Normative data and discriminative properties of short form 36 (SF-36) in Turkish urban population

BACKGROUND: SF-36 has been both translated into different languages and adapted to different cultures to obtain comparable data on health status internationally. However there have been only a limited number of studies focused on the discriminative ability of SF-36 regarding social and disease status in developing countries. The aim of this study was to obtain population norms of the short form 36 (SF-36) health survey and the association of SF-36 domains with demographic and socioeconomic variables in an urban population in Turkey. METHODS: A cross-sectional study. Face to face interviews were carried out with a sample of households. The sample was systematically selected from two urban Health Districts in Izmir, Turkey. The study group consisted of 1,279 people selected from a study population of 46,290 people aged 18 and over. RESULTS: Internal consistencies of the scales were high, with the exception of mental health and vitality. Physical health scales were associated with both age and gender. On the other hand, mental health scales were less strongly associated with age and gender. Women reported poorer health compared to men in general. Social risk factors (employment status, lower education and economic strain) were associated with worse health profiles. The SF-36 was found to be capable of discriminating disease status. CONCLUSION: Our findings, cautiously generalisable to urban population, suggest that the SF-36 can be a valuable tool for studies on health outcomes in Turkish population. SF-36 may also be a promising measure for research on health inequalities in Turkey and other developing countries

Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study

Exposures constitute a dense network of the environment: exposome. Here, we argue for embracing the exposome paradigm to investigate the sum of nongenetic "risk" and show how predictive modeling approaches can be used to construct an exposome score (ES; an aggregated score of exposures) for schizophrenia. The training dataset consisted of patients with schizophrenia and controls, whereas the independent validation dataset consisted of patients, their unaffected siblings, and controls. Binary exposures were cannabis use, hearing impairment, winter birth, bullying, and emotional, physical, and sexual abuse along with physical and emotional neglect. We applied logistic regression (LR), Gaussian Naive Bayes (GNB), the least absolute shrinkage and selection operator (LASSO), and Ridge penalized classification models to the training dataset. ESs, the sum of weighted exposures based on coefficients from each model, were calculated in the validation dataset. In addition, we estimated ES based on meta-analyses and a simple sum score of exposures. Accuracy, sensitivity, specificity, area under the receiver operating characteristic, and Nagelkerke's R2 were compared. The ESMeta-analyses performed the worst, whereas the sum score and the ESGNB were worse than the ESLR that performed similar to the ESLASSO and ESRIDGE. The ESLR distinguished patients from controls (odds ratio [OR] = 1.94, P < .001), patients from siblings (OR = 1.58, P < .001), and siblings from controls (OR = 1.21, P = .001). An increase in ESLR was associated with a gradient increase of schizophrenia risk. In reference to the remaining fractions, the ESLR at top 30%, 20%, and 10% of the control distribution yielded ORs of 3.72, 3.74, and 4.77, respectively. Our findings demonstrate that predictive modeling approaches can be harnessed to evaluate the exposome

White Noise Speech Illusions: A Trait-Dependent Risk Marker for Psychotic Disorder?

Introduction: White noise speech illusions index liability for psychotic disorder in case-control comparisons. In the current study, we examined i) the rate of white noise speech illusions in siblings of patients with psychotic disorder and ii) to what degree this rate would be contingent on exposure to known environmental risk factors (childhood adversity and recent life events) and level of known endophenotypic dimensions of psychotic disorder [psychotic experiences assessed with the Community Assessment of Psychic Experiences (CAPE) scale and cognitive ability]. Methods: The white noise task was used as an experimental paradigm to elicit and measure speech illusions in 1,014 patients with psychotic disorders, 1,157 siblings, and 1,507 healthy participants. We examined associations between speech illusions and increasing familial risk (control -> sibling -> patient), modeled as both a linear and a categorical effect, and associations between speech illusions and level of childhood adversities and life events as well as with CAPE scores and cognitive ability scores. Results: While a positive association was found between white noise speech illusions across hypothesized increasing levels of familial risk (controls -> siblings -> patients) [odds ratio (OR) linear 1.11, 95% confidence interval (CI) 1.02-1.21, p = 0.019], there was no evidence for a categorical association with sibling status (OR 0.93, 95% CI 0.79-1.09, p = 0.360). The association between speech illusions and linear familial risk was greater if scores on the CAPE positive scale were higher (p interaction = 0.003; ORlow CAPE positive scale 0.96, 95% CI 0.85-1.07; ORhigh CAPE positive scale 1.26, 95% CI 1.09-1.46); cognitive ability was lower (p interaction < 0.001; ORhigh cognitive ability 0.94, 95% CI 0.84-1.05; ORlow cognitive ability 1.43, 95% CI 1.23-1.68); and exposure to childhood adversity was higher (p interaction < 0.001; ORlow adversity 0.92, 95% CI 0.82-1.04; ORhigh adversity 1.31, 95% CI 1.13-1.52). A similar, although less marked, pattern was seen for categorical patient-control and sibling-control comparisons. Exposure to recent life events did not modify the association between white noise and familial risk (p interaction = 0.232). Conclusion: The association between white noise speech illusions and familial risk is contingent on additional evidence of endophenotypic expression and of exposure to childhood adversity. Therefore, speech illusions may represent a trait-dependent risk marker

A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

Cancer is one of the most common diseases worldwide and its treatment is a complex and time-consuming process. Specifically, prostate cancer as the most common cancer among male population has received the attentions of many researchers. Oncologists and medical physicists usually rely on their past experience and expertise to prescribe the dose plan for cancer treatment. The main objective of dose planning process is to deliver high dose to the cancerous cells and simultaneously minimize the side effects of the treatment. In this article, a novel TOPSIS case based reasoning goal-programming approach has been proposed to optimize the dose plan for prostate cancer treatment. Firstly, a hybrid retrieval process TOPSIS–CBR [technique for order preference by similarity to ideal solution (TOPSIS) and case based reasoning (CBR)] is used to capture the expertise and experience of oncologists. Thereafter, the dose plans of retrieved cases are adjusted using goal-programming mathematical model. This approach will not only help oncologists to make a better trade-off between different conflicting decision making criteria but will also deliver a high dose to the cancerous cells with minimal and necessary effect on surrounding organs at risk. The efficacy of proposed method is tested on a real data set collected from Nottingham City Hospital using leave-one-out strategy. In most of the cases treatment plans generated by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Developed decision support system can assist both new and experienced oncologists in the treatment planning process

Evidence, and replication thereof, that molecular-genetic and environmental risks for psychosis impact through an affective pathway

Background There is evidence that environmental and genetic risk factors for schizophrenia spectrum disorders are transdiagnostic and mediated in part through a generic pathway of affective dysregulation. Methods We analysed to what degree the impact of schizophrenia polygenic risk (PRS-SZ) and childhood adversity (CA) on psychosis outcomes was contingent on co-presence of affective dysregulation, defined as significant depressive symptoms, in (i) NEMESIS-2 (n = 6646), a representative general population sample, interviewed four times over nine years and (ii) EUGEI (n = 4068) a sample of patients with schizophrenia spectrum disorder, the siblings of these patients and controls. Results The impact of PRS-SZ on psychosis showed significant dependence on co-presence of affective dysregulation in NEMESIS-2 [relative excess risk due to interaction (RERI): 1.01, p = 0.037] and in EUGEI (RERI = 3.39, p = 0.048). This was particularly evident for delusional ideation (NEMESIS-2: RERI = 1.74, p = 0.003; EUGEI: RERI = 4.16, p = 0.019) and not for hallucinatory experiences (NEMESIS-2: RERI = 0.65, p = 0.284; EUGEI: -0.37, p = 0.547). A similar and stronger pattern of results was evident for CA (RERI delusions and hallucinations: NEMESIS-2: 3.02, p < 0.001; EUGEI: 6.44, p < 0.001; RERI delusional ideation: NEMESIS-2: 3.79, p < 0.001; EUGEI: 5.43, p = 0.001; RERI hallucinatory experiences: NEMESIS-2: 2.46, p < 0.001; EUGEI: 0.54, p = 0.465). Conclusions The results, and internal replication, suggest that the effects of known genetic and non-genetic risk factors for psychosis are mediated in part through an affective pathway, from which early states of delusional meaning may arise

Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum

Psychosis spectrum disorder has a complex pathoetiology characterised by interacting environmental and genetic vulnerabilities. The present study aims to investigate the role of gene-environment interaction using aggregate scores of genetic (polygenic risk score for schizophrenia (PRS-SCZ)) and environment liability for schizophrenia (exposome score for schizophrenia (ES-SCZ)) across the psychosis continuum

The sustainable materials roadmap

Over the past 150 years, our ability to produce and transform engineered materials has been responsible for our current high standards of living, especially in developed economies. However, we must carefully think of the effects our addiction to creating and using materials at this fast rate will have on the future generations. The way we currently make and use materials detrimentally affects the planet Earth, creating many severe environmental problems. It affects the next generations by putting in danger the future of the economy, energy, and climate. We are at the point where something must drastically change, and it must change now. We must create more sustainable materials alternatives using natural raw materials and inspiration from nature while making sure not to deplete important resources, i.e. in competition with the food chain supply. We must use less materials, eliminate the use of toxic materials and create a circular materials economy where reuse and recycle are priorities. We must develop sustainable methods for materials recycling and encourage design for disassembly. We must look across the whole materials life cycle from raw resources till end of life and apply thorough life cycle assessments (LCAs) based on reliable and relevant data to quantify sustainability. We need to seriously start thinking of where our future materials will come from and how could we track them, given that we are confronted with resource scarcity and geographical constrains. This is particularly important for the development of new and sustainable energy technologies, key to our transition to net zero. Currently 'critical materials' are central components of sustainable energy systems because they are the best performing. A few examples include the permanent magnets based on rare earth metals (Dy, Nd, Pr) used in wind turbines, Li and Co in Li-ion batteries, Pt and Ir in fuel cells and electrolysers, Si in solar cells just to mention a few. These materials are classified as 'critical' by the European Union and Department of Energy. Except in sustainable energy, materials are also key components in packaging, construction, and textile industry along with many other industrial sectors. This roadmap authored by prominent researchers working across disciplines in the very important field of sustainable materials is intended to highlight the outstanding issues that must be addressed and provide an insight into the pathways towards solving them adopted by the sustainable materials community. In compiling this roadmap, we hope to aid the development of the wider sustainable materials research community, providing a guide for academia, industry, government, and funding agencies in this critically important and rapidly developing research space which is key to future sustainability.journal articl