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A land use regression model to predict emissions from oil and gas production using machine learning

Cao et al. | Mar 24, 2023

A land use regression model to predict emissions from oil and gas production using machine learning

Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO2), volatile organic compounds (VOCs), and ozone (O3) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO2, NOx, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.

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The Development of a Highly Sensitive Home Diagnosis Kit for Group A Streptococcus Bacteria (GAS)

Mai et al. | Dec 05, 2018

The Development of a Highly Sensitive Home Diagnosis Kit for Group A Streptococcus Bacteria (GAS)

In this article, Mai et al. have developed a do-it-yourself kit for the detection of Strep A bacterial infections. While Strep A infections require antibiotic administration, viral infections, which can present with similar symptoms, often resolve on their own. The problem with delayed antibiotic treatment is an increasing risk of complications. Currently an accurate diagnosis requires that patients make the trip to the hospital where sensitive tests can be performed. The method described here, bundled into a commercially available kit, could help speed up the identification of such bacterial infections. When presented with symptoms of a sore throat and fever, you could just buy the kit at your local pharmacy, perform the simple yet highly accurate and sensitive test, and know whether an urgent trip to the doctor's for an antibiotic prescription is necessary. How convenient!

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Developing anticholinergic drugs for the treatment of asthma with improved efficacy

Wong et al. | Jul 05, 2023

Developing anticholinergic drugs for the treatment of asthma with improved efficacy
Image credit: Wong et al.

Anticholinergics are used in treating asthma, a chronic inflammation of the airways. These drugs block human M1 and M2 muscarinic acetylcholine receptors, inhibiting bronchoconstriction. However, studies have reported complications of anticholinergic usage, such as exacerbated eosinophil production and worsened urinary retention. Modification of known anticholinergics using bioisosteric replacements to increase efficacy could potentially minimize these complications. The present study focuses on identifying viable analogs of anticholinergics to improve binding energy to the receptors compared to current treatment options. Glycopyrrolate (G), ipratropium (IB), and tiotropium bromide (TB) were chosen as parent drugs of interest, due to the presence of common functional groups within the molecules, specifically esters and alcohols. Docking score analysis via AutoDock Vina was used to evaluate the binding energy between drug analogs and the muscarinic acetylcholine receptors. The final results suggest that G-A3, IB-A3, and TB-A1 are the most viable analogs, as binding energy was improved when compared to the parent drug. G-A4, IB-A4, IB-A5, TB-A3, and TB-A4 are also potential candidates, although there were slight regressions in binding energy to both muscarinic receptors for these analogs. By researching the effects of bioisosteric replacements of current anticholinergics, it is evident that there is a potential to provide asthmatics with more effective treatment options.

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