efficiency, as well as more recently because of their superior reliability and robustness than in vivo tests, mainly since the entry into the scene of artificial intelligence (AI)-based models, promoting and setting the necessary requirements that these new in silico methodologies must meet. This review offers a multidisciplinary overview of the state of the art in the application of AI-based methodologies for the fulfillment of regulatory-related toxicological issues. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use