Use of Portable Air Cleaners in Washington State Schools: A Qualitative Analysis Based on the Technology Acceptance Model.

BACKGROUND: The US government allocated over $2.5 billion in "Elementary and Secondary School Emergency Relief (ESSER)" funds to Washington State for COVID-19 response and ventilation improvements. Despite available funding, gaps persist in supporting schools to successfully use portable air cleaners (PACs). We evaluated PAC needs within King County, Washington and characterized factors influencing schools' purchase and use of PACs. METHODS: Public Health-Seattle & King County (PHSKC) assessed school's ventilation systems and IAQ improvements through a survey (N = 17). Separately, semi-structured interviews (N = 13) based on the technology acceptance model (TAM) were conducted with school personnel. A thematic analysis using inductive and deductive coding was conducted and logistic regression models assessed the predictive capability of the TAM. RESULTS: The PHSKC survey findings informed our recommendations. Positive attitudes, knowledge, and beliefs in ease of use and effectiveness of PACs were facilitators to PAC use. While barriers included a lack of training, education, and concerns about PAC maintenance and sustainability. TAM constructs of perceived usefulness (PU) and perceived ease of use (PEU) were predictive of having the intention to use PACs in schools. CONCLUSIONS: There is a critical need for solutions to circumvent challenges to implementing PACs in schools. This characterization provides insight for promoting PAC use in IAQ-impacted schools.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

BACKGROUND: Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. OBJECTIVE: Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. METHODS: We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. RESULTS: The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- R 2 = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and R 2 = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- R 2 = 0.51 (with LCS). IMPACT: We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection.

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM2.5 in epidemiological studies. Using sparse regulatory PM2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort's residential locations). In this study, we used the PM2.5 measurements from a publicly available commercial low-cost PM2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 "gold-standard" monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R2 and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 µg/m3 to 0.92 and 1.63 µg/m3, respectively. The external spatial validation R2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 µg/m3 to 0.79 and 0.88 µg/m3, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model's prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

Field measurements of PM2.5 infiltration factor and portable air cleaner effectiveness during wildfire episodes in US residences.

Wildfires have frequently occurred in the western United States (US) during the summer and fall seasons in recent years. This study measures the PM2.5 infiltration factor in seven residences recruited from five dense communities in Seattle, Washington, during a 2020 wildfire episode and evaluates the impacts of HEPA-based portable air cleaner (PAC) use on reducing indoor PM2.5 levels. All residences with windows closed went through an 18-to-24-h no filtration session, with five of seven following that period with an 18-to-24-h filtration session. Auto-mode PACs, which automatically adjust the fan speed based on the surrounding PM2.5 levels, were used for the filtration session. 10-s resolved indoor PM2.5 levels were measured in each residence's living room, while hourly outdoor levels were collected from the nearest governmental air quality monitoring station to each residence. Additionally, a time-activity diary in minute resolution was collected from each household. With the impacts of indoor sources excluded, indoor PM2.5 mass balance models were developed to estimate the PM2.5 indoor/outdoor (I/O) ratios, PAC effectiveness, and decay-related parameters. Among the seven residences, the mean infiltration factor ranged from 0.33 (standard deviation [SD]: 0.06) to 0.76 (SD: 0.05). The use of auto-mode PAC led to a 48%-78% decrease of indoor PM2.5 levels after adjusting for outdoor PM2.5 levels and indoor sources. The mean (SD) air exchange rates ranged from 0.30 (0.13) h-1 to 1.41 (3.18) h-1 while the PM2.5 deposition rate ranged from 0.10 (0.54) h-1 to 0.49 (0.47) h-1. These findings suggest that staying indoors, a common protective measure during wildfire episodes, is insufficient to prevent people's excess exposure to wildfire smoke, and provides quantitative evidence to support the utilization of auto-mode PACs during wildfire events in the US.

Virion-incorporated PSGL-1 and CD43 inhibit both cell-free infection and transinfection of HIV-1 by preventing virus-cell binding.

HIV-1 particles incorporate various host transmembrane proteins in addition to viral Env glycoprotein during assembly at the plasma membrane. In polarized T cells, HIV-1 structural protein Gag localizes to the plasma membrane of uropod, a rear-end protrusion. Notably, uropod transmembrane proteins PSGL-1 and CD43 cocluster specifically with Gag assembling at the plasma membrane even in cells that do not form uropods. Recent reports have shown that expression of either PSGL-1 or CD43 in virus-producing cells reduces the infectivity of progeny virions and that HIV-1 infection reduces the cell surface expression of these proteins. However, the mechanisms for both processes remain to be determined. In this study, we found that virion incorporation of PSGL-1 and CD43 closely correlates with diminished virion infectivity. PSGL-1 and CD43 inhibited virus attachment to CD4+ cells irrespective of the presence of Env. These proteins also inhibited virion attachment to CD4- lymphoid organ fibroblastic reticular cells that mediate transinfection of CD4+ T cells. Consistent with the possibility that highly extended extracellular domains of these proteins physically block virus-cell attachment, the inhibitory effect of PSGL-1 required its full-length ectodomain. HIV-1 encoding Gag mutants that are defective in either coclustering with these host proteins or ESCRT-dependent particle release failed to reduce PSGL-1 on surface of infected cells. This study reveals an anti-HIV-1 mechanism that suppresses virus-cell attachment and a previously unappreciated process of HIV-1-mediated down-regulation of host antiviral proteins, both of which likely require virion incorporation of these proteins.

Beyond "Asian": Specific East and Southeast Asian Races or Ethnicities Associated With Jaundice Readmission.

OBJECTIVES: Clinical practice guidelines have recognized "Asian" and "East Asian" as risk factors for newborn jaundice and readmission. We sought to identify more detailed and specific, parent-identified races or ethnicities associated with jaundice readmission. METHODS: We conducted a case control study of 653 newborn infants born (2014-2016) at a West-Coast, urban hospital to examine specific parent-described races or ethnicities that are associated with newborn hospital readmissions for hyperbilirubinemia. Parent-reported race or ethnicity was abstracted from the California Newborn Screening Test. RESULTS: Our sample included 105 infants readmitted for jaundice (cases) and 548 infants as controls. In the full cohort, 66 infants (10.1%) were Coombs positive, 39 infants (6.0%) were born before 37 weeks' gestational age, and 405 infants (62.0%) were born to first-time mothers. The parents described the 653 infants using 45 unique races and ethnicities. In a multivariable model that controlled for Coombs positivity, gestational age <37 weeks, and primiparity, infants described as "Far East Asian" (odds ratio [OR] = 3.17; 95% confidence interval [CI] = 1.94-5.18) or "Southeast Asian" (OR = 3.17; 95% CI = 1.66-6.08) had increased risk for jaundice readmission. Infants described as Southeast Asian (eg, Laotian, Cambodian, Indonesian, Vietnamese, and Filipino) and Far East Asian (eg, Chinese, Korean, Taiwanese, Japanese, and Mongolian) had an increased risk of readmission. Finally, we did not find an association between South Asian (OR = 0.79; 95% CI = 0.33-1.92) race or ethnicity and risk of jaundice readmission. CONCLUSIONS: In this study, we help clarify and move beyond the term "Asian" as a risk factor for readmission due to hyperbilirubinemia.

An uncommon inheritance pattern in Niemann-Pick disease type C: identification of probable paternal germline mosaicism in a Mexican family.

BACKGROUND: Niemann-Pick disease type C (NP-C) is a fatal lysosomal neurodegenerative and neurovisceral disease. It is caused by defects in intracellular lipid trafficking, which lead to the accumulation of lipids and glycosphingolipids within the endosomes and lysosomes of affected individuals. Pathogenic variants of the NPC1 or NPC2 genes yield highly variable phenotypes with a time course that ranges from fetal onset (i.e., hydrops fetalis) to progressive dementia in adults. NP-C is typically inherited in an autosomal-recessive manner. To our knowledge, no previous report has identified germline mosaicism as an inheritance mechanism in NP-C. CASE PRESENTATION: We report the case of a male Mexican patient with "variant" filipin staining and a juvenile form of NP-C attributed to compound heterozygosity for two previously reported pathogenic variants of NPC1: c.[1042C>T];[2780C>T] or p.[Arg348*];[Ala927Val]. The proband's mother and healthy sister were heterozygous carriers of the c.2780C > T (exon 18) and c.1042C > T (exon 8) variants, respectively. However, direct sequencing of exons 8 and 18 of NPC1 revealed no mutation in genomic DNA obtained from the father's peripheral blood. DNA profiling ruled out the possibility of non-paternity. We were unable to obtain a sperm sample to demonstrate paternal gonadal mosaicism. NPC1 haplotype analysis using 20 linked single nucleotide variants failed to yield sufficient information to document a p.(Arg348*) NPC1 pathogenic variant-associated haplotype in the family. CONCLUSIONS: We propose that this case of NP-C involves paternal germline mosaicism. To the best of our knowledge, this has not previously been reported in NP-C.