Optical sensing system based on wireless paired emitter detector diode device and ionogels for lab-on-a-disc water quality analysis

This work describes the first use of a wireless paired emitter detector diode device (PEDD) as an optical sensor for water quality monitoring in a lab-on-a-disc device. The microfluidic platform, based on an ionogel sensing area combined with a low-cost optical sensor is applied for pH (quantitative) and qualitative turbidity monitoring of water samples at the point-of-need. The autonomous capabilities of the PEDD system, combined with the portability and wireless communication of the full device, provide the flexibility needed for on-site water testing. Water samples from local fresh and brackish sources were successfully analysed using the device, showing very good correlation with standard bench-top systems

Sodium valproate and clozapine induced neutropenia: A case control study using register data

BACKGROUND: The use of clozapine is limited due to the occurrence of neutropenia, and the rare but life threatening adverse event of agranulocytosis. There is little epidemiological research into clinical factors that may impact on this risk. We conducted a case control study examining the clinical risk factors for neutropenia patients treated with clozapine. METHOD: A case-control study was conducted within a database of anonymised electronic clinical records. All patients who discontinued clozapine due to a neutropenic event were included as cases. Matched controls were selected from patients with a documented clozapine exposure at the time of the clozapine neutropenic event of the case patient, matched by duration of clozapine treatment. RESULTS: 136 cases and 136 controls were included. In multivariable analysis, the concurrent use of sodium valproate was associated with neutropenia (Odds Raito (OR) 2.28, 95%CI: 1.27–4.11, p = 0.006). There was a dose-response effect, with greater associations for higher doses. Patients who discontinued clozapine due to neutropenia were more likely to be of black ethnicity (OR 2.99, p < 0.001), were younger (t = 5.86, df = 267, p < 0.001), and received lower doses of clozapine (t = − 2.587, p = 0.01) than those who did not develop neutropenia. CONCLUSION: We identified an association between the concurrent use of sodium valproate and an increased risk of clozapine associated neutropenia. These results, taken in combination with the results from previous case series, suggest that the risk of clozapine associated neutropenia could be reduced by avoiding concurrent valproate treatment

Inequalities in glycemic management in people living with type 2 diabetes mellitus and severe mental illnesses: cohort study from the UK over 10 years

Introduction Using data from a a primary care pay-for-performance scheme targeting quality indicators, the objective of this study was to assess if people living with type 2 diabetes mellitus (T2DM) and severe mental illnesses (SMI) experienced poorer glycemic management compared with people living with T2DM alone, and if observed differences varied by race/ethnicity, deprivation, gender, or exclusion from the scheme. Research design and methods Primary care data from a cohort of 56 770 people with T2DM, including 2272 people with T2DM and SMI, from London (UK), diagnosed between January 17, 2008 and January 16, 2018, were used. Adjusted mean glycated hemoglobin (HbA1c) and HbA1c differences were assessed using multilevel regression models. Results Compared with people with T2DM only, people with T2DM/SMI were more likely to be of an ethnic minority background, excluded from the pay-for-performance scheme and residing in more deprived areas. Across the sample, mean HbA1c was lower in those with T2DM and SMI (mean HbA1c: 58 mmol/mol; 95% CI 57 to 59), compared with people with T2DM only (mean HbA1c: 59 mmol/mol; 95% CI 59 to 60). However, HbA1c levels were greater in Bangladeshi, Indian, Pakistani, and Chinese people compared with the White British reference in the T2DM/SMI group. People with T2DM/SMI who had been excluded from the pay-for-performance scheme, had HbA1c levels which were +7 mmol/mol (95% CI 2 to 11) greater than those with T2DM/SMI not excluded. Irrespective of SMI status, increasing deprivation and male gender were associated with increased HbA1c levels. Conclusions Despite a pay-for-performance scheme to improve quality standards, inequalities in glycemic management in people with T2DM and SMI persist in those excluded from the scheme and by gender, ethnicity, and area-level deprivation

Clinical correlates of vitamin D deficiency in established psychosis

Background Suboptimal vitamin D levels have been identified in populations with psychotic disorders. We sought to explore the relationship between vitamin D deficiency, clinical characteristics and cardiovascular disease risk factors among people with established psychosis. Methods Vitamin D levels were measured in 324 community dwelling individuals in England with established psychotic disorders, along with measures of mental health, cardiovascular risk and lifestyle choices. Vitamin D deficiency was defined as serum 25-hydroxyvitamin D (25-OHD) levels below 10 ng/ml (equivalent to 50 nmol/L). Results The mean 25-OHD serum level was 12.4 (SD 7.3) ng/ml, (range 4.0-51.7 ng/ml). Forty nine percent (n = 158) were vitamin D deficient, with only 14 % (n = 45) meeting criteria for sufficiency. Accounting for age, gender, ethnicity and season of sampling, serum 25-OHD levels were negatively correlated with waist circumference (r = −0.220, p < 0.002), triglycerides (r = −0.160, p = 0.024), total cholesterol (r = −0.144, p = 0.043), fasting glucose (r = −0.191, p = 0.007), HbA1c (r = −0.183, p = 0.01), and serum CRP levels (r = −0.211, p = 0.003) and were linked to the presence of metabolic syndrome. Conclusions This is the largest cross sectional study of serum 25-OHD levels in community dwelling individuals with established psychosis, indicating a high level of vitamin D deficiency. Lower vitamin D levels are associated with increased cardiovascular disease risk factors and in particular metabolic syndrome. Further research is needed to define appropriate protocols for vitamin D testing and supplementation in practice to see if this can improve cardiovascular disease risk

Gene-expression analysis of clozapine treatment in whole blood of patients with psychosis

OBJECTIVES: Clozapine is an atypical antipsychotic primarily prescribed for treatment-resistant schizophrenia. We tested the specific effect of clozapine versus other drug treatments on whole-blood gene expression in a sample of patients with psychosis from the UK. METHODS: A total of 186 baseline whole-blood samples from individuals receiving treatment for established psychosis were analysed for gene expression on Illumina HumanHT-12.v4 BeadChips. After standard quality-control procedures, 152 samples remained, including 55 from individuals receiving clozapine. In a within-case study design, weighted gene correlation network analysis was used to identify modules of coexpressed genes. The influence of mood stabilizers, lithium carbonate/lithium citrate and sodium valproate was studied to identify their possible roles as confounders. RESULTS: Individuals receiving clozapine as their only antipsychotic (clozapine monotherapy) had a nominal association with one gene-expression module, whereas no significant change in gene expression was found for other drugs. CONCLUSION: Overall, this study does not provide evidence that clozapine treatment induces medium to large different gene-expression patterns in human whole blood versus other antipsychotic treatments. This does not rule out the possibility of smaller effects as observed for other common antipsychotic treatments

Ethnicity and cardiovascular health inequalities in people with severe mental illnesses: protocol for the E-CHASM study

“The final publication is available at Springer via http://dx. doi.10.1007/s00127-016-1185-8JD is funded by the Health Foundation working with the Academy of Medical Sciences. CM is supported by a European Research Council Consolidator Award (Ref: ERC-CoG- 2014-Proposal 648837, REACH). RS is funded by the NIHR Spe- cialist Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psy- chiatry, King’s College London. GT and FG are supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South London at King’s College London Foundation Trust. GT acknowledges financial support from the Department of Health via the National Institute for Health Research (NIHR) Biomedical Research Centre and Dementia Unit awarded to South London and Maudsley NHS Foundation Trust in partnership with King’s College London and King’s College Hospital NHS Foundation Trust. GT is supported by the European Union Seventh Framework Programme (FP7/2007–2013) Emerald project. AR is funded by the European Union Horizon 2020 pro- gramme OpenMinTeD and KConnect projects, by the National Institute for Health Research (NIHR) Biomedical Research Centre and Dementia Biomedical Research Unit at South London and Maudsley NHS Foundation Trust and King’s College London, and by QBurs

Predicting onset of early- and late-treatment resistance in first-episode schizophrenia patients using advanced shrinkage statistical methods in a small sample

Evidence suggests there are two treatment-resistant schizophrenia subtypes (i.e. early treatment resistant (E-TR) and late-treatment resistant (L-TR)). We aimed to develop prediction models for estimating individual risk for these outcomes by employing advanced statistical shrinkage methods. 239 first-episode schizophrenia (FES) patients were followed-up for approximately 5 years after first presentation to psychiatric services; of these, n=56 (25.2%) were defined as E-TR and n=24 (12.6%) were defined as L-TR. Using known risk factors for poor schizophrenia outcomes, we developed prediction models for E-TR and L-TR using LASSO and RIDGE logistic regression models. Models’ internal validation was performed employing Harrell's optimism-correction with repeated cross-validation; their predictive accuracy was assessed through discrimination and calibration. Both LASSO and RIDGE models had high discrimination, good calibration. While LASSO had moderate sensitivity for estimating an individual risk for E-TR and L-TR, sensitivity estimated for RIDGE model for these outcomes was extremely low, which was due to having a very large estimated optimism. Although it was possible to discriminate with sufficient accuracy who would meet criteria for E-TR and L-TR during the 5-year follow-up after first contact with mental health services for schizophrenia, further work is necessary to improve sensitivity for these models

Utilising symptom dimensions with diagnostic categories improves prediction of time to first remission in first-episode psychosis

There has been much recent debate concerning the relative clinical utility of symptom dimensions versus conventional diagnostic categories in patients with psychosis. We investigated whether symptom dimensions rated at presentation for first-episode psychosis (FEP) better predicted time to first remission than categorical diagnosis over a four-year follow-up. The sample comprised 193 FEP patients aged 18–65 years who presented to psychiatric services in South London, UK, between 2006 and 2010. Psychopathology was assessed at baseline with the Positive and Negative Syndrome Scale and five symptom dimensions were derived using Wallwork/Fortgang's model; baseline diagnoses were grouped using DSM-IV codes. Time to start of first remission was ascertained from clinical records. The Bayesian Information Criterion (BIC) was used to find the best fitting accelerated failure time model of dimensions, diagnoses and time to first remission. Sixty percent of patients remitted over the four years following first presentation to psychiatric services, and the average time to start of first remission was 18.3 weeks (SD = 26.0, median = 8). The positive (BIC = 166.26), excited (BIC = 167.30) and disorganised/concrete (BIC = 168.77) symptom dimensions, and a diagnosis of schizophrenia (BIC = 166.91) predicted time to first remission. However, a combination of the DSM-IV diagnosis of schizophrenia with all five symptom dimensions led to the best fitting model (BIC = 164.35). Combining categorical diagnosis with symptom dimension scores in FEP patients improved the accuracy of predicting time to first remission. Thus our data suggest that the decision to consign symptom dimensions to an annexe in DSM-5 should be reconsidered at the earliest opportunity