MicroRNA-122-3p plays as the target of long non-coding RNA LINC00665 in repressing the progress of arthritis

MicroRNAs (miRNAs) play important roles in many diseases, including rheumatoid arthritis (RA). However, the mechanisms underlying the effects of miR-122-3p-3p on RA are not distinct and require further investigation. Patients with RA and healthy controls were recruited to analyze the miR-122-3p levels. The MH7A cells were stimulated with interleukin (IL)-1β to mimic the local inflammation of RA. Cell Counting Kit-8 (CCK-8) and flow cytometry were performed to measure the viability and apoptosis of MH7A cells. Diana tools and TargetScan were used to predict the target relationships. Luciferase reporter assay was used to validate the target relationship. miR-122-3p is downregulated in RA patients and IL-1β-stimulated MH7A cells. miR-122-3p suppresses MH7A cell viability and promotes MH7A cell apoptosis. miR-122-3p targets LINC00665. LINC00665 eliminates the inhibitory effect of miR-122-3p on IL-1β-stimulated MH7A cells. Eukaryotic translation initiation factor 2 alpha kinase 1 (EIF2AK1) targets miR-122-3p. In addition, EIF2AK1 is highly expressed in patients with RA. In addition, EIF2AK1 activates the mTOR signaling pathway. miR-122-3p represses RA progression by reducing cell viability and increasing synoviocyte apoptosis.</p

Water uptake and optical properties of mixed organic-inorganic particles

Atmospheric aerosol particles are frequently mixtures of inorganic and organic species, both of which can contribute to aerosol water uptake and determine the particles’ ability to scatter and absorb light. While water uptake of purely inorganic aerosol is well represented in current regional and global chemical transport models, it is challenging to represent it for particles that are mixtures of organic and inorganic species. Here we quantified the growth factor for aerosols that consist of mixed organic-inorganic particles using an accurate lattice-based adsorption isotherm model (Ad-iso) as a benchmark. We then determined the error in the growth factor and resulting optical properties for simplifying assumptions that are commonly made in current chemical transport models. The systems studied here are representative of ambient atmospheric aerosols, consisting of model water-soluble inorganic-organic mixtures, with and without a core of absorbing black carbon, under conditions of relative humidity larger than 85%. The assumption of completely neglecting the water uptake by organic components, for particles with an organic mass fraction of 50%, led to errors of up to 7% in growth factor and up to 3.5% in single scattering albedo. Larger errors occurred for larger organic mass fractions. Approximating the organic water uptake with a constant hygroscopicity parameter, for organic mass fractions between 45 and 65%, the errors remained within 3% for the growth factor and 0.6% for the single scattering albedo. For organic mass fractions smaller than 45% or larger than 65%, the errors increased up to 6% for the single scattering albedo. The magnitudes of these errors underscore the importance of considering organic/inorganic mixtures for estimating direct aerosol radiative forcing. Copyright © 2021 American Association for Aerosol Research</p

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM_2.5 Monitoring in Accra, Ghana

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow