Short-term ozone exposure and cancer mortality in Brazil: A nationwide case-crossover study.

Exposure to ambient ozone (O3) is linked to increased mortality risks from various diseases, but epidemiological investigations delving into its potential implications for cancer mortality are limited. We aimed to examine the association between short-term O3 exposure and site-specific cancer mortality and investigate vulnerable subgroups in Brazil. In total 3,459,826 cancer death records from 5570 Brazilian municipalities between 2000 and 2019, were included. Municipal average daily O3 concentration was calculated from a global estimation at 0.25°×0.25° spatial resolution. The time-stratified case-crossover design was applied to assess the O3-cancer mortality association. Subgroup analyses by age, sex, season, time-period, region, urban hierarchy, climate classification, quantiles of GDP per capita and illiteracy rates were performed. A linear and non-threshold exposure-response relationship was observed for short-term exposure to O3 with cancer mortality, with a 1.00% (95% CI: 0.79%-1.20%) increase in all-cancer mortality risks for each 10-µg/m3 increment of three-day average O3. Kidney cancer was most strongly with O3 exposure, followed by cancers of the prostate, stomach, breast, lymphoma, brain and lung. The associated cancer risks were relatively higher in the warm season and in southern Brazil, with a decreasing trend over time. When restricting O3 concentration to the national minimum value during 2000-2019, a total of 147,074 (116,690-177,451) cancer deaths could be avoided in Brazil, which included 17,836 (7014-28,653) lung cancer deaths. Notably, these associations persisted despite observed adaptation within the Brazilian population, highlighting the need for a focus on incorporating specific measures to mitigate O3 exposure into cancer care recommendations.

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 µg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.

Using machine learning to predict the risk of short-term and long-term death in acute kidney injury patients after commencing CRRT.

BACKGROUND: The mortality rate and prognosis of short-term and long-term acute kidney injury (AKI) patients who undergo continuous renal replacement therapy (CRRT) are different. Setting up risk stratification tools for both short-term and long-term deaths is highly important for clinicians. METHOD: A total of 1535 AKI patients receiving CRRT were included in this study, with 1144 from the training set (the Dryad database) and 391 from the validation set (MIMIC IV database). A model for predicting mortality within 10 and 90 days was built using nine different machine learning (ML) algorithms. AUROC, F1-score, accuracy, sensitivity, specificity, precision, and calibration curves were used to assess the predictive performance of various ML models. RESULTS: A total of 420 (31.1%) deaths occurred within 10 days, and 1080 (68.8%) deaths occurred within 90 days. The random forest (RF) model performed best in both predicting 10-day (AUROC: 0.80, 95% CI: 0.74-0.84; accuracy: 0.72, 95% CI: 0.67-0.76; F1-score: 0.59) and 90-day mortality (AUROC: 0.78, 95% CI: 0.73-0.83; accuracy: 0.73, 95% CI: 0.69-0.78; F1-score: 0.80). The importance of the feature shows that SOFA scores are rated as the most important risk factor for both 10-day and 90-day mortality. CONCLUSION: Our study, utilizing multiple machine learning models, estimates the risk of short-term and long-term mortality among AKI patients who commence CRRT. The results demonstrated that the prognostic factors for short-term and long-term mortality are different. The RF model has the best prediction performance and has valuable potential for clinical application.

Maternal ozone exposure lowers infant's birthweight: A nationwide cohort of over 4 million livebirths in Iran.

BACKGROUND: Nationwide evidence linking maternal ozone exposure with fetal growth restriction (FGR) was extensively scarce, especially in the Middle East with dry climate and distinct religious culture. METHODS: We carried out a national retrospective birth cohort study using registry-based records from 749 hospitals across 31 provinces in Iran from 2013 to 2018. Monthly concentrations of maximum daily average 8-hour (MDA8) ozone at 0.125° × 0.125° resolution were extracted from well-validated spatiotemporal grid dataset. Linear and logistic regression models were employed to evaluate associations of maternal MDA8 ozone exposure with birthweight outcomes. Assuming causality, the comparative risk assessment framework was utilized to estimate the burden of low birthweight (LBW), small for gestational age (SGA), and birthweight loss per livebirth (BLL) attributable to ambient ozone pollution. RESULTS: Of 4030383 livebirths included in the study, 264304 (6.6%) were LBW and 484405 (12.0%) were SGA. Each 10-ppb increase in MDA8 ozone exposure was associated with an odds ratio of 1.123 (95% confidence interval [CI]: 1.104 to 1.142) for LBW and 1.210 (95% CI: 1.197 to 1.223) for SGA, and a 30.5-g (95% CI: 29.0 to 32.0) reduction in birthweight. We observed approximately linear exposure-response relationships of maternal MDA8 ozone exposure with LBW (Pnonlinear= 0.786), SGA (Pnonlinear= 0.156), and birthweight reduction (Pnonlinear= 0.104). Under the premise of causal association, we estimated 6.6% (95% CI: 5.7 to 7.5) of LBW, 10.1% (95% CI: 9.6 to 10.6) of SGA, and 18.8 g (95% CI: 17.9 to 19.7) of BLL could be attributable to maternal ozone exposure in Iran. Considerably greater risk and burden of ozone-related FGR were observed among younger, less-educated, and rural-dwelling mothers. CONCLUSIONS: Our study provided compelling evidence that maternal ozone exposure was associated with heightened FGR risk and burden, particularly among socioeconomically disadvantaged mothers. These findings underscored the urgent need for government to incorporate socioeconomic factors into future ozone-related health policies, not only to mitigate pollution, but also minimize inequality.

Air quality improvements can strengthen China's food security.

Air pollution exerts crucial influence on crop yields and impacts regional and global food supplies. Here we employ a statistical model using satellite-based observations and flexible functional forms to analyse the synergistic effects of reductions in ozone and aerosols on China's food security. The model consistently shows that ozone is detrimental to crops, whereas aerosol has variable effects. China's maize, rice and wheat yields are projected to increase by 7.84%, 4.10% and 3.43%, respectively, upon reaching two air quality targets (60 µg m-3 for peak-season ozone and 35 µg m-3 for annual fine particulate matter). Average calories produced from these crops would surge by 4.51%, potentially allowing China to attain grain self-sufficiency 2 years earlier than previously estimated. These results show that ozone pollution control should be a high priority to increase staple crop edible calories, and future stringent air pollution regulations would enhance China's food security.

Environmentally relevant exposure to cotinine induces neurobehavioral toxicity in zebrafish (Danio rerio): A study using neurobehavioral and metabolomic approaches.

As an important psychoactive substance, cotinine is ubiquitous in aquatic environment and poses a threat to aquatic organisms. However, the mechanism of its adverse health impacts remains unclear. We evaluated the effects of cotinine exposure at environmentally relevant concentrations on the development and locomotor behavior of zebrafish (Danio rerio) larvae using neurotransmitters and whole endogenous metabolism. Mild developmental toxicity and significant neurobehavior disorder, such as spontaneous movement (1-1000 µg/L), 48 hpf tactile response (50, 100, and 1000 µg/L), and 144 hpf swimming speed (1, 10, 100, 500, and 1000 µg/L), were observed in zebrafish. Exposure to cotinine led to significant alterations in 11 neurotransmitters, including homogentisic acid, serotonin, glutamic acid and aspartic acid, etc. 298 metabolites were identified and two pathways - linoleic acid metabolism and taurine and hypotaurine metabolism - were delineated. In addition, amino acid neurotransmitters were significantly correlated with metabolites such as arachidonic acid as well as its derivatives, steroidal compounds, and amino acids. Serotonin demonstrates a noteworthy correlation with 31 out of 40 differentially expressed neurotransmitters, encompassing lipids, amino acids, and other compounds. These novel findings contribute to a comprehensive understanding of the ecological risks associated with cotinine contamination in surface waters.

A Chinese longitudinal maternity cohort study (2013-2021) on intrahepatic cholestasis phenotypes: Risk associations from environmental exposure to adverse pregnancy outcomes.

Intrahepatic cholestasis of pregnancy (ICP) is an idiopathic disease that occurs during mid-to-late pregnancy and is associated with various adverse pregnancy outcomes, including intrauterine fetal demise. However, since the underlying cause of ICP remains unclear, there is an ongoing debate on the phenotyping criteria used in the diagnostic process. Here, we identified single- and multi-symptomatic ICP (ICP-S and ICP-M) in 104,221 Chinese females from the ZEBRA maternity cohort, with the objective of exploring the risk implications of the two phenotypes on pregnancy outcomes and from environmental exposures. We employed multivariate binary logistic regression to estimate confounder-adjusted odds ratios and found that ICP-M was more strongly associated with preterm birth and low birth weight compared to ICP-S. Throughout pregnancy, incremental exposure to PM2.5, O3, and greenness could alter ICP risks by 17.3%, 12.5%, and -2.3%, respectively, with more substantial associations observed with ICP-M than with ICP-S. The major scientific advancements lie in the elucidation of synergistic risk interactions between pollutants and the protective antagonistic effects of greenness, as well as highlighting the risk impact of preconceptional environmental exposures. Our study, conducted in the context of the "three-child policy" in China, provides epidemiological evidence for policy-making to safeguard maternal and neonatal health.