‘Perceptions of non-accidental child deaths as preventable events: The impact of probability heuristics and biases on child protection work'

Anxiety about the possibility of non-accidental deaths of children has had a major influence on child care policy and practice over the last 40 years. The formal inquiry reports and media coverage of these rare events serve to maintain the perception that these are regular incidents that happen far too often and that they could have been prevented. This focus on individual events tends to distort a clear view of the actual probability of non-accidental deaths and serves to reinforce the notion that potentially all child care cases are risky and that any social work practitioner could be involved in such a case. As a result, work with children has become highly risk averse. However, in statistical terms, the probability of non-accidental child deaths is very low and recently has averaged about 55 deaths a year. Children are at considerably higher risk of being killed on the roads. This paper examines the way in which perceptions of the ‘high’ level of risk of possible child deaths are maintained despite the very low statistical probability of such incidents. It draws on thinking from behavioural psychology and, in particular the work of Kahneman and Tversky, to consider some of the biases in probability reasoning affecting people’s perception of risk and explores how inquiry reports into single past events reconfirm risk perceptions. It is suggested that recognition of the essentially unpredictable nature of future non-accidental child deaths would free up childcare professionals to work in a more positive and less risk-averse manner in the present

Advances in extraction and biological activities of crawfish chitosan and its application in decolorization of synthetic dyes

ABSTRACT An eco-friendly method of extraction for chitin and chitosan extracted from crawfish was our goal. Chitin is always present with proteins, minerals, and other components. This study used an eco-accommodating, novel technique for chitin and chitosan extraction. Lactobacillus lactis was used for the deproteinization and demineralization of chitin in a single stage by Saccharomyces cerevisiae (BB: biological-biological extraction) to convert chitin into chitosan. BB is a more environmentally friendly method of producing chitosan than deacetylation with NaOH (BC: biological-chemical extraction). Chitosan was characterized by FTIR. A high degree of deacetylation (%) was observed. The UV spectrum for chitosan was similar at 0.788, 0.415, and 1.150 for CC, BC, and BB, respectively. The results show that chitosan (BB) has potential applications in the biomedical fields such as antioxidant activity, anticancer activity against human liver cancer (HepG2), breast cancer (MCF-7) and human hepatocellular carcinoma (HCT) cell lines. The results in terms of water treatment and removal of dyes using chitosan (BB) are valuable in terms of its application in industrial wastewater treatment and demonstrate that it can be used as a biosorbent

Slag valorization from electric arc furnaces in concrete paver formulation

Slag is produced in enormous amounts by steelworks, which are scrap metal recycling industries that produce steel wire rods and steel reinforcing bars. In the absence of a sustainable recovery route, the latter pose a possible environmental risk. This work is concerned with the valorization of this by-product in the production of concrete pavers. To accomplish so, the slag was previously evaluated using X-ray fluorescence, particle size analysis, density, absorption coefficient, and other criteria that are recommended for usage in this field. The pavers were then manufactured using the Dreux Gorisse recipe, with slag substituting the gravel. The results demonstrate that the slag is rich in iron, which is characterized by lime, silica, and magnesia rates of 31.73%, 16.33%, and 16.33%, respectively, low percentages of manganese and alumina. The water absorption rate is between 2.6% and 2.8%, and their density is similar to 3.6 kg/l. Los Angeles has a 20-coefficient. As a result of their inclusion in the design of the pavers, they were able to split in line with the NF EN 1338 standard and retained a reasonable degree of tensile strength

Improvement of RNA-SIP by pyrosequencing to identify putative 4-n-nonylphenol degraders in activated sludge

International audienceNonylphenols (NP) have estrogenic potential because of their phenolic ring, but the organisms involved in the degradation of this alkylated phenol remain unidentified. Using 16S ribosomal RNA (rRNA)-based stable isotope probing (SIP) and a new method based on pyrosequencing, we identified the bacteria involved in the degradation of the aromatic ring of [U-ring-13C] 4-n-NP in aerobic sludge. The first order degradation rate of 4-n-NP was 5.5 d−1. Single strand conformation polymorphism of density-separated labeled and unlabeled 16S rRNA showed significant differences and enabled selection of four representative fractions for pyrosequencing. Nineteen phylotypes showed a significant enrichment in the heavy fraction in the labeled pulse. The relative abundances of these phylotypes were combined with the RNA concentration of each fraction to yield a simple model of the distribution of each phylotype across the gradient. This model was used to estimate the percentage of labeling for each phylotype. The sequences showing the highest labeling (11%) were closely related to Afipia sp. but represented only 2 % of the RNA in the heavy fraction of the labeled pulse. The sequences representing the largest proportion of the RNA in the heavy fraction were related to Propionibacterium acnes and Frateuria aurantia, which are known to possess enzymes for phenol degradation. The model shows that despite Afipia having the highest 13C enrichment, other species encoding phenol degradation pathways are responsible for more 13C incorporation. Last, we showed that some species represent 12% of the total RNA but contain only 1% 13C above natural abundance

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024