Identification of priority health conditions for field-based screening in urban slums in Bangalore, India

BACKGROUND: Urban slums are characterised by unique challenging living conditions, which increase their inhabitants' vulnerability to specific health conditions. The identification and prioritization of the key health issues occurring in these settings is essential for the development of programmes that aim to enhance the health of local slum communities effectively. As such, the present study sought to identify and prioritise the key health issues occurring in urban slums, with a focus on the perceptions of health professionals and community workers, in the rapidly growing city of Bangalore, India. METHODS: The study followed a two-phased mixed methods design. During Phase I of the study, a total of 60 health conditions belonging to four major categories: - 1) non-communicable diseases; 2) infectious diseases; 3) maternal and women's reproductive health; and 4) child health - were identified through a systematic literature review and semi-structured interviews conducted with health professionals and other relevant stakeholders with experience working with urban slum communities in Bangalore. In Phase II, the health issues were prioritised based on four criteria through a consensus workshop conducted in Bangalore. RESULTS: The top health issues prioritized during the workshop were: diabetes and hypertension (non-communicable diseases category), dengue fever (infectious diseases category), malnutrition and anaemia (child health, and maternal and women's reproductive health categories). Diarrhoea was also selected as a top priority in children. These health issues were in line with national and international reports that listed them as top causes of mortality and major contributors to the burden of diseases in India. CONCLUSIONS: The results of this study will be used to inform the development of technologies and the design of interventions to improve the health outcomes of local communities. Identification of priority health issues in the slums of other regions of India, and in other low and lower middle-income countries, is recommended

IOT Based Smart Wastewater Treatment Model for Industry 4.0 Using Artificial Intelligence

Wastewater is created by pharma firms and has become a huge worry for the ecosystem. There is a significant amount of toxins that are being dropped continuously from numerous pharmaceutical companies that causes serious damages to the environment and public health because of its comprising high organics as well as inorganic loadings and thus requirements appropriate treatment before final disposal to the ecosystem. Goal of this approach is to treat the wastewater treatment model with industrial data. Algorithms of the artificial neural network (ANN) were employed progressively to predict parameters for wastewater plants. This provision assists users to take remedial measures and function the process by the standards. It is proven as beneficial technology because of its complicated mechanism, dynamic and inconsistent changes in aspects, to overcome some of the limitations of common mathematical models for the wastewater treatment plant. The target is to achieve better prediction accuracy in wastewater treatment model. In this paper, ANN approaches are relevant to the prediction of input and effluent chemical oxygen demand (COD) for effluent treatment procedures. Artificial neural networks (ANNs) offer accurate technique modeling for complex systems using an artificial intelligence technique. Three distinct types of back-propagation ANN were devised to avoid the concentration of wastewater treatment facilities in the concentration of COD, suspended particles, and mixed liquid solids in an epidermal water treatment tank (MLSS). To anticipate COD levels in influential and effluent areas, two ANN-based techniques have been presented. The proper structure for the neural network models was identified via a variety of training and model testing methods. An efficient and robust forecasting tool has been created for the ANN model.</jats:p

Human genetic variants disrupt RGS14 nuclear shuttling and regulation of LTP in hippocampal neurons.

The human genome contains vast genetic diversity as naturally occurring coding variants, yet the impact of these variants on protein function and physiology is poorly understood. RGS14 is a multifunctional signaling protein that suppresses synaptic plasticity in dendritic spines of hippocampal neurons. RGS14 also is a nucleocytoplasmic shuttling protein, suggesting that balanced nuclear import/export and dendritic spine localization are essential for RGS14 functions. We identified genetic variants L505R (LR) and R507Q (RQ) located within the nuclear export sequence (NES) of human RGS14. Here we report that RGS14 encoding LR or RQ profoundly impacts protein functions in hippocampal neurons. RGS14 membrane localization is regulated by binding Gαi-GDP, whereas RGS14 nuclear export is regulated by Exportin 1 (XPO1). Remarkably, LR and RQ variants disrupt RGS14 binding to Gαi1-GDP and XPO1, nucleocytoplasmic equilibrium, and capacity to inhibit long-term potentiation (LTP). Variant LR accumulates irreversibly in the nucleus, preventing RGS14 binding to Gαi1, localization to dendritic spines, and inhibitory actions on LTP induction, while variant RQ exhibits a mixed phenotype. When introduced into mice by CRISPR/Cas9, RGS14-LR protein expression was detected predominantly in the nuclei of neurons within hippocampus, central amygdala, piriform cortex, and striatum, brain regions associated with learning and synaptic plasticity. Whereas mice completely lacking RGS14 exhibit enhanced spatial learning, mice carrying variant LR exhibit normal spatial learning, suggesting that RGS14 may have distinct functions in the nucleus independent from those in dendrites and spines. These findings show that naturally occurring genetic variants can profoundly alter normal protein function, impacting physiology in unexpected ways