Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

Phytoremediation potential of indoor plants in reducing air pollutants

Indoor air quality (IAQ), specifically after the COVID-19 pandemic, has become an international issue, as humans spend 80–90% of their time in indoor microenvironments. Poor IAQ has been related to the sick-building syndrome, nasal and ocular irritations, allergies, and respiratory dysfunction, including premature deaths. Phytoremediation is a novel strategy to absorb, adsorb, assimilate or transfer/reduce air pollutants and improve the IAQ using plants. Hence, the current review aims to explore indoor plants' role in improving indoor air quality, including their purification capabilities. There is increasing evidence that various plant species (e.g., Ficus benjamina, Chlorophytum comosum, Draceana) or their parts can reliably reduce the concentration of numerous air pollutants in the indoor microenvironment and promote human wellbeing. However, the indoor air pollutants removal efficiency depends on the species of plant, various plant characteristics such as leaf size, thickness, area, photosynthetic activity, light intensity and part of plant involved, i.e., roots, leaves, wax, cuticle and stomata. Using indoor plants is one of the most cost-effective and reliable methods of making a healthier indoor environment. Better public health can be maintained at a lower cost, with less strain on the health care system, if more emphasis is placed on creating a biophilic atmosphere and increasing the use of indoor plants. However, there are no established criteria for the best indoor plants and the impact of indoor plants on various factors such as interior ventilation, temperature, humidity, etc. Therefore, further experimental research is needed that simulates the interior environment to monitor the impacts of indoor plants on factors such as humidity, temperature, ventilation, etc., in improving the microenvironment of a closed space/room. Copyright © 2022 Ravindra and Mor.

Worldwide spread of COVID-19 pandemic and risk factors among co-morbid conditions especially diabetes mellitus in India

This narrative review aims to know the association of the novel corona virus disease (COVID-19) in patients with diabetes mellitus and other co-morbidities. COVID 19 has reached the number 2.5 million of cases according to the situation report of World Health Organization on 21st, April 2020, affecting nearly 210 countries worldwide. It's highly virulent, involves ‘flu-like symptoms’, which are mild in most cases, in severe condition, results into acute respiratory distress syndrome, multiple organ failure leading to the blockage in lower respiratory tract/pathways and difficulty in breathing, it increases the risk for hospitalization and sometimes, leads to death in COVID-19 patients. Most of the patients are asymptomatic, but they can be a carrier to those who have less immunity. Patient with other co-morbidities like diabetes, cardiac problems, and older ages fails to survive as they are more prone to virus infections. The additional burden of disease can be one of the factors to the mortality in these cases.