Expanded PAH analysis of household air pollution in a rural region of China with high lung cancer incidence.

The domestic combustion of locally sourced smoky (bituminous) coal in Xuanwei and Fuyuan counties, China, is responsible for some of the highest lung cancer rates in the world. Recent research has pointed to methylated PAHs (mPAHs), particularly 5-methylchrysene (5MC), within coal combustion products as a driving factor. Here we describe measurements of mPAHs in Xuanwei and Fuyuan derived from controlled burnings (i.e., water boiling tests, WBT, n = 27) representing exposures during stove use, and an exposure assessment (EA) study (n=116) representing 24 h weighted exposures. Using smoky coal leads to significantly higher concentrations of known and likely human carcinogens than using smokeless coal, including 5MC (3.7 ng/m3 vs. 1.0 ng/m3 for EA samples and 100.8 ng/m3 vs. 2.2 ng/m3 for WBT samples), benzo[a]pyrene (38.0 ng/m3 vs. 7.9 ng/m3 for EA samples and 455.3 ng/m3 vs. 12.0 ng/m3 for WBT samples), and 7,12-dimethylbenz[a]anthracene (1.9 ng/m3 vs. 0.2 ng/m3 for EA samples and 47.7 ng/m3 vs. 0.6 ng/m3 for WBT samples). Mixed effect models for both EA samples and WBT samples revealed clear variation in mPAHs concentrations depending on smoky coal source while stove ventilation was consistently found to reduce measured concentrations (by up to nine fold and 65 fold for EA and WBT samples respectively when using smoky coal). Fuel type had a larger influence on mPAHs concentrations than stove type. These findings indicate that users of smoky coal experience exposure to many PAHs, including known and suspected human carcinogens (especially during cooking activities), many of which are not routinely tested for. Collectively, this provides insights into the potential etiologies of lung cancer in the region and further highlights the importance of clean fuel transitions and stove refinements as the final goal for reducing household air pollution and its associated health risks.

Physiological Response to the COVID-19 Vaccine: Insights From a Prospective, Randomized, Single-Blinded, Crossover Trial.

BACKGROUND: Rapid development and implementation of vaccines constituted a crucial step in containing the COVID-19 pandemic. A comprehensive understanding of physiological responses to these vaccines is important to build trust in medicine. OBJECTIVE: This study aims to investigate temporal dynamics before and after COVID-19 vaccination in 4 physiological parameters as well as the duration of menstrual cycle phases. METHODS: In a prospective trial, 17,825 adults in the Netherlands wore a medical device on their wrist for up to 9 months. The device recorded their physiological signals and synchronized with a complementary smartphone app. By means of multilevel quadratic regression, we examined changes in wearable-recorded breathing rate, wrist skin temperature, heart rate, heart rate variability, and objectively assessed the duration of menstrual cycle phases in menstruating participants to assess the effects of COVID-19 vaccination. RESULTS: The recorded physiological signals demonstrated short-term increases in breathing rate and heart rate after COVID-19 vaccination followed by a prompt rebound to baseline levels likely reflecting biological mechanisms accompanying the immune response to vaccination. No sex differences were evident in the measured physiological responses. In menstruating participants, we found a 0.8% decrease in the duration of the menstrual phase following vaccination. CONCLUSIONS: The observed short-term changes suggest that COVID-19 vaccines are not associated with long-term biophysical issues. Taken together, our work provides valuable insights into continuous fluctuations of physiological responses to vaccination and highlights the importance of digital solutions in health care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-021-05241-5.

Hemoglobin and Its Relationship with Fatigue in Long-COVID Patients Three to Six Months after SARS-CoV-2 Infection.

Background: While some long-term effects of COVID-19 are respiratory in nature, a non-respiratory effect gaining attention has been a decline in hemoglobin, potentially mediated by inflammatory processes. In this study, we examined the correlations between hemoglobin levels and inflammatory biomarkers and evaluated the association between hemoglobin and fatigue in a cohort of Long-COVID patients. Methods: This prospective cohort study in the Netherlands evaluated 95 (mostly hospitalized) patients, aged 40-65 years, 3-6 months post SARS-CoV-2 infection, examining their venous hemoglobin concentration, anemia (hemoglobin < 7.5 mmol/L in women and <8.5 mmol/L in men), inflammatory blood biomarkers, average FSS (Fatigue Severity Score), demographics, and clinical features. Follow-up hemoglobin was compared against hemoglobin during acute infection. Spearman correlation was used for assessing the relationship between hemoglobin concentrations and inflammatory biomarkers, and the association between hemoglobin and fatigue was examined using logistic regression. Results: In total, 11 (16.4%) participants were suffering from anemia 3-6 months after SARS-CoV-2 infection. The mean hemoglobin value increased by 0.3 mmol/L 3-6 months after infection compared to the hemoglobin during the acute phase (p-value = 0.003). Whilst logistic regression showed that a 1 mmol/L greater increase in hemoglobin is related to a decrease in experiencing fatigue in Long-COVID patients (adjusted OR 0.38 [95%CI 0.13-1.09]), we observed no correlations between hemoglobin and any of the inflammatory biomarkers examined. Conclusion: Our results indicate that hemoglobin impairment might play a role in developing Long-COVID fatigue. Further investigation is necessary to identify the precise mechanism causing hemoglobin alteration in these patients.

Early life ambient air pollution, household fuel use, and under-5 mortality in Ghana.

INTRODUCTION: Environmental exposures, such as ambient air pollution and household fuel use affect health and under-5 mortality (U5M) but there is a paucity of data in the Global South. This study examined early-life exposure to ambient particulate matter with a diameter of 2.5 µm or less (PM2.5), alongside household characteristics (including self-reported household fuel use), and their relationship with U5M in the Navrongo Health and Demographic Surveillance Site (HDSS) in northern Ghana. METHODS: We employed Satellite-based spatiotemporal models to estimate the annual average PM2.5 concentrations with the Navrongo HDSS area (1998 to 2016). Early-life exposure levels were determined by pollution estimates at birth year. Socio-demographic and household data, including cooking fuel, were gathered during routine surveillance. Cox proportional hazards models were applied to assess the link between early-life PM2.5 exposure and U5M, accounting for child, maternal, and household factors. FINDINGS: We retrospectively studied 48,352 children born between 2007 and 2017, with 1872 recorded deaths, primarily due to malaria, sepsis, and acute respiratory infection. Mean early-life PM2.5 was 39.3 µg/m3, and no significant association with U5M was observed. However, Children from households using "unclean" cooking fuels (wood, charcoal, dung, and agricultural waste) faced a 73 % higher risk of death compared to those using clean fuels (adjusted HR = 1.73; 95 % CI: 1.29, 2.33). Being born female or to mothers aged 20-34 years were linked to increased survival probabilities. INTERPRETATION: The use of "unclean" cooking fuel in the Navrongo HDSS was associated with under-5 mortality, highlighting the need to improve indoor air quality by introducing cleaner fuels.

Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study).

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches.

Traffic-related air pollution, road traffic noise, and Parkinson's disease: Evaluations in two Dutch cohort studies.

Background: Environmental factors such as air pollution have been associated with Parkinson's disease (PD), but findings have been inconsistent. We investigated the association between exposure to several air pollutants, road traffic noise, and PD risk in two Dutch cohorts. Methods: Data from 50,087 participants from two Dutch population-based cohort studies, European Prospective Investigation into Cancer and Nutrition in the Netherlands and Arbeid, Milieu en Gezondheid Onderzoek were analyzed. In these cohorts, 235 PD cases were ascertained based on a previously validated algorithm combining self-reported information (diagnosis, medication, and symptoms) and registry data. We assigned the following traffic-related exposures to residential addresses at baseline: NO2, NOx, particulate matter (PM)2.5absorbance (as a marker for black carbon exposure), PM with aerodynamic diameter ≤2.5 µm (PM2.5), ≤10 µm (PM10), PMcoarse (size fraction 2.5-10 µm), ultrafine particles <0.1 µm (UFP), and road traffic noise (Lden). Logistic regression models were applied to investigate the associations with PD, adjusted for possible confounders. Results: Both single- and two-pollutant models indicated associations between exposure to NOx, road traffic noise, and increasing odds of developing PD. Odds ratios of fully adjusted two-pollutant models in the highest compared with the lowest exposure quartile were 1.62 (95% CI = 1.02, 2.62) for NOx and 1.47 (95% CI = 0.97, 2.25) for road traffic noise, with clear trends across exposure categories. Conclusions: Our findings suggest that NOx and road traffic noise are associated with an increased risk of PD. While the association with NOx has been shown before, further investigation into the possible role of environmental noise on PD is warranted.