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. This also reinforces the relevance of species different from Klebsiella pneumoniae or Escherichia coli in the CPE landscape and circulating lineages and plasmids in local CPE epidemiology.Benzene is a known genotoxic carcinogen linked to many hematological abnormalities. S-phenylmercapturic acid (PHMA, N-Acetyl-S-(phenyl)-L-cysteine, CAS# 4775-80-8) is a urinary metabolite of benzene and is used as a biomarker to assess benzene exposure. Pre-S-phenylmercapturic acid (pre-PHMA) is a PHMA precursor that dehydrates to PHMA at acidic pH. Published analytical methods that measure urinary PHMA adjust urine samples to a wide range of pH values using several types of acid, potentially leading to highly variable results depending on the concentration of pre-PHMA in a sample. Information is lacking on the variation in sample preparation among laboratories regularly measuring PHMA and the effect of those differences on PHMA quantitation in human urine samples. To investigate the differences in PHMA quantitation, we conducted an inter-laboratory comparison that included the analysis of 50 anonymous human urine samples (25 self-identified smokers, 25 self-identified non-smokers), quality control samples, and commercially available reference samples in five laboratories using different analytical methods. Observed urinary PHMA concentrations were proportionally higher at lower pH and results for anonymous urine samples varied widely among the methods. The method with the neutral preparation pH yielded results about 60% lower than the method using the most acidic conditions. Samples spiked with PHMA showed little variation, suggesting that the variability in results in human urine samples across methods is driven by the acid-mediated conversion of pre-PHMA to PHMA.The rise of the COVID-19 pandemic has exposed the incongruity of individualization ideologies that position individuals at the centre of health care, by contributing, making informed decisions and exercising choice regarding their health options and lifestyle considerations. When confronted with a global health threat, government across the world, have understood that the rhetoric of individualization, personal responsibility and personal choice would only led to disastrous national health consequences. In other words, individual choice offers a poor criterion to guide the health and wellbeing of a population. This reality has forced many advanced economies around the world to suspend their pledges to 'small government', individual responsibility and individual freedom, opting instead for a more rebalanced approach to economic and health outcomes with an increasing role for institutions and mutualization. For many marginalized communities, individualization ideologies and personalization approaches have never worked. On the contrary, they have exacerbated social and health inequalities by benefiting affluent individuals who possess the educational, cultural and economic resources required to exercise 'responsibility', avert risks and adopt health protecting behaviours. The individualization of the management of risk has also further stigmatized the poor by shifting the blame for poor health outcomes from government to individuals. This paper will explore how the COVID-19 pandemic exposes the cracks of neoliberal rhetoric on personalization and opens new opportunities to approach the health of a nation as socially, economically and politically determined requiring 'upstream' interventions on key areas of health including housing, employment, education and access to health care.
Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis.
The study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell's C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia.
A total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 63 854) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3-3.6). External validation indicated strong performance on unseen data (Harrell's C 0.85, 95% CI 0.84-0.86), an increase of 0.06 from the original model.
Using NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.
Using NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.Stimulants are often used to treat attention deficit disorders and nasal congestion. IDN-6556 molecular weight As they can be misused and overdosed, the detection of stimulants is relevant in the toxicological field as well as in the doping control field. The effects of stimulants can indeed be beneficial for athletes. Therefore, their in-competition use is prohibited by the World Anti-Doping Agency (WADA). As stimulants represent one of the most detected categories of prohibited substances, automation of methods to detect and confirm their presence is desirable. Previous work has shown the advantages of using turbulent flow online solid-phase extraction liquid chromatography-tandem mass spectrometry (online SPE LC-MS-MS) for the detection and confirmation of diuretics and masking agents. Hence, a turbulent flow online solid-phase extraction (SPE) LC-MS-MS method, compliant with WADA's identification criteria, was developed and validated for the detection and confirmation of 80 stimulants or metabolites with limits of identification (LOIs) varying between 10 (or possibly lower) and 100 ng/ml.