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In the United States, patients with chronic conditions experience disparities in health outcomes across the care continuum. Among patients with multiple sclerosis, diabetic retinopathy, and lung cancer, there is a lack of evidence summarizing interventions to improve care and decrease these disparities. The aim of this rapid literature review was to identify interventions among patients with these chronic conditions to improve health and reduce disparities in screening, diagnosis, access to treatment and specialists, adherence, and retention in care. Using structured search terms in PubMed and Web of Science, we completed a rapid review of studies published in the prior five years conducted in the United States on our subject of focus. We screened the retrieved articles for inclusion and extracted data using a standard spreadsheet. The data were synthesized across clinical conditions and summarized. Screening was the most common point in the care continuum with documented interventions. Most studies we identified addressed interventions for patients with lung cancer, with half as many studies identified for patients with diabetic retinopathy, and few studies identified for patients with multiple sclerosis. Almost two-thirds of the studies focused on patients who identify as Black, Indigenous, or people of color. Interventions with evidence evaluating implementation in multiple conditions included telemedicine, mobile clinics, and insurance subsidies, or expansion. Despite documented disparities and a focus on health equity, a paucity of evidence exists on interventions that improve health outcomes among patients who are medically underserved with multiple sclerosis, diabetic retinopathy, and lung cancer.

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The past decade marked a drastic increase in the usage of electronic cigarettes. The adverse health impact of secondhand exposure due to exhaled e-cig particles has raised significant concerns, demanding further research on the characteristics of these particles. In this work, we report direct volatility measurements on exhaled e-cig aerosols using a field-portable device (termed c-Air) enabled by deep learning and lens-free holographic microscopy; for this analysis, we performed a series of field experiments in a vape shop where customers used/vaped their e-cig products. During four days of experiments, we periodically sampled the indoor air with intervals of ~ 16 min and collected the exhaled particles with c-Air. Time-lapse inline holograms of the collected particles were recorded by c-Air and reconstructed using a convolutional neural network yielding phase-recovered microscopic images of the particles. Volumetric decay of individual particles due to evaporation was used as an indicator of the volatility of each aerosol. Volatility dynamics quantified through c-Air experiments showed that indoor vaping increased the percentage of volatile and semi-volatile particles in air. The reported methodology and findings can guide further studies on volatility characterization of indoor e-cig emissions.

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With the rapid growth of the electronic cigarette (e-cig) market, there is an increasing number of vape shops that exclusively sell e-cigs. The use of e-cigs in the vape shop is a primary source of indoor particles, which might transport to its nearby indoor spaces in the multiunit setting. In this study, six pairs of vape shops and neighboring businesses in Southern California were recruited for real-time measurements of particulate pollutants between February 2017 and October 2019. The mean (SD) particle number concentration (PNC) and PM2.5 concentration in the studied vape shops were 2.8 × 104 (2.3 × 104) particles/cm3 and 276 (546) µg/m3, which were substantially higher than those in neighboring businesses and outdoor areas. In addition, 24-h time-weighted average (TWA) nicotine sampling was conducted in the six pairs and three additional pairs. Nicotine was detected in the air of all the studied vape shops and neighboring businesses, in which the mean (SD) concentration was 2.59 (1.02) and 0.17 (0.13) µg/m3, respectively. Inside vape shops, the dilution-corrected vaping density (puffs/h/100 m3) is a strong predictor of the particle concentration, and nicotine concentration highly depends on the air exchange rate (AER). Out of the six studied pairs, PNCs in five vape shops and PM2.5 in two vape shops were significantly correlated with those in their neighboring businesses. This correlation was stronger when the door of the vape shop was closed. When the door was open, environmental electronic vaping (EEV) aerosols, especially smaller particles, could transport from the vape shop to the outdoor environment. Overall, e-cig usage in the vape shop impacts both its own and nearby air quality, raising concerns regarding the risk of exposure to EEV aerosols in the surrounding environments.

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An electronic cigarette (e-cig) generates aerosols by vaporizing the e-liquid, which mainly consists of propylene glycol (PG), vegetable glycerin (VG), and nicotine. Understanding the effects of e-liquid main compositions on e-cig aerosols is important for exposure assessment. This study investigated how the PG/VG ratio and nicotine content affect e-cig aerosol emissions and dynamics. A tank-based e-cig device with 10 different flavorless e-liquid mixtures (e.g., PG/VG ratios of 0/100, 10/90, 30/70, 50/50, and 100/0 with 0.0% or 2.4% nicotine) was used to puff aerosols into a 0.46 m3 stainless steel chamber for 0.5 h. Real-time measurements of particle number concentration (PNC), fine particulate matter (PM2.5), and particle size distributions were conducted continuously throughout the puffing and the following 2-h decay period. During the decay period, particle loss rates were determined by a first-order log-linear regression and used to calculate the emission factor. The addition of nicotine in the e-liquid significantly decreased the particle number emission factor by 33%. The PM2.5 emission factor significantly decreased with greater PG content in the e-liquid. For nicotine-free e-liquids, increasing the PG/VG ratio resulted in increased particle loss rates measured by PNC and PM2.5. This pattern was not observed with nicotine in the e-liquids. The particle loss rates, however, were significantly different with and without nicotine especially when the PG/VG ratios were greater than 30/70. Compared with nonvolatile diethyl-hexyl subacute (DEHS) aerosols, e-cig particle concentration decayed faster inside the chamber, presumably due to evaporation. These results have potential implications for assessing human exposure to e-cig aerosols.

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Organismal chemical tolerance is often used to assess ecological risk and monitor water quality, yet tolerance can differ between field- and lab-raised organisms. In this study, we examined how tolerance to copper (Cu) and tributyltin oxide (TBTO) in two species of marine copepods, Tigriopus japonicus and T. californicus, changed across generations under benign laboratory culture (in the absence of pre-exposure to chemicals). Both copepod species exhibited similar chemical-specific changes in tolerance, with laboratory maintenance resulting in increased Cu tolerance and decreased TBTO tolerance. To assess potential factors underlying these patterns, chemical tolerance was measured in conjunction with candidate environmental variables (temperature, UV radiation, diet type, and starvation). The largest chemical-specific effect was found for starvation, which decreased TBTO tolerance but had no effect on Cu tolerance. Understanding how chemical-specific tolerance can change in the laboratory will be critical in strengthening bioassays and their applications for environmental protection and chemical management.

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Due to its cytotoxicity, genotoxicity, and adipogenicity observed in in vitro studies, bisphenol A diglycidyl ether (BADGE) may pose a health risk to humans. Quantifying BADGE exposure is an essential step to assess potential health risks associated with this ubiquitous compound widely used in certain plastic products. Due to the lack of endogenous sources for BADGE, bio-monitoring of BADGE and/or its hydrolytic metabolites (BADGE·H2O and BADGE·2H2O) can be a useful means to measure exposure. In this study, we developed a highly specific and sensitive method to measure BADGE, BADGE·H2O and BADGE·2H2O in plasma and urine, using a fast liquid-liquid extraction technique followed by a high-performance liquid chromatography and positive electrospray tandem mass spectrometry (LC-ESI-MS/MS) method. The method can quantify BADGE, BADGE·H2O and BADGE·2H2O with lower limits of quantification (LLOQ) of 0.05, 0.05 and 0.2 ng/ml, respectively. The percentage deviation of mean calculated concentrations from target concentrations was within 20%, variations across repeated analyses were within 15%, and mean extraction recovery was higher than 51.4% for all the three analytes in both plasma and urine matrices. The method has been applied to venous blood samples, cord blood samples, and urine samples collected from 9 to 14 adult volunteers. Results showed that concentrations of BADGE were lower than LLOQ in all of these samples except one urine sample. Low levels of BADGE·H2O from 0.108 to 0.222 ng/ml were observed in four venous blood samples and one urine sample (0.187 ng/ml). In contrast, concentrations of BADGE·2H2O were higher than LLOQ, varying from 0.660 to 303.593 ng/ml, in all the 10 venous blood samples and 1 cord blood sample (0.592 ng/ml) and two urine samples (0.200 and 0.306 ng/ml). The results suggest that bio-monitoring of blood and urine for BADGE exposure should focus on the hydrolysis derivatives of BADGE, mainly in the form of BADGE·2H2O.

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