Effects of Ambient Coarse,Fine, and Ultrafine Particles and Their Biological Constituents on Systemic Biomarkers: A Controlled Human Exposure Study

Background

Ambient coarse, fine, and ultrafine particles have been associated with mortality and morbidity. Few studies have compared how various particle size fractions affect systemic biomarkers.

Objectives

We examined changes of blood and urinary biomarkers following exposures to three particle sizes.

Methods

Fifty healthy nonsmoking volunteers, mean age of 28 years, were exposed to coarse (2.5–10 μm; mean, 213 μg/m³) and fine (0.15–2.5 μm; mean, 238 μg/m³) concentrated ambient particles (CAPs), and filtered ambient and/or medical air. Twenty-five participants were exposed to ultrafine CAP (< 0.3 μm; mean, 136 μg/m³) and filtered medical air. Exposures lasted 130 min, separated by ≥ 2 weeks. Blood/urine samples were collected preexposure and 1 hr and 21 hr postexposure to determine blood interleukin-6 and C-reactive protein (inflammation), endothelin-1 and vascular endothelial growth factor (VEGF; vascular mediators), and malondialdehyde (lipid peroxidation); as well as urinary VEGF, 8-hydroxy-deoxy-guanosine (DNA oxidation), and malondialdehyde. Mixed-model regressions assessed pre- and postexposure differences.

Results

One hour postexposure, for every 100-μg/m³ increase, coarse CAP was associated with increased blood VEGF (2.41 pg/mL; 95% CI: 0.41, 4.40) in models adjusted for O_3, fine CAP with increased urinary malondialdehyde in single- (0.31 nmol/mg creatinine; 95% CI: 0.02, 0.60) and two-pollutant models, and ultrafine CAP with increased urinary 8-hydroxydeoxyguanosine in single- (0.69 ng/mg creatinine; 95% CI: 0.09, 1.29) and two-pollutant models, lasting < 21 hr. Endotoxin was significantly associated with biomarker changes similar to those found with CAPs.

Conclusions

Ambient particles with various sizes/constituents may influence systemic biomarkers differently. Endotoxin in ambient particles may contribute to vascular mediator changes and oxidative stress.

Citation

Liu L, Urch B, Poon R, Szyszkowicz M, Speck M, Gold DR, Wheeler AJ, Scott JA, Brook JR, Thorne PS, Silverman FS. 2015. Effects of ambient coarse, fine, and ultrafine particles and their biological constituents on systemic biomarkers: a controlled human exposure study. Environ Health Perspect 123:534–540; http://dx.doi.org/10.1289/ehp.1408387     

Interpreting declines in HIV test positivity: an analysis of routine data from Zimbabwe's national sex work programme, 2009–2019

IntroductionEarly diagnosis of HIV is critical for epidemic control. To achieve this, successful testing programmes are essential and test positivity is often used as a marker of their performance. The aim of this study was to analyse trends and predictors of HIV test positivity over time and explore how an understanding of seroconversion rates could build on our interpretation of this indicator among female sex workers in Zimbabwe.MethodsWe analysed HIV test data from Zimbabwe''s nationally scaled sex work programme between 2009 and 2019. We defined test positivity as the proportion of all tests that were HIV positive and measured new diagnoses by estimating seroconversion rates among women with repeat tests, defined as an HIV‐positive test after at least one HIV‐negative test in the programme. We used logistic regression to analyse test positivity over three time‐periods: 2009–2013, 2014–2017 and 2018–2019, adjusting for potential confounding by demographic factors and the mediating effects of time since last HIV test. We calculated the seroconversion rates for the same time‐periods.ResultsDuring the 10‐year study period, 54,503 tests were recorded in 39,462 women. Between 2009 and 2013, 18% of tests were among women who reported testing in the previous 6 months. By 2018–2019, this had increased to 57%. Between 2018 and 2019, test positivity was 9.6%, compared to 47.9% for 2009–2013 (aOR 6.08 95% CI 5.52–6.70) and 18.8% for 2014–2017 (aOR 2.17 95% CI 2.06–2.28). Adjusting for time since last test reduced effect estimates for 2009–2013 (aOR 4.03 95% CI 3.64–4.45) and 2014–2017 (aOR 1.97 95% CI 1.86–2.09) compared to 2018–2019. Among 7573 women with an initial HIV‐negative test in the programme and at least one subsequent test, 464 tested HIV positive at a rate of 3.9 per 100 pyar (95% CI 3.5–4.2).ConclusionsTest positivity decreased among women testing through the programme over time, while seroconversion rates remained high. These declines were partly driven by changes in individual testing history, reflecting comprehensive coverage of testing services and greater knowledge of HIV status, but not necessarily declining rates of seroconversion. Understanding testing history and monitoring new HIV infections from repeat tests could strengthen the interpretation of test positivity and provide a better understanding of programme performance.     

Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model

There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters—sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment.     

Peripancreatic Fat Invasion Is an Independent Predictor of Poor Outcome Following Pancreaticoduodenectomy for Pancreatic Ductal Adenocarcinoma

Background  

Following pancreaticoduodenectomy for pancreatic ductal adenocarcinoma (PDAC), identification of peripancreatic fat tumor invasion promotes a tumor to stage T3. We sought to understand better the impact of histological peripancreatic fat invasion on prognosis and site of recurrence in a cohort of patients with PDAC.     

The validity,reliability and generalizability of diary data

There is need for reliable and valid behavioral measures for field research. Five sets of data were analyzed, in which diary as well as interview approaches were taken. Results suggest that scores derived from one-week diaries have satisfactory construct validity (convergent, concurrent, predictive and nomological); that, for salient activities, they are less subjective to retrospective bias than are interview data and that, for this reason, interview-diary discrepancies may be useful for hypothesis testing. For items of neutral value, interview and diary provided remarkably similar values. A one-day diary was not an adequate substitute for a one-week diary. Attrition related to diary-keeping resulted in under-representation of certain groups (e.g., low income, little education, minority ethnicity, poor health), and conclusions would be different if based on diarists' responses or those of the intact sample.