Attitudinal and demographic factors associated with seeking help and receiving antidepressant medication for symptoms of common mental disorder

BACKGROUND: Despite the increased attention given to improvement of mental health-related knowledge and attitudes, rates of help-seeking for mental illness remain low even in countries with well-developed mental health services. This study examines the relationships between attitudes to mental illness, symptoms of common mental disorder and seeking-help and receiving medication for a mental health problem.METHODS: We used data from the nationally representative Health Survey for England 2014 to design three logistic regression models to test for the effects of attitudes to mental illness (measured by a shortened version of the Community Attitudes toward the Mentally Ill, CAMI scale) on: recent contact with a doctor for a mental health problem; use of any type of mental health service in the last 12 months; and having antidepressants currently prescribed, while controlling for symptoms of common mental disorder (measured by the General Health Questionnaire, GHQ). We also tested for an interaction between attitudes to mental illness and symptoms of common mental disorder on the outcomes.RESULTS: A significant but very small effect of CAMI score was found on 'antidepressants currently prescribed' model (OR = 1.01(1.00, 1.02) but not on the two indicators of help-seeking. We also found a significant but very small interaction between CAMI and GHQ scores on recent contact with a doctor (OR = 0.99, 95% CI (0.990, 0.998); adjusted Wald test P = 0.01)). Knowing someone with a mental illness had a significant positive effect on help-seeking indicated by: (a) recent contact with a doctor (2.65 (1.01, 6.98)) and (b) currently prescribed antidepressant (2.67 (1.9, 3.75)) after controlling for attitudes to mental illness.CONCLUSIONS: Our results suggest that knowing someone with a mental health problem seems to have a further positive effect on help-seeking, beyond improving attitudes to mental illness. Furthermore, multiple different types and aspects of stigma may contribute to help-seeking behaviours, consequently multi-faceted approaches are likely to be most efficient.</p

MONITORING THE POLLUTION OF GROUNDWATER IN THE AREA OF INDUSTRIAL WASTE

Monitoring of the underground water pollution in the deposit of waste inindustrial area. The paper presents the monitoring of the pollution phenomenon ofunderground water in the industrial landfill area. Industrial landfill causes pronouncedunderground water pollution in the operation phase, but also in the conservation phase.The pollution monitoring is carried out on all environmental components: air, soil andunderground water. Pollution phenomenon is analyzed in time by using a tracking anddata reception characteristic control section. The data taken is processed and interpreted toachieve the best environmental measures in the area of the landfill site. By usingsimulation models provides a forecast of the pollution in different periods of time. Thesimulation model is applicable to the operating period taking into account the change inquantities and concentrations of pollutants. This paper presents remediation measuresappropriate to the type of industrial landfill analyzed. The results obtained allow modelingof environmental protection measures and especially the subsoil and groundwater

Leaves anatomical and physiological adaptations of Vinca major ‘Variegata’ and Hedera helix L. to specific roof garden conditions

Urban agglomerations create extreme microclimates for plants, in which growth, development and survival means adaptation. Plantations expansions beyond the typical gardens to buildings, walls or other build structures were realized in many cities with a rigorous selection of plant species. Although the number of woody species well adapted to the urban environmental conditions is quite large, few species manage to grow and develop on the roofs. Two species - Vinca major ‘Variegata’ and Hedera helix, regularly used for this type of plantations in Bucharest, were selected to understand their mechanism of adaptation. A comparative study was conducted on these species, growing on a rooftop garden and at the ground level into a typical garden. Both species revealed considerable anatomical differences of the leaves. In addition, physiological determinations revealed a stronger intensity of photosynthesis, an intense transpiration and a lower respiration rate at plants grown in the roof garden

Linkage mapping of the Mediterranean cypress, Cupressus sempervirens, based on molecular and morphological markers

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THE MANAGEMENT AND ANALYSIS OF POWER QUALITY IN POWER DISTRIBUTION GRIDS BY USING PQVIEW SOFTWARE SYSTEM

Nowadays the power quality is considered one of the most important aspect regarding the performance of a power grid operator, concerning equally all the consumers categories, too. Since a low level of the power quality has negative consequences on the technical-economic indices of the power networks’operation, there are perfectly justified the permanent efforts of the power grids operators in finding the best methods and tools able to assist them in managing and analyzing a huge volume of power quality data. The paper presents the capabilities of an intelligent system for the management and analysis of power quality data, PQView. This system is used by power grid operators in their operational activity, as well as within Smart Grid Laboratory of University of Craiova’s INCESA for research and testing purposes. This software system was used for an extensive power quality analysis of real operation a PV power plant interconnected to MV power distribution grid

Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and non-invasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to non-invasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate non-invasive cell therapy characterization can be achieved with QBAM and machine learning