Nov, 2021 - By SMI
A team of researchers at the Icahn School of Medicine, Mount Sinai designed a novel machine learning algorithm to identify toxic air pollutants in the U.S.
Asthma is a medical condition in which a person’s airways become inflamed and the person faces difficulty in breathing. It is one of the most prevalent diseases in children in the U.S. Thus, the researchers in this study considered 151 children who are affected by mild to severe forms of asthma. The researchers studied about the exposure of children to a number of toxic air pollutants which can be potentially linked to the disease. The study tried to find link between the early exposures of a child to mixtures of pollutants and asthma outcomes later in the child’s life. For this study, the researchers developed a list of air pollutants that a child might have been exposed to which can eventually lead to longer-term problems with asthma. They used a novel machine learning algorithm to identify whether individual chemicals may be linked to poor asthma outcomes later in life. It is important to note many chemicals in the mixtures were never linked to long-term asthma risk. The researchers observed that if a child is exposed to acrylic acid, then the risk of child’s requirement for daily asthma medication increases. It is quite alarming that combination of acrylic acid and other chemicals increases the hospitalization rates. Interestingly, the researchers observed that pollutants such as toluene and cobalt compounds when mixed with other compounds do not accelerate the disease nor increase the hospitalization rates.
The findings of the study reveal that how breathing individual and mixture of pollutants can result into asthma outcomes. The researchers stressed on the fact that a comprehensive and holistic view of air pollution can help the healthcare providers and children in their fight against asthma.
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