Seasonal variations in hydrogeochemical characteristics of groundwater were assessed from an intensive agricultural region to identify contaminants of concern that are a potential risk to human ...health. A total of 116 groundwater samples were collected grid-wise from an intensive agricultural region of confined Wanaparthy watershed to evaluate seasonal variations in hydrogeochemical processes of dissolved ions, nitrate health risk assessment and water quality during pre-monsoon (PRM) and post-monsoon (POM) seasons. The major ions concentration found in ascending order as PRM: F- < NO3-< SO42-< HCO3-< Cl- and K+< Mg+2< Ca+2< Na+ while POM: F- < NO3-< SO42-< Cl-< HCO3- and K+< Ca+2< Mg+2< Na+ respectively. Piper diagram for water-types shows PRM; Na-Cl type (70.68%) while POM; Ca-Mg-Cl type (39.66%) and Ca-HCO3 type (31.03%). Gibbs diagram explained the favorable environmental conditions as rock and evaporation dominance in both seasons. Spatial distribution map shows samples with higher and above permissible limits are found at/near to adjoining to higher-order streams and streams origin. As per the water quality index (WQI), 36.21% (PRM) and 60.34% (POM) fall in poor to unfit for drinking class. Hazard quotient (HQ) values of nitrate reach as high as for infants 1.31E + 01, children 1.23E + 01 and adults 4.68E + 00 respectively. Subsequently, HQ>1 with 68.97% of infants and 72.41% of children are in danger for non-carcinogenic ingestion of nitrate contaminated groundwater than in adults.
Display omitted
•Major ion concentration enriched in POM due to fertilizers leachate into aquifer.•Spatial distribution maps demonstrated the extent of groundwater contamination.•WQI showed majority groundwater samples in POM fall unfit for drinking category.•High risk to infants and children due to nitrate ingestion through drinking water.
Perfluoroalkyl acids (PFAAs), a group of synthetic organic chemicals with industrial and commercial uses, are of current concern because of increasing awareness of their presence in drinking water ...and their potential to cause adverse health effects. PFAAs are distinctive among persistent, bioaccumulative, and toxic (PBT) contaminants because they are water soluble and do not break down in the environment. This commentary discusses scientific and risk assessment issues that impact the development of drinking water guidelines for PFAAs, including choice of toxicological endpoints, uncertainty factors, and exposure assumptions used as their basis. In experimental animals, PFAAs cause toxicity to the liver, the immune, endocrine, and male reproductive systems, and the developing fetus and neonate. Low-dose effects include persistent delays in mammary gland development (perfluorooctanoic acid; PFOA) and suppression of immune response (perfluorooctane sulfonate; PFOS). In humans, even general population level exposures to some PFAAs are associated with health effects such as increased serum lipids and liver enzymes, decreased vaccine response, and decreased birth weight. Ongoing exposures to even relatively low drinking water concentrations of long-chain PFAAs substantially increase human body burdens, which remain elevated for many years after exposure ends. Notably, infants are a sensitive subpopulation for PFAA's developmental effects and receive higher exposures than adults from the same drinking water source. This information, as well as emerging data from future studies, should be considered in the development of health-protective and scientifically sound guidelines for PFAAs in drinking water.
Airborne particulate matter (APM) has an important role in inhalation exposure, especially in China. The environmental occurrence of conventional and unknown per- and polyfluoroalkyl substances ...(PFASs) in APM remains unclear. Therefore, in this study, a two-stage experiment was designed to identify potential PFASs and to investigate their distribution in APM. Indoor and outdoor APM samples were collected from five selected cities in China. Through PFAS homologue analysis and suspect screening, 50 peaks were identified with different confidence levels (levels 1–3). Among the identified PFASs, 34 emerging PFASs including p-perfluorous nonenoxybenzenesulfonate, 6:2 polyfluoroalkyl phosphate diester, n:2 fluorotelomer sulfonates, n:2 fluorinated telomer acids, n:2 chlorinated polyfluoroalkyl ether sulfonic acids, 1:n polyfluoroalkyl ether carboxylic acids (1:n PFECAs), perfluoroalkyl dioic acids (PFdiOAs), hydro-substituted perfluoroalkyl dioic acids (H-PFdiOAs), and unsaturated perfluorinated alcohols (UPFAs) were identified in APM. In particular, 1:n PFECAs, PFdiOAs, H-PFdiOAs, and UPFAs were first detected in APM. Although human exposure to perfluorooctanoic acid via inhaled APM was noted to not be a risk (hazard quotient <0.1) in this study, the expansion of the PFASs screened in APM implies that human exposure to PFASs might be much more serious and should be considered in future risk assessments in China.
Summary
Guidance is provided in an international setting on the assessment and specific treatment of postmenopausal women at low, high and very high risk of fragility fractures.
Introduction
The ...International Osteoporosis Foundation and European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis published guidance for the diagnosis and management of osteoporosis in 2019. This manuscript seeks to apply this in an international setting, taking additional account of further categorisation of increased risk of fracture, which may inform choice of therapeutic approach.
Methods
Clinical perspective and updated literature search.
Results
The following areas are reviewed: categorisation of fracture risk and general pharmacological management of osteoporosis.
Conclusions
A platform is provided on which specific guidelines can be developed for national use to characterise fracture risk and direct interventions.
This paper introduces Solution-focused Sustainability Assessment (SfSA), provides practical guidance formatted as a versatile process framework, and illustrates its utility for solving a wicked ...environmental management problem.
Society faces complex and increasingly wicked environmental problems for which sustainable solutions are sought. Wicked problems are multi-faceted, and deriving of a management solution requires an approach that is participative, iterative, innovative, and transparent in its definition of sustainability and translation to sustainability metrics. We suggest to add the use of a solution-focused approach. The SfSA framework is collated from elements from risk assessment, risk governance, adaptive management and sustainability assessment frameworks, expanded with the ‘solution-focused’ paradigm as recently proposed in the context of risk assessment. The main innovation of this approach is the broad exploration of solutions upfront in assessment projects. The case study concerns the sustainable management of slightly contaminated sediments continuously formed in ditches in rural, agricultural areas. This problem is wicked, as disposal of contaminated sediment on adjacent land is potentially hazardous to humans, ecosystems and agricultural products. Non-removal would however reduce drainage capacity followed by increased risks of flooding, while contaminated sediment removal followed by offsite treatment implies high budget costs and soil subsidence. Application of the steps in the SfSA-framework served in solving this problem. Important elements were early exploration of a wide ‘solution-space’, stakeholder involvement from the onset of the assessment, clear agreements on the risk and sustainability metrics of the problem and on the interpretation and decision procedures, and adaptive management. Application of the key elements of the SfSA approach eventually resulted in adoption of a novel sediment management policy. The stakeholder participation and the intensive communication throughout the project resulted in broad support for both the scientific approaches and results, as well as for policy implementation.
Display omitted
•Solution-focused Sustainability Assessment (SfSA) is introduced and defined.•SfSA explores stakeholder and science supported solutions for wicked problems.•SfSA considers multi-faceted people, planet, and profit metrics.•The focus on multiple solutions improves the utility of the assessment results.•The utility of SfSA is illustrated with a case study on sediment management.
To evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for the prediction of risk of cardiovascular diseases (CVDs), chronic kidney disease ...(CKD), diabetes (DM), and hypertension (HTN) and in a prospective cohort study using simple clinical predictors.
We conducted analyses in a population-based cohort study in Asian adults (n = 6,762). Five different ML models were considered—single-hidden-layer neural network, support vector machine, random forest, gradient boosting machine, and k-nearest neighbor—and were compared with standard logistic regression.
The incidences at 6 years of CVD, CKD, DM, and HTN cases were 4.0%, 7.0%, 9.2%, and 34.6%, respectively. Logistic regression reached the highest area under the receiver operating characteristic curve for CKD (0.905 0.88, 0.93) and DM (0.768 0.73, 0.81) predictions. For CVD and HTN, the best models were neural network (0.753 0.70, 0.81) and support vector machine (0.780 0.747, 0.812), respectively. However, the differences with logistic regression were small (less than 1%) and nonsignificant. Logistic regression, gradient boosting machine, and neural network were systematically ranked among the best models.
Logistic regression yields as good performance as ML models to predict the risk of major chronic diseases with low incidence and simple clinical predictors.
•Low-dimensional settings include low number of events and predictors.•In such settings, logistic regression yields as good performance as ML models.•ML techniques may not be warranted in such cases.
•Extensive literature review performed on in vivo GST activity in healthy humans.•Variability analysis of in vivo GST activity due to age, ethnicity, polymorphisms.•Tissue and organ distribution of ...GST activity is reported.•Bayesian meta-analysis was conducted to derive GST-related uncertainty factors.•Limited datasets highlighted large data gaps.
The input into the QIVIVE and Physiologically-Based kinetic and dynamic models of drug metabolising enzymes performance and their inter-individual differences significantly improve the modelling performance, supporting the development and integration of alternative approaches to animal testing. Bayesian meta-analyses allow generating and integrating statistical distributions with human in vitro metabolism data for quantitative in vitro-in vivo extrapolation. Such data are lacking on glutathione-S-transferases (GSTs). This paper reports for the first time results on the human variability of GST activities in healthy individuals, their tissue localisation and the frequencies of their major polymorphic variants by means of extensive literature search, data collection, data base creation and meta-analysis.
A limited number of papers focussed on in vivo GST inter-individual differences in humans. Ex-vivo total GST activity without discriminating amongst isozymes is generally reported, resulting in a high inter-individual variability.
The highest levels of cytosolic GSTs in humans are measured in the kidney, liver, adrenal glands and blood. The frequencies of GST polymorphisms for cytosolic isozymes in populations of different geographical ancestry were also presented. Bayesian meta-analyses to derive GST-related uncertainty factors provided uncertain estimates, due to the limited database.
Considering the relevance of GST activities and their pivotal role in cellular adaptive response mechanisms to chemical stressors, further studies are needed to identify GST probe substrates for specific isozymes and quantify inter-individual differences.
Abstract
Background
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to ...predict the outcome of patients with severe COVID-19 infection early.
Methods
Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients’ outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models’ performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model.
Results
The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at https://phenomics.fudan.edu.cn/risk_scores/.
Conclusions
Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients’ outcomes.
Environmental risk assessment traditionally relies on a wide range of in vivo testing to assess the potential hazards of chemicals in the environment. These tests are often time-consuming and costly ...and can cause test organisms’ suffering. Recent developments of reliable low-cost alternatives, both in vivo- and in silico-based, opened the door to reconsider current toxicity assessment. However, many of these new approach methodologies (NAMs) rely on high-quality annotated genomes for surrogate species of regulatory risk assessment. Currently, a lack of genomic information slows the process of NAM development. Here, we present a phylogenetically resolved overview of missing genomic resources for surrogate species within a regulatory ecotoxicological risk assessment. We call for an organized and systematic effort within the (regulatory) ecotoxicological community to provide these missing genomic resources. Further, we discuss the potential of a standardized genomic surrogate species landscape to enable a robust and nonanimal-reliant ecotoxicological risk assessment in the systems ecotoxicology era.
PDF
