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1.
Public Health Pract (Oxf) ; 7: 100481, 2024 Jun.
Article En | MEDLINE | ID: mdl-38419738

Objective: Exposure to tobacco smoke causes numerous health problems in children, and create burden on the population in terms of economy, morbidity and mortality. In order to protect the child from exposure to tobacco smoke in the outdoor environment, sufficient legislative enactments are available in Indian legislation. The objective of the present study is to investigate the fact that in absence of any specific laws stating about protection of children from exposure to tobacco smoke in indoor environment, whether outdoor related legislations are sufficient to protect children from exposureand to explore the scope for enforcement of both state and central laws in improving health of children in India. Study design: The study considered cross-sectional survey data of Demographic and Health Survey Data on India, National Family and Health Survey fourth round (NFHS-4) for the year 2015-16 on Indian children (below age of four). Methods: Both bivariate and multivariate logistic regression models were used to assess the impact of anti-smoking laws on the prevalence of acute respiratory infection (ARI) based on the place of residence, indoor tobacco smoke exposure and age of the child. Results: The results have shown an inclination of ARI among children in association with states having single law, rural area resident, exposure to indoor tobacco smoke and age of the child, both as independent or in combination are quite conspicuous, and are found to be underestimated. The logistic regression also revealed the influence of these factors both as independent and even in interaction with other. Conclusions: Legislative intervention through both at central (or national)and state levels through anti-smoking laws will decrease the indoor tobacco smoke exposure as a result ARI prevalence will also decrease among children in India.

2.
Front Neurosci ; 17: 1174951, 2023.
Article En | MEDLINE | ID: mdl-38033547

Background: Parkinson's disease (PD) is an increasingly common neurodegenerative condition, which causes movement dysfunction and a broad range of non-motor symptoms. There is no molecular or biochemical diagnosis test for PD. The miRNAs are a class of small non-coding RNAs and are extensively studied owing to their altered expression in pathological states and facile harvesting and analysis techniques. Methods: A total of 48 samples (16 each of PD, aged-matched, and young controls) were recruited. The small extracellular vesicles (sEVs) were isolated and validated using Western blot, transmission electron microscope, and nanoparticle tracking analysis. Small RNA isolation, library preparation, and small RNA sequencing followed by differential expression and targeted prediction of miRNA were performed. The real-time PCR was performed with the targeted miRNA on PD, age-matched, and young healthy control of plasma and plasma-derived sEVs to demonstrate their potential as a diagnostic biomarker. Results: In RNA sequencing, we identified 14.89% upregulated (fold change 1.11 to 11.04, p < 0.05) and 16.54% downregulated (fold change -1.04 to -7.28, p < 0.05) miRNAs in PD and controls. Four differentially expressed miRNAs (miR-23b-3p, miR-29a-3p, miR-19b-3p, and miR-150-3p) were selected. The expression of miR-23b-3p was "upregulated" (p = 0.002) in plasma, whereas "downregulated" (p = 0.0284) in plasma-derived sEVs in PD than age-matched controls. The ROC analysis of miR-23b-3p revealed better AUC values in plasma (AUC = 0.8086, p = 0.0029) and plasma-derived sEVs (AUC = 0.7278, p = 0.0483) of PD and age-matched controls. Conclusion: We observed an opposite expression profile of miR-23b-3p in PD and age-matched healthy control in plasma and plasma-derived sEV fractions, where the expression of miR-23b-3p is increased in PD plasma while decreased in plasma-derived sEV fractions. We further observed the different miR-23b-3p expression profiles in young and age-matched healthy control.

3.
J Eval Clin Pract ; 26(6): 1592-1598, 2020 12.
Article En | MEDLINE | ID: mdl-32970386

RATIONALE, AIMS, AND OBJECTIVES: The recent outbreak of coronavirus (COVID-19) has infected around 1 560 000 individuals till 10 April 2020, which has resulted in 95 000 deaths globally. While no vaccine or anti-viral drugs for COVID-19 are available, lockdown acts as a protective public health measures to reduce human interaction and lower transmission. The study aims to explore the impact of delayed planning or lack of planning for the lockdown and inadequate implementation of the lockdown, on the transmission rate of COVID-19. METHOD: Epidemiological data on the incidence and mortality of COVID-19 cases as reported by public health authorities were accessed from six countries based on total number of infected cases, namely, United States and Italy (more than 100 000 cases); United Kingdom, and France (50 000-100 000 cases), and India and Russia (6000-10 000 cases). The Bayesian inferential technique was used to observe the changes (three points) in pattern of number of cases on different duration of exposure (in days) in these selected countries 1 month after World Health Organization (WHO) declaration about COVID-19 as a global pandemic. RESULTS: On comparing the pattern of transmission rates observed in these six countries at posterior estimated change points, it is found that partial implementation of lockdown (in the United States), delayed planning in lockdown (Russia, United Kingdom, and France), and inadequate implementation of the lockdown (in India and Italy) were responsible to the spread of infections. CONCLUSIONS: In order to control the spreading of COVID-19, like other national and international laws, lockdown must be implemented and enforced. It is suggested that on-time or adequate implementation of lockdown is a step towards social distancing and to control the spread of this pandemic.


COVID-19/epidemiology , Global Health/statistics & numerical data , Quarantine/standards , Social Isolation , Bayes Theorem , COVID-19/prevention & control , Humans , Incidence , Models, Biological , Time Factors
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