Air pollution is a sustained problem of public health for the general population in urban areas, especially for those living in areas of intensive traffic or industrial activity. Accumulating evidence has confirmed a significant association between exposure to fine ambient particulate matter with aerodynamic diameters <2.5 μm (PM_2.5) and the increase of morbidity and mortality associated with cardiovascular and metabolic diseases. It has been identified that inflammation and intracellular stress responses play important roles in PM_2.5-caused pathogenesis. Unfolded protein response (UPR) is an intracellular stress signaling from the endoplasmic reticulum (ER) to help cell recovery from the stress caused by the accumulation of unfolded or misfolded proteins. Exposure to high levels of environmentally relevant PM_2.5 may directly or indirectly interrupt the protein folding process in the ER, causing ER stress. A number of studies suggested that ER stress response, or UPR, interacts with mitochondrial stress and inflammatory responses, under PM_2.5 exposure, to modulate functions and survival of specialized cell types that are involved in the development of cardiovascular, metabolic, and neurodegenerative diseases. In this review, we summarize the recent advance in understanding the mechanistic links between PM_2.5 and ER stress response.

Aim: The present study explores the effects of high population density (PD), climatic and environmental factors on transmission of coronavirus disease 2019 (COVID-19) in selected metropolitan cities of India. Materials and Methods: A data extraction sheet has been prepared to summarize the data of confirmed severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) cases and number of deaths in ten metropolitan cities, which was taken from Government of India website. The data on environmental factors of each selected metropolitan city were compiled from the official website and climatic conditions from Meteorological Department Government of India. Results: In India, maximum positive COVID-19 cases (>32%) has been found in tropical wet and dry climate zone. While the incidence of COVID-19 cases has been found less in the arid zone of India. Poor correlation has been found between level of Vitamin D, total COVID-19 cases, and mortalities in the studied metropolitan cities. No significant correlation was found between the health care index and COVID-19 cases and mortality. Conclusions: Correspondence and principal component analysis statistics showed high PD, poverty, climatic and environmental factors influenced the SARS-CoV-2 transmission in metropolitan cities of India.

Background: Chemical intolerance (CI) is characterized by multisystem symptoms initiated by a one-time high-dose or a persistent low-dose exposure to environmental toxicants. Prior studies have investigated symptom clusters rather than defined comorbid disease clusters. We use a latent class modeling approach to determine the number and type of comorbid disease clusters associated with CI. Methods: Two hundred respondents with and without CI were recruited to complete the Quick Environmental Exposure and Sensitivity Inventory (QEESI), and a 17-item comorbid disease checklist. A logistic regression model was used to predict the odds of comorbid disease conditions between groups. A latent class analysis was used to inspect the pattern of dichotomous item responses from the 17 comorbid diseases. Results: Those with the highest QEESI scores had significantly greater probability of each comorbid disease compared to the lowest scoring individuals (P < 0.0001). Three latent class disease clusters were found. Class 1 (17% of the sample) was characterized by a cluster consisting of irritable bowel syndrome (IBS), arthritis, depression, anxiety, fibromyalgia, and chronic fatigue. The second class (53% of the sample) was characterized by a low probability of any of the co-morbid diseases. The third class (30% of the sample) was characterized only by allergy. Discussion: We have demonstrated that several salient comorbid diseases form a unique statistical cluster among a subset of individuals with CI. Understanding these disease clusters may help physicians and other health care workers to gain a better understanding of individuals with CI. As such, assessing their patients for CI may help identify the salient initiators and triggers of their CI symptoms—therefore guide potential treatment efforts.