Supporting blood pressure measurement prior to online prescription of the combined oral contraceptive.

Editorial - No Abstract available

A profile and approach to chronic disease in Abu Dhabi

Abstract As a country, the United Arab Emirates has developed very rapidly from a developing country with a largely nomadic population, to a modern and wealthy country with a Western lifestyle. This economic progress has brought undoubted social benefits and opportunities for UAE citizens, including a high and increasing life expectancy. However, rapid modernization and urbanization have contributed to a significant problem with chronic diseases, particularly obesity-related cardiovascular risk. In response the Health Authority of Abu Dhabi has significantly strengthened its data systems to better assess the baseline and measure the impact of targeted interventions. The unique population-level Weqaya Programme for UAE Nationals living in Abu Dhabi has recruited more than 94% of adults into a screening programme for the rapid identification of those at risk and the deployment of targeted interventions to control that risk. This article describes the burden of non-communicable disease in Abu Dhabi, and the efforts made by the Health Authority of Abu Dhabi to tackle this burden including the development of a whole population cardiovascular screening programme changes to health policy, particularly in terms of lifestyle and behaviour change, and empowerment of the community to enable individuals to make healthier choices. In addition, recommendations have been made for global responsibility for tackling chronic disease.</p

Utility of (MgO)12 nanocage as a chemical sensor for recognition of amphetamine drug: A computational inspection

DFT calculations on sensor-drug interactions are necessary for understanding binding mechanisms, predicting sensor performance, evaluating stability and reactivity, and rational design of sensor materials. We scrutinized the adsorption of amphetamine (AFE) on the pure magnesium oxide nano-cage (MgONC) by applying density functional theory. All geometries and single point energy computations were optimized at M06–2X/6–311 G (d, p). Furthermore, we performed an analysis of the natural bond orbital (NBO) and evaluated the values of partial natural charges. Additionally, we investigated donor-acceptor (D-A) interactions and examined the Wiberg bond index (WBI) in greater depth. The MgONC was capable of adsorbing AFE with greater strength with the energy of adsorption (Eads) of −48.19 kcal/mol (for stable configurations). Moreover, the NBO method demonstrated more effective D-A interactions between AFE and the MgONC. Based on the computations, for the most stable configuration, there was a substantial alteration in the HOMO-LUMO gap of the MgONC following the drug adsorption, thus increasing the electrical conductance (EC) of the MgONC. The sensing mechanism is related to the gap difference, which depends on the change in the EC. We adopted the conventional transition state theory for the prediction of recovery time. The computations indicated that the MgONC+ AFE configuration had a short recovery time for the desorption of AFE. Finally, based on our findings, we could conclude that the MgONC is an appropriate choice for the improvement of effective AFE sensors. DFT study of drug sensors will focus on enhancing sensitivity, selectivity, and stability while exploring novel materials and optimizing performance through theoretical simulations and analysis

Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps