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Cognition capacity is essentially age-dependent and it is associated with the overall well-being of an individual. The public health aspects of cognitive research primarily focus on the possible delaying of cognitive decline among the older adult population. In this context, using the most recent round of the Longitudinal Ageing Study in India, 2017–2018 data, this study examines the cognition capacity among older adults aged 45 and above subject to their nutritional health and health behaviour (tobacco and alcohol consumption). It is observed that almost one in every tenth individual (10%) above 45 years of age in India shows low cognition scores. Low cognition is much more prevalent among 60 + females than males. Around one-fifth of the underweight older adults (18%) demonstrate low cognition capacity among them. Of those older adults who consume only tobacco, 11% of them demonstrate low cognition than the rest. The partial proportional odds model estimation shows that older adults are at higher risk of developing low cognition with increasing age and beyond age 65, the individuals carry a critically higher risk to experience low cognition. The estimation also shows that with increasing age older adults are higher likely to experience poor cognition independent of nutritional status, but underweight older adults are comparatively more likely to experience low cognition followed by normal and overweight older adults. In terms of alcohol-tobacco consumption behaviour, older adults who consume both are more likely to experience low cognition with increasing age followed by ‘only alcohol consumers’, and ‘only tobacco consumers’.

According to the International Diabetes Federation-2019 estimates, India is home to 77 million diabetic individuals which is projected to grow up to 147.2 million by 2045. Diabetes being a progressive health disorder leads to multiple morbidities and complications including eye diseases and visual impairments. As the burden of diabetes mellitus is increasing, eye problems like cataracts and glaucoma are commonly cited problems among the older adults. In this context, this study aims to provide the public health evidences on diabetes associated burden and risk of developing cataracts and glaucoma among older adults aged 60 and above in India. The analytical sample of this cross-sectional study comprised of 31,464 individuals aged 60 and above. Bivariate cross-tabulation and chi-square test were performed to understand the differential in the prevalence of cataracts and glaucoma by diabetes mellitus including the socio-economic and demographic characteristics of the individuals. Binary logistic regression estimation was executed to estimate the adjusted odds ratio for each of the outcome variables within a multivariate framework. The cataract problem affects more than one-fifth of the older people, while glaucoma affects 2% of them. The prevalence of cataract and glaucoma is 29% among diabetic older adults compared to 22% among non-diabetic persons. In terms of gender, the cataract prevalence is comparatively higher among females (25%) than males (21%). It is important to note that while adjusting for socio-economic and demographic characteristics, the likelihood of cataract (AOR 1.495; p-value < 0.01) and glaucoma (AOR 1.554; p-value < 0.01) is significantly higher among older adults with diabetes than among their counterparts. Medical practitioners should conduct prognosis for diabetic eye problems among patients and raise awareness about the potential risks of developing vision loss, such as cataracts and glaucoma, which are more prevalent among individuals with diabetes.

The public health burden of nutritional deficiency and child mortality is the major challenge India is facing upfront. In this context, using National Family Health Survey, 2015–16 data, this study estimated rate of composite index of anthropometric failure (CIAF) among Indian children by their population characteristics, across states and examined the multilevel contextual determinants. We further investigated district level burden of infant and child mortality in terms of multiple anthropometric failure prevalence across India. The multilevel analysis confirms a significant state, district and PSU level variation in the prevalence of anthropometric failures. Factors like- place of residence, household’s economic wellbeing, mother’s educational attainment, age, immunization status and drinking water significantly determine the different forms of multiple anthropometric failures. Wealth status of the household and mother’s educational status show a clear gradient in terms of the estimated odds ratios. The district level estimation of infant and child mortality demonstrates that districts with higher burden of multiple anthropometric failures show elevated risk of infant and child mortality. Unlike previous studies, this study does not use the conventional indices, instead considered the CIAF to identify the exact and severe form of undernutrition among Indian children and the associated nexus with infant and child mortality at the district level.

This work deals with the flow and heat transfer in upper-convected Maxwell fluid above an exponentially stretching surface. Cattaneo-Christov heat flux model is employed for the formulation of the energy equation. This model can predict the effects of thermal relaxation time on the boundary layer. Similarity approach is utilized to normalize the governing boundary layer equations. Local similarity solutions are achieved by shooting approach together with fourth-fifth-order Runge-Kutta integration technique and Newton's method. Our computations reveal that fluid temperature has inverse relationship with the thermal relaxation time. Further the fluid velocity is a decreasing function of the fluid relaxation time. A comparison of Fourier's law and the Cattaneo-Christov's law is also presented. Present attempt even in the case of Newtonian fluid is not yet available in the literature.

Background Despite sustained economic growth and reduction in money metric poverty in last two decades, prevalence of malnutrition remained high in India. During 1992–2016, the prevalence of underweight among children had declined from 53% to 36%, stunting had declined from 52% to 38% while that of wasting had increased from 17% to 21% in India. The national average in the level of malnutrition conceals large variation across districts of India. Using data from the recent round of National Family Health Survey (NFHS), 2015–16 this paper examined the spatial heterogeneity and meso-scale correlates of child malnutrition across 640 districts of India. Methods Moran’s I statistics and bivariate LISA maps were used to understand spatial dependence and clustering of child malnutrition. Multiple regression, spatial lag and error models were used to examine the correlates of malnutrition. Poverty, body mass index (BMI) of mother, breastfeeding practices, full immunization, institutional births, improved sanitation and electrification in the household were used as meso scale correlates of malnutrition. Results The univariate Moran’s I statistics was 0.65, 0.51 and 0.74 for stunting, wasting and underweight respectively suggesting spatial heterogeneity of malnutrition in India. Bivariate Moran’s I statistics of stunting with BMI of mother was 0.52, 0.46 with poverty and − 0.52 with sanitation. The pattern was similar with respect to wasting and underweight suggesting spatial clustering of malnutrition against the meso scale correlates in the geographical hotspots of India. Results of spatial error model suggested that the coefficient of BMI of mother and poverty of household were strong and significant predictors of stunting, wasting and underweight. The coefficient of BMI in spatial error model was largest found for underweight (β = 0.38, 95% CI: 0.29–0.48) followed by stunting (β = 0.23, 95% CI: 0.14–0.33) and wasting (β = 0.11, 95% CI: 0.01–0.22). Women’s educational attainment and breastfeeding practices were also found significant for stunting and underweight. Conclusion Malnutrition across the districts of India is spatially clustered. Reduction of poverty, improving women’s education and health, sanitation and child feeding knowledge can reduce the prevalence of malnutrition across India. Multisectoral and targeted intervention in the geographical hotspots of malnutrition can reduce malnutrition in India.

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the nervous system that primarily affects young adults. Although the exact etiology of the disease remains obscure, it is clear that alterations in the metabolome contribute to this process. As such, defining a reliable and disease-specific metabolome has tremendous potential as a diagnostic and therapeutic strategy for MS. Here, we provide an overview of studies aimed at identifying the role of metabolomics in MS. These offer new insights into disease pathophysiology and the contributions of metabolic pathways to this process, identify unique markers indicative of treatment responses, and demonstrate the therapeutic effects of drug-like metabolites in cellular and animal models of MS. By and large, the commonly perturbed pathways in MS and its preclinical model include lipid metabolism involving alpha-linoleic acid pathway, nucleotide metabolism, amino acid metabolism, tricarboxylic acid cycle, d-ornithine and d-arginine pathways with collective role in signaling and energy supply. The metabolomics studies suggest that metabolic profiling of MS patient samples may uncover biomarkers that will advance our understanding of disease pathogenesis and progression, reduce delays and mistakes in diagnosis, monitor the course of disease, and detect better drug targets, all of which will improve early therapeutic interventions and improve evaluation of response to these treatments.

Individual's early life style and health behaviors are directly linked to chronic non-communicable diseases. Considering the increased burden of NCDs during the last two decades, the aim of this study is to assess co-occurrence/clustering of lifestyle risk factors and its association with different socio-demographic and economic characteristics among adult men and women in India from 2005-2016. This study utilized the data from the National Family Health Survey 2005-06 and 2015-16 survey rounds. Multinomial logistic regression is employed to evaluate co-occurrence of multiple risk factors among adult men and women of different socio-economic and demographic characteristics to identify the subgroups with elevated risk of clustering of multiple unhealthy lifestyle risk factors. More adult men in India tend to exhibit clustering of multiple non-communicable disease risk factors than females. Individuals between 30-49 years of age, residing in urban areas, the population with no education, separated couples and those from poor economic strata are the specific population subgroups show higher prevalence of co-occurrence of multiple risk factors. The regional pattern of clustering of risk factors shows that the prevalence of co-occurrence of multiple risk factors is higher among men and women from the North-Eastern part of India compared to the other regions of the country. The prevalence of clustering of multiple risk factors associated with chronic NCDs is substantially high and has increased between 2005-06 to 2015-16. India may therefore experience a significant increase in the burden of chronic non-communicable diseases in the coming years. We therefore conclude that appropriate strategies should be implemented by policy makers and the government to reduce the overall health burden of NCDs due to lifestyle habits.

Almost two-fifth of the children in India is stunted and among various factors, poverty differential in child undernutrition is the largest. Using the latest population-based survey of National Family Health Survey, 2015-16 and 2019-21 this paper examined the poverty induced inequality in child stunting across the sub-populations of India. A sample of 213,136 children aged between 0-5 years from NFHS fourth round and 98,222 children in the same age group from the NFHS fifth round constitute the study sample. The wealth index is used as the proxy of household's economic wellbeing and height-for-age (HAZ) z-score of a child is used to identify the stunting status of the child. Box plots are drawn to understand the distributional characteristics of the HAZ score for both the study sample. We calculate the Erreygers corrected concentration index and decomposed the concentration indices using Gonzalo-Almorox and Urbanos-Garrido method. During 2015-16, more than half of the children from the poorest wealth quintile were stunted (52%), compared to 22% among the children from richest wealth quintile. In 2015-16, stunting was as high as 65% among the children of mothers with low stature (height less than 145 cm) and from the poorest wealth quintile whereas, the prevalence was observed 56% from the same sub-population during 2019-21. Among various factors, the concentration index of stunting was observed highest among the children of 36-47 months (-0.28) followed by children of age 48-59 months (-0.27) and among the fully immunized children (-0.25). Similar to NFHS-4, NFHS-5 also shows a predominantly higher socio-economic inequality among 24+ months children and among the fully immunised children. Factors like child age, birth order and sanitation showed positive elasticity. Decomposition analysis of NFHS-4 data shows that due to uneven distribution of wealth, mother's education as a determinant of child stunting solely explained 33% of the overall inequality followed by improved access to sanitation (24%), mother's height (8%) and place of residence (5%). Similar to NFHS-4, NFHS-5 data also shows that mother's education, sanitation, mother's height and place of residence predominantly contributes to the overall wealth inequality in child stunting. In India, poverty differential in child undernutrition is acute among the different sub-population of children. And the concentration of stunted children is higher among the different sub-population with higher wealth poverty. Mother's education, improved sanitation and mother's height explained larger variation in the overall inequalities in child nutrition across India.

The alpha–beta filter algorithm has been widely researched for various applications, for example, navigation and target tracking systems. To improve the dynamic performance of the alpha–beta filter algorithm, a new prediction learning model is proposed in this study. The proposed model has two main components: (1) the alpha–beta filter algorithm is the main prediction module, and (2) the learning module is a feedforward artificial neural network (FF‐ANN). Furthermore, the model uses two inputs, temperature sensor and humidity sensor data, and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings. Using the novel proposed technique, prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network, and also reduces the root mean square error (RMSE) and mean absolute error (MAE). We carried out different experiments with different experimental setups. The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter. A higher prediction accuracy was achieved, and the MAE and RMSE were 35.1%–38.2% respectively. The final proposed model results show increased performance when compared to traditional methods.