Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2). This study proposes a deep learning model based on the graph convolutional network (GCN) that enables, for the first time, the prediction of exogenous substances that affect the transcriptional expression of the ACE2 gene. It outperforms other machine learning models, achieving an area under receiver operating characteristic curve (AUROC) of 0.712 and 0.703 on the validation and internal test set, respectively. In addition, quantitative polymerase chain reaction (qPCR) experiments provided additional supporting evidence for indoor air pollutants identified by the GCN model. More broadly, the proposed methodology can be applied to predict the effect of environmental chemicals on the gene transcription of other virus receptors as well. In contrast to typical deep learning models that are of black box nature, we further highlight the interpretability of the proposed GCN model and how it facilitates deeper understanding of gene change at the structural level.

Disposable Polypropylene Face Masks: A Potential Source of Micro/Nanoparticles and Organic Contaminates in Humans.

We have been effectively protected by disposable propylene face masks during the COVID-19 pandemic; however, they may pose health risks due to the release of fine particles and chemicals. We measured micro/nanoparticles and organic chemicals in disposable medical masks, surgical masks, and (K)N95 respirators. In the breathing-simulation experiment, no notable differences were found in the total number of particles among mask types or between breathing intensities. However, when considering subranges, <2.5 µm particles accounted for ∼90% of the total number of micro/nanoparticles. GC-HRMS-based suspect screening tentatively revealed 79 (semi)volatile organic compounds in masks, with 18 being detected in ≥80% of samples and 44 in ≤20% of samples. Three synthetic phenolic antioxidants were quantified, and AO168 reached a median concentration of 2968 ng/g. By screening particles collected from bulk mask fabrics, we detected 18 chemicals, including four commonly detected in masks, suggesting chemical partition between the particles and the fabric fibers and chemical exposure via particle inhalation. These particles and chemicals are believed to originate from raw materials, intentionally and nonintentionally added substances in mask production, and their transformation products. This study highlights the need to study the long-term health risks associated with mask wearing and raises concerns over mask quality control.