Investigating the relationship between extreme weather and cryptosporidiosis and giardiasis in Colorado: A multi-decade study using distributed-lag nonlinear models.

Environmentally-mediated protozoan diseases like cryptosporidiosis and giardiasis are likely to be highly impacted by extreme weather, as climate-related conditions like temperature and precipitation have been linked to their survival, distribution, and overall transmission success. Our aim was to investigate the relationship between extreme temperature and precipitation and cryptosporidiosis and giardiasis infection using monthly weather data and case reports from Colorado counties over a twenty-one year period. Data on reportable diseases and weather among Colorado counties were collected using the Colorado Electronic Disease Reporting System (CEDRS) and the Daily Surface Weather and Climatological Summaries (Daymet) Version 3 dataset, respectively. We used a conditional Poisson distributed-lag nonlinear modeling approach to estimate the lagged association (between 0 and 12-months) between relative temperature and precipitation extremes and the risk of cryptosporidiosis and giardiasis infection in Colorado counties between 1997 and 2017, relative to the risk found at average values of temperature and precipitation for a given county and month. We found distinctly different patterns in the associations between temperature extremes and cryptosporidiosis, versus temperature extremes and giardiasis. When maximum or minimum temperatures were high (90th percentile) or very high (95th percentile), we found a significant increase in cryptosporidiosis risk, but a significant decrease in giardiasis risk, relative to risk at the county and calendar-month mean. Conversely, we found very similar relationships between precipitation extremes and both cryptosporidiosis and giardiasis, which highlighted the prominent role of long-term (>8 months) lags. Our study presents novel insights on the influence that extreme temperature and precipitation can have on parasitic disease transmission in real-world settings. Additionally, we present preliminary evidence that the standard lag periods that are typically used in epidemiological studies to assess the impacts of extreme weather on cryptosporidiosis and giardiasis may not be capturing the entire relevant period.

Immunologic Profiling of Immune-Related Cutaneous Adverse Events with Checkpoint Inhibitors Reveals Polarized Actionable Pathways.

PURPOSE: Immune-related cutaneous adverse events (ircAEs) occur in ≥50% of patients treated with checkpoint inhibitors (CPI), but mechanisms are poorly understood. EXPERIMENTAL DESIGN: Phenotyping/biomarker analyses were conducted in 200 patients on CPIs (139 with ircAEs, 61 without, control) to characterize their clinical presentation and immunologic endotypes. Cytokines were evaluated in skin biopsies, skin tape strip (STS) extracts and plasma using real-time PCR and Meso Scale Discovery multiplex cytokine assays. RESULTS: Eight ircAE phenotypes were identified: pruritus (26%), maculopapular rash (MPR; 21%), eczema (19%), lichenoid (11%), urticaria (8%), psoriasiform (6%), vitiligo (5%), and bullous dermatitis (4%). All phenotypes showed skin lymphocyte and eosinophil infiltrates. Skin biopsy PCR revealed the highest increase in IFN-gamma mRNA in patients with lichenoid (p<0.0001) and psoriasiform dermatitis (p<0.01) as compared to patients without ircAEs, while the highest IL-13 mRNA levels were detected in the eczema (p<0.0001, compared to control). IL-17A mRNA was selectively increased in psoriasiform (p<0.001), lichenoid (p<0.0001), bullous dermatitis (p<0.05) and MPR (p<0.001), compared to control. Distinct cytokine profiles were confirmed in STS and plasma. Analysis determined increased skin/plasma IL-4 cytokine in pruritus, skin IL-13 in eczema, plasma IL-5 and IL-31 in eczema and urticaria, and mixed-cytokine pathways in MPR. Broad inhibition via corticosteroids or type 2-cytokine targeted inhibition resulted in clinical benefit in these ircAEs. In contrast, significant skin upregulation of type 1/type 17 pathways was found in psoriasiform, lichenoid, bullous dermatitis, and type 1 activation in vitiligo. CONCLUSIONS: Distinct immunologic ircAE endotypes suggest actionable targets for precision medicine-based interventions.

Hawaiian Volcanic Ash, an Airborne Fomite for Nontuberculous Mycobacteria.

Nontuberculous mycobacteria (NTM) are environmentally acquired opportunistic pathogens that can cause chronic lung disease. Within the U.S., Hawai'i shows the highest prevalence rates of NTM lung infections. Here, we investigated a potential role for active volcanism at the Kilauea Volcano located on Hawai'i Island in promoting NTM growth and diversity. We recovered NTM that are known to cause lung disease from plumbing biofilms and soils collected from the Kilauea environment. We also discovered viable Mycobacterium avium, Mycobacterium abscessus, and Mycobacterium intracellulare subsp. chimaera on volcanic ash collected during the 2018 Kilauea eruption. Analysis of soil samples showed that NTM prevalence is positively associated with bulk content of phosphorus, sulfur, and total organic carbon. In growth assays, we showed that phosphorus utilization is essential for proliferation of Kilauea-derived NTM, and demonstrate that NTM cultured with volcanic ash adhere to ash surfaces and remain viable. Ambient dust collected on O'ahu concurrent with the 2018 eruption contained abundant fresh volcanic glass, suggestive of inter-island ash transport. Phylogenomic analyses using whole genome sequencing revealed that Kilauea-derived NTM are genetically similar to respiratory isolates identified on other Hawaiian Islands. Consequently, we posit that volcanic eruptions could redistribute environmental microorganisms over large scales. While additional studies are needed to confirm a direct role of ash in NTM dispersal, our results suggest that volcanic particulates harbor and can redistribute NTM and should therefore be studied as a fomite for these burgeoning, environmentally acquired respiratory infections.

Early Evidence of Chronic Obstructive Pulmonary Disease Obscured by Race-Specific Prediction Equations.

Rationale: The identification of early chronic obstructive pulmonary disease (COPD) is essential to appropriately counsel patients regarding smoking cessation, provide symptomatic treatment, and eventually develop disease-modifying treatments. Disease severity in COPD is defined using race-specific spirometry equations. These may disadvantage non-White individuals in diagnosis and care. Objectives: Determine the impact of race-specific equations on African American (AA) versus non-Hispanic White individuals. Methods: Cross-sectional analyses of the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) cohort were conducted, comparing non-Hispanic White (n = 6,766) and AA (n = 3,366) participants for COPD manifestations. Measurements and Main Results: Spirometric classifications using race-specific, multiethnic, and "race-reversed" prediction equations (NHANES [National Health and Nutrition Examination Survey] and Global Lung Function Initiative "Other" and "Global") were compared, as were respiratory symptoms, 6-minute-walk distance, computed tomography imaging, respiratory exacerbations, and St. George's Respiratory Questionnaire. Application of different prediction equations to the cohort resulted in different classifications by stage, with NHANES and Global Lung Function Initiative race-specific equations being minimally different, but race-reversed equations moving AA participants to more severe stages and especially between the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 0 and preserved ratio impaired spirometry groups. Classification using the established NHANES race-specific equations demonstrated that for each of GOLD stages 1-4, AA participants were younger, had fewer pack-years and more current smoking, but had more exacerbations, shorter 6-minute-walk distance, greater dyspnea, and worse BODE (body mass index, airway obstruction, dyspnea, and exercise capacity) scores and St. George's Respiratory Questionnaire scores. Differences were greatest in GOLD stages 1 and 2. Race-reversed equations reclassified 774 AA participants (43%) from GOLD stage 0 to preserved ratio impaired spirometry. Conclusions: Race-specific equations underestimated disease severity among AA participants. These effects were particularly evident in early disease and may result in late detection of COPD.

Ultrasound-Guided Injections of HYADD4 for Knee Osteoarthritis Improves Pain and Functional Outcomes at 3, 6, and 12 Months without Changes in Measured Synovial Fluid, Serum Collagen Biomarkers, or Most Synovial Fluid Biomarker Proteins at 3 Months.

BACKGROUND: Prior studies have demonstrated improved efficacy when intra-articular (IA) therapeutics are injected using ultrasound (US) guidance. The aim of this study was to determine if clinical improvement in pain and function after IA hyaluronic acid injections using US is associated with changes in SF volumes and biomarker proteins at 3 months. METHODS: 49 subjects with symptomatic knee OA, BMI < 40, and KL radiographic grade II or III participated. Subjects with adequate aspirated synovial fluid (SF) volumes received two US-guided IA-HA injections of HYADD4 (24 mg/3 mL) 7 days apart. Clinical evaluations at 3, 6, and 12 months included WOMAC, VAS, PCS scores, 6 MWD, and US-measured SF depth. SF and blood were collected at 3 months and analyzed for four serum OA biomarkers and fifteen SF proteins. RESULTS: Statistical differences were observed at 3, 6, and 12 months compared to baseline values, with improvements at 12 months for WOMAC scores (50%), VAS (54%), and PCS scores (24%). MMP10 levels were lower at 3 months without changes in SF volumes, serum levels of C2C, COMP, HA, CPII, or SF levels of IL-1 ra, IL-4, 6, 7, 8, 15, 18, ILGFBP-1, 3, and MMP 1, 2, 3, 8, 9. Baseline clinical features or SF biomarker protein levels did not predict responsiveness at 3 months. CONCLUSIONS: Clinical improvements were observed at 12 months using US needle guidance for IA HA, whereas only one SF protein biomarker protein was different at 3 months. Larger studies are needed to identify which SF biomarkers will predict which individual OA patients will receive the greatest benefit from IA therapeutics.

Complete genome assembly of Hawai'i environmental nontuberculous mycobacteria reveals unexpected co-isolation with methylobacteria.

Nontuberculous mycobacteria (NTM) are ubiquitous environmental opportunistic pathogens that can cause chronic lung disease. Within the United States, Hawai'i has the highest incidence of NTM lung disease, though the precise reasons are yet to be fully elucidated. One possibility is the high prevalence of NTM in the Hawai'i environment acting as a potential reservoir for opportunistic NTM infections. Through our previous initiatives to collect and characterize NTM in Hawai'i, community scientists of Hawai'i have collected thousands of environmental samples for sequencing. Here, these community scientists were invited for the first time into a high school lab in O'ahu for a genomic sequencing workshop, where participants sequenced four of the collected isolate genomic samples using the Oxford Nanopore Technologies MinION sequencer. Participants generated high quality long read data that when combined with short read Illumina data yielded complete bacterial genomic assemblies suitable for in-depth analysis. The gene annotation analysis identified a suite of genes that might help NTM thrive in the Hawai'i environment. Further, we found evidence of co-occurring methylobacteria, revealed from the sequencing data, suggesting that in some cases methylobacteria and NTM may coexist in the same niche, challenging previously accepted paradigms. The sequencing efforts presented here generated novel insights regarding the potential survival strategies and microbial interactions of NTM in the geographic hot spot of Hawai'i. We highlight the contributions of community scientists and present an activity that can be reimplemented as a workshop or classroom activity by other research groups to engage their local communities.

Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms.

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets. In this work, dust-related metrics from four public areal data products were compared to a monitor-based "gold standard" dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern-Era Retrospective analysis for Research and Applications-Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol-type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi-binomial regression. In general, metrics from CAMS yielded the strongest associations with the "gold standard," followed by the NWS storm database metric. Dust aerosol (0.9-20 µm) mixing ratio, vertically integrated mass of dust aerosol (9-20 µm), and dust aerosol optical depth at 550 nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine-learning methods to the best-performing metrics to create a public dust storm database suitable for long-term epidemiologic studies.

Immunological and metabolic characterization of environmental Mycobacterium chimaera infection in a murine model.

Mycobacterium chimaera causes pulmonary disease, but little is known of gradations in isolate virulence. Previously, 17 M. chimaera isolates were screened for survival in THP1 macrophages. "M. chimaera 1" was categorized as "more virulent" because it showed the greatest survival in macrophages, whereas "M. chimaera 2" was categorized as "less virulent" with reduced survival. Herein, we infected C3HeB/FeJ mice to compare the in vivo immune responses to M. chimaera 1 and 2. Unlike macrophages, significantly lower M. chimaera 1 counts were recovered from mouse lung tissue and BAL cells with less lung histopathologic changes compared to M. chimaera 2. Compared to M. chimaera 2, significantly more IL-1ß, IL-6, and TNFα was produced early after M. chimaera 1 infection. LC-MS metabolomics analyses of BAL fluid revealed divergence in sphingolipid, phospholipid metabolism between M. chimaera 1 versus M. chimaera 2 mice. From pan-GWAS analyses, virulence and organizing DNA/molecular structure genes were associated with more virulent M. chimaera isolates. Vigorous lung-specific immune responses to M. chimaera 1 may influence effective bacterial control, but for a different isolate M. chimaera 2, subvert immune control. Continued studies of the gradations in virulence among the same NTM species will advance our understanding of NTM pathogenesis.

Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination.

BACKGROUND: Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence. METHODS: In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data. RESULTS: The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen's kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (an indicator of surface water presence) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways. CONCLUSION: Our results suggest that in low-transmission environments, leveraging open-source environmental data can yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.

Pirfenidone in fibrotic hypersensitivity pneumonitis: a double-blind, randomised clinical trial of efficacy and safety.

BACKGROUND: Fibrotic hypersensitivity pneumonitis (FHP) is an irreversible lung disease with high morbidity and mortality. We sought to evaluate the safety and effect of pirfenidone on disease progression in such patients. METHODS: We conducted a single-centre, randomised, double-blinded, placebo-controlled trial in adults with FHP and disease progression. Patients were assigned in a 2:1 ratio to receive either oral pirfenidone (2403 mg/day) or placebo for 52 weeks. The primary end point was the mean absolute change in the per cent predicted forced vital capacity (FVC%). Secondary end points included progression-free survival (PFS, time to a relative decline ≥10% in FVC and/or diffusing capacity of the lung for carbon monoxide (DLCO), acute respiratory exacerbation, a decrease of ≥50 m in the 6 min walk distance, increase or introduction of immunosuppressive drugs or death), change in FVC slope and mean DLCO%, hospitalisations, radiological progression of lung fibrosis and safety. RESULTS: After randomising 40 patients, enrolment was interrupted by the COVID-19 pandemic. There was no significant between-group difference in FVC% at week 52 (mean difference -0.76%, 95% CI -6.34 to 4.82). Pirfenidone resulted in a lower rate of decline in the adjusted FVC% at week 26 and improved PFS (HR 0.26, 95% CI 0.12 to 0.60). Results for other secondary end points showed no significant difference between groups. No deaths occurred in the pirfenidone group and one death (respiratory) occurred in the placebo group. There were no treatment-emergent serious adverse events. CONCLUSIONS: The trial was underpowered to detect a difference in the primary end point. Pirfenidone was found to be safe and improved PFS in patients with FHP. TRIAL REGISTRATION MUMBER: NCT02958917.

Use of the Spirometric "Fixed-Ratio" Underdiagnoses COPD in African-Americans in a Longitudinal Cohort Study.

BACKGROUND: COPD diagnosis is tightly linked to the fixed-ratio spirometry criteria of FEV1/FVC < 0.7. African-Americans are less often diagnosed with COPD. OBJECTIVE: Compare COPD diagnosis by fixed-ratio with findings and outcomes by race. DESIGN: Genetic Epidemiology of COPD (COPDGene) (2007-present), cross-sectional comparing non-Hispanic white (NHW) and African-American (AA) participants for COPD diagnosis, manifestations, and outcomes. SETTING: Multicenter, longitudinal US cohort study. PARTICIPANTS: Current or former smokers with ≥ 10-pack-year smoking history enrolled at 21 clinical centers including over-sampling of participants with known COPD and AA. Exclusions were pre-existing non-COPD lung disease, except for a history of asthma. MEASUREMENTS: Subject diagnosis by conventional criteria. Mortality, imaging, respiratory symptoms, function, and socioeconomic characteristics, including area deprivation index (ADI). Matched analysis (age, sex, and smoking status) of AA vs. NHW within participants without diagnosed COPD (GOLD 0; FEV1 ≥ 80% predicted and FEV1/FVC ≥ 0.7). RESULTS: Using the fixed ratio, 70% of AA (n = 3366) were classified as non-COPD, versus 49% of NHW (n = 6766). AA smokers were younger (55 vs. 62 years), more often current smoking (80% vs. 39%), with fewer pack-years but similar 12-year mortality. Density distribution plots for FEV1 and FVC raw spirometry values showed disproportionate reductions in FVC relative to FEV1 in AA that systematically led to higher ratios. The matched analysis demonstrated GOLD 0 AA had greater symptoms, worse DLCO, spirometry, BODE scores (1.03 vs 0.54, p < 0.0001), and greater deprivation than NHW. LIMITATIONS: Lack of an alternative diagnostic metric for comparison. CONCLUSIONS: The fixed-ratio spirometric criteria for COPD underdiagnosed potential COPD in AA participants when compared to broader diagnostic criteria. Disproportionate reductions in FVC relative to FEV1 leading to higher FEV1/FVC were identified in these participants and associated with deprivation. Broader diagnostic criteria for COPD are needed to identify the disease across all populations.

Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination.

Background: Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence. Methods: In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data. Results: The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen’s kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (NDWI) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways. Conclusion: Our results suggest that in low-transmission environments, investing in training geographic information systems professionals to leverage open-source environmental data could yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.

A nested machine learning approach to short-term PM2.5 prediction in metropolitan areas using PM2.5 data from different sensor networks.

Many predictive models for ambient PM2.5 concentrations rely on ground observations from a single monitoring network consisting of sparsely distributed sensors. Integrating data from multiple sensor networks for short-term PM2.5 prediction remains largely unexplored. This paper presents a machine learning approach to predict ambient PM2.5 concentration levels at any unmonitored location several hours ahead using PM2.5 observations from nearby monitoring sites from two sensor networks and the location's social and environmental properties. Specifically, this approach first applies a Graph Neural Network and Long Short-Term Memory (GNN-LSTM) network to time series of daily observations from a regulatory monitoring network to make predictions of PM2.5. This network produces feature vectors to store aggregated daily observations as well as dependency characteristics to predict daily PM2.5. The daily feature vectors are then set as the precondition of the hourly level learning process. The hourly level learning again uses a GNN-LSTM network based on daily dependency information and hourly observations from a low-cost sensor network to produce spatiotemporal feature vectors capturing the combined dependency described by daily and hourly observations. Finally, the spatiotemporal feature vectors from the hourly learning process and social-environmental data are merged and used as the input to a single-layer Fully Connected (FC) network to output the predicted hourly PM2.5 concentrations. To demonstrate the benefits of this novel prediction approach, we have conducted a case study using data collected from two sensor networks in Denver, CO, during 2021. Results show that the utilization of data from two sensor networks improves the overall performance of predicting fine-level, short-term PM2.5 concentrations compared to other baseline models.

The Unintended Benefits of the Conservation Reserve Program for Air Quality.

The link between agriculture and air pollution is well-established, as are the benefits of the US Department of Agriculture's Conservation Reserve Program (CRP). However, little research has linked CRP to air quality directly. This study aims to address this gap by modeling the relationship between CRP and fine particulate matter (PM2.5) concentrations at the county level from 2001 to 2016. Several econometric models are estimated with panel data while controlling for drought, population, and wildfire. Results show that CRP has a statistically significant negative effect on PM2.5 concentrations. Using estimates from this model, we project an avoided 1,353 deaths, 1,687 deaths, and 3,022 deaths nationally in 2008 relative to three different counterfactual scenarios: all CRP acreage placed under cultivation, increased drought, and a combination of the first two. The value of the avoided mortality is estimated to be $9.5 billion, $11.8 billion, and $21.2 billion, respectively. These findings provide evidence that CRP may generate economic gains in terms of avoided mortality, well above the cost of the program.

Exposures to low-levels of fine particulate matter are associated with acute changes in heart rate variability, cardiac repolarization, and circulating blood lipids in coronary artery disease patients.

Exposure to air pollution is a major risk factor for cardiovascular disease, disease risk factors, and mortality. Specifically, particulate matter (PM), and to some extent ozone, are contributors to these effects. In addition, exposures to these pollutants may be especially dangerous for susceptible populations. In this repeated-visit panel study, cardiovascular markers were collected from thirteen male participants with stable coronary artery disease. For 0-4 days prior to the health measurement collections, daily concentrations of fine PM (PM2.5) and ozone were obtained from local central monitoring stations located near the participant's homes. Then, single (PM2.5) and two-pollutant (PM2.5 and ozone) models were used to assess whether there were short-term changes in cardiovascular health markers. Per interquartile range increase in PM2.5, there were decrements in several heart rate variability metrics, including the standard deviation of the normal-to-normal intervals (lag 3, -5.8%, 95% confidence interval (CI) = -11.5, 0.3) and root-mean squared of successive differences (five day moving average, -8.1%, 95% CI = -15.0, -0.7). In addition, increases in PM2.5 were also associated with changes in P complexity (lag 1, 4.4%, 95% CI = 0.5, 8.5), QRS complexity (lag 1, 4.9%, 95% CI = 1.4, 8.5), total cholesterol (five day moving average, -2.1%, 95% CI = -4.1, -0.1), and high-density lipoprotein cholesterol (lag 2, -1.6%, 95% CI = -3.1, -0.1). Comparisons to our previously published work on ozone were conducted. We found that ozone affected inflammation and endothelial function, whereas PM2.5 influenced heart rate variability, repolarization, and lipids. All the health changes from these two studies were found at concentrations below the United States Environmental Protection Agency's National Ambient Air Quality Standards. Our results imply clear differences in the cardiovascular outcomes observed with exposure to the two ubiquitous air pollutants PM2.5 and ozone; this observation suggests different mechanisms of toxicity for these exposures.

Soil Properties and Moisture Synergistically Influence Nontuberculous Mycobacterial Prevalence in Natural Environments of Hawai'i.

Nontuberculous mycobacteria (NTM) are opportunistic pathogens that cause chronic pulmonary disease (PD). NTM infections are thought to be acquired from the environment; however, the basal environmental factors that drive and sustain NTM prevalence are not well understood. The highest prevalence of NTM PD cases in the United States is reported from Hawai'i, which is unique in its climate and soil composition, providing an opportunity to investigate the environmental drivers of NTM prevalence. We used microbiological sampling and spatial logistic regression complemented with fine-scale soil mineralogy to model the probability of NTM presence across the natural landscape of Hawai'i. Over 7 years, we collected and microbiologically cultured 771 samples from 422 geographic sites in natural areas across the Hawaiian Islands for the presence of NTM. NTM were detected in 210 of these samples (27%), with Mycobacterium abscessus being the most frequently isolated species. The probability of NTM presence was highest in expansive soils (those that swell with water) with a high water balance (>1-m difference between rainfall and evapotranspiration) and rich in Fe-oxides/hydroxides. We observed a positive association between NTM presence and iron in wet soils, supporting past studies, but no such association in dry soils. High soil-water balance may facilitate underground movement of NTM into the aquifer system, potentially compounded by expansive capabilities allowing crack formation under drought conditions, representing further possible avenues for aquifer infiltration. These results suggest both precipitation and soil properties are mechanisms by which surface NTM may reach the human water supply. IMPORTANCE Nontuberculous mycobacteria (NTM) are ubiquitous in the environment, being found commonly in soils and natural bodies of freshwater. However, little is known about the environmental niches of NTM and how they relate to NTM prevalence in homes and other human-dominated areas. To characterize NTM environmental associations, we collected and cultured 771 samples from 422 geographic sites in natural areas across Hawai'i, the U.S. state with the highest prevalence of NTM pulmonary disease. We show that the environmental niches of NTM are most associated with highly expansive, moist soils containing high levels of iron oxides/hydroxides. Understanding the factors associated with NTM presence in the natural environment will be crucial for identifying potential mechanisms and risk factors associated with NTM infiltration into water supplies, which are ultimately piped into homes where most exposure risk is thought to occur.

Fluctuations in quality of life and immune responses during intravenous immunoglobulin infusion cycles.

Despite adequate infection prophylaxis, variation in self-reported quality of life (QOL) throughout the intravenous immunoglobulin (IVIG) infusion cycle is a widely reported but infrequently studied phenomenon. To better understand this phenomenon, subjects with humoral immunodeficiency receiving replacement doses of IVIG were studied over 3 infusion cycles. Questionnaire data from 6 time points spread over 3 IVIG infusions cycles (infusion day and 7 days after each infusion) were collected in conjunction with monitoring the blood for number of regulatory T-cells (Treg) and levels of 40 secreted analytes: primarily cytokines, chemokines, and growth factors. At day 7, self-reported well-being increased, and self-reported fatigue decreased, reflecting an overall improvement in QOL 7 days after infusion. Over the same period, percentage of Treg cells in the blood increased (p<0.01). Multiple inflammatory chemokine and cytokine levels increased in the blood by 1 hour after infusion (CCL4 (MIP-1b), CCL3 (MIP-1a), CCL2 (MCP-1), TNF-α, granzyme B, IL-10, IL-1RA, IL-8, IL-6, GM-CSF, and IFN- γ). The largest changes in analytes occurred in subjects initiated on IVIG during the study. A significant decrease in IL-25 (IL-17E) following infusion was seen in most intervals among subjects already receiving regular infusions prior to study entry. These findings reveal several short-term effects of IVIG given in replacement doses to patients with humoral immunodeficiency: QOL consistently improves in the first week of infusion, levels of a collection of monocyte-associated cytokines increase immediately after infusion whereas IL-25 levels decrease, and Treg levels increase. Moreover, patients that are new to IVIG experience more significant fluctuations in cytokine levels than those receiving it regularly.