Global population exposure to landscape fire air pollution from 2000 to 2019.

Wildfires are thought to be increasing in severity and frequency as a result of climate change1-5. Air pollution from landscape fires can negatively affect human health4-6, but human exposure to landscape fire-sourced (LFS) air pollution has not been well characterized at the global scale7-23. Here, we estimate global daily LFS outdoor fine particulate matter (PM2.5) and surface ozone concentrations at 0.25° × 0.25° resolution during the period 2000-2019 with the help of machine learning and chemical transport models. We found that overall population-weighted average LFS PM2.5 and ozone concentrations were 2.5 µg m-3 (6.1% of all-source PM2.5) and 3.2 µg m-3 (3.6% of all-source ozone), respectively, in 2010-2019, with a slight increase for PM2.5, but not for ozone, compared with 2000-2009. Central Africa, Southeast Asia, South America and Siberia experienced the highest LFS PM2.5 and ozone concentrations. The concentrations of LFS PM2.5 and ozone were about four times higher in low-income countries than in high-income countries. During the period 2010-2019, 2.18 billion people were exposed to at least 1 day of substantial LFS air pollution per year, with each person in the world having, on average, 9.9 days of exposure per year. These two metrics increased by 6.8% and 2.1%, respectively, compared with 2000-2009. Overall, we find that the global population is increasingly exposed to LFS air pollution, with socioeconomic disparities.

SMG: self-supervised masked graph learning for cancer gene identification.

Cancer genomics is dedicated to elucidating the genes and pathways that contribute to cancer progression and development. Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data. Nevertheless, because of the limited labelled data, pinpointing CGs from a multitude of potential mutations remains an exceptionally challenging task. To address this, we propose a novel deep learning framework, termed self-supervised masked graph learning (SMG), which comprises SMG reconstruction (pretext task) and task-specific fine-tuning (downstream task). In the pretext task, the nodes of multi-omic featured protein-protein interaction (PPI) networks are randomly substituted with a defined mask token. The PPI networks are then reconstructed using the graph neural network (GNN)-based autoencoder, which explores the node correlations in a self-prediction manner. In the downstream tasks, the pre-trained GNN encoder embeds the input networks into feature graphs, whereas a task-specific layer proceeds with the final prediction. To assess the performance of the proposed SMG method, benchmarking experiments are performed on three node-level tasks (identification of CGs, essential genes and healthy driver genes) and one graph-level task (identification of disease subnetwork) across eight PPI networks. Benchmarking experiments and performance comparison with existing state-of-the-art methods demonstrate the superiority of SMG on multi-omic feature engineering.

Associations Between Extreme Temperatures and Cardiovascular Cause-Specific Mortality: Results From 27 Countries.

BACKGROUND: Cardiovascular disease is the leading cause of death worldwide. Existing studies on the association between temperatures and cardiovascular deaths have been limited in geographic zones and have generally considered associations with total cardiovascular deaths rather than cause-specific cardiovascular deaths. METHODS: We used unified data collection protocols within the Multi-Country Multi-City Collaborative Network to assemble a database of daily counts of specific cardiovascular causes of death from 567 cities in 27 countries across 5 continents in overlapping periods ranging from 1979 to 2019. City-specific daily ambient temperatures were obtained from weather stations and climate reanalysis models. To investigate cardiovascular mortality associations with extreme hot and cold temperatures, we fit case-crossover models in each city and then used a mixed-effects meta-analytic framework to pool individual city estimates. Extreme temperature percentiles were compared with the minimum mortality temperature in each location. Excess deaths were calculated for a range of extreme temperature days. RESULTS: The analyses included deaths from any cardiovascular cause (32 154 935), ischemic heart disease (11 745 880), stroke (9 351 312), heart failure (3 673 723), and arrhythmia (670 859). At extreme temperature percentiles, heat (99th percentile) and cold (1st percentile) were associated with higher risk of dying from any cardiovascular cause, ischemic heart disease, stroke, and heart failure as compared to the minimum mortality temperature, which is the temperature associated with least mortality. Across a range of extreme temperatures, hot days (above 97.5th percentile) and cold days (below 2.5th percentile) accounted for 2.2 (95% empirical CI [eCI], 2.1-2.3) and 9.1 (95% eCI, 8.9-9.2) excess deaths for every 1000 cardiovascular deaths, respectively. Heart failure was associated with the highest excess deaths proportion from extreme hot and cold days with 2.6 (95% eCI, 2.4-2.8) and 12.8 (95% eCI, 12.2-13.1) for every 1000 heart failure deaths, respectively. CONCLUSIONS: Across a large, multinational sample, exposure to extreme hot and cold temperatures was associated with a greater risk of mortality from multiple common cardiovascular conditions. The intersections between extreme temperatures and cardiovascular health need to be thoroughly characterized in the present day-and especially under a changing climate.

Extreme Temperatures and Stroke Mortality: Evidence From a Multi-Country Analysis.

BACKGROUND: Extreme temperatures contribute significantly to global mortality. While previous studies on temperature and stroke-specific outcomes presented conflicting results, these studies were predominantly limited to single-city or single-country analyses. Their findings are difficult to synthesize due to variations in methodologies and exposure definitions. METHODS: Within the Multi-Country Multi-City Network, we built a new mortality database for ischemic and hemorrhagic stroke. Applying a unified analysis protocol, we conducted a multinational case-crossover study on the relationship between extreme temperatures and stroke. In the first stage, we fitted a conditional quasi-Poisson regression for daily mortality counts with distributed lag nonlinear models for temperature exposure separately for each city. In the second stage, the cumulative risk from each city was pooled using mixed-effect meta-analyses, accounting for clustering of cities with similar features. We compared temperature-stroke associations across country-level gross domestic product per capita. We computed excess deaths in each city that are attributable to the 2.5% hottest and coldest of days based on each city's temperature distribution. RESULTS: We collected data for a total of 3 443 969 ischemic strokes and 2 454 267 hemorrhagic stroke deaths from 522 cities in 25 countries. For every 1000 ischemic stroke deaths, we found that extreme cold and hot days contributed 9.1 (95% empirical CI, 8.6-9.4) and 2.2 (95% empirical CI, 1.9-2.4) excess deaths, respectively. For every 1000 hemorrhagic stroke deaths, extreme cold and hot days contributed 11.2 (95% empirical CI, 10.9-11.4) and 0.7 (95% empirical CI, 0.5-0.8) excess deaths, respectively. We found that countries with low gross domestic product per capita were at higher risk of heat-related hemorrhagic stroke mortality than countries with high gross domestic product per capita (P=0.02). CONCLUSIONS: Both extreme cold and hot temperatures are associated with an increased risk of dying from ischemic and hemorrhagic strokes. As climate change continues to exacerbate these extreme temperatures, interventional strategies are needed to mitigate impacts on stroke mortality, particularly in low-income countries.

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.

Tropical cyclone-specific mortality risks and the periods of concern: A multicountry time-series study.

BACKGROUND: More intense tropical cyclones (TCs) are expected in the future under a warming climate scenario, but little is known about their mortality effect pattern across countries and over decades. We aim to evaluate the TC-specific mortality risks, periods of concern (POC) and characterize the spatiotemporal pattern and exposure-response (ER) relationships on a multicountry scale. METHODS AND FINDINGS: Daily all-cause, cardiovascular, and respiratory mortality among the general population were collected from 494 locations in 18 countries or territories during 1980 to 2019. Daily TC exposures were defined when the maximum sustained windspeed associated with a TC was ≥34 knots using a parametric wind field model at a 0.5° × 0.5° resolution. We first estimated the TC-specific mortality risks and POC using an advanced flexible statistical framework of mixed Poisson model, accounting for the population changes, natural variation, seasonal and day of the week effects. Then, a mixed meta-regression model was used to pool the TC-specific mortality risks to estimate the overall and country-specific ER relationships of TC characteristics (windspeed, rainfall, and year) with mortality. Overall, 47.7 million all-cause, 15.5 million cardiovascular, and 4.9 million respiratory deaths and 382 TCs were included in our analyses. An overall average POC of around 20 days was observed for TC-related all-cause and cardiopulmonary mortality, with relatively longer POC for the United States of America, Brazil, and Taiwan (>30 days). The TC-specific relative risks (RR) varied substantially, ranging from 1.04 to 1.42, 1.07 to 1.77, and 1.12 to 1.92 among the top 100 TCs with highest RRs for all-cause, cardiovascular, and respiratory mortality, respectively. At country level, relatively higher TC-related mortality risks were observed in Guatemala, Brazil, and New Zealand for all-cause, cardiovascular, and respiratory mortality, respectively. We found an overall monotonically increasing and approximately linear ER curve of TC-related maximum sustained windspeed and cumulative rainfall with mortality, with heterogeneous patterns across countries and regions. The TC-related mortality risks were generally decreasing from 1980 to 2019, especially for the Philippines, Taiwan, and the USA, whereas potentially increasing trends in TC-related all-cause and cardiovascular mortality risks were observed for Japan. CONCLUSIONS: The TC mortality risks and POC varied greatly across TC events, locations, and countries. To minimize the TC-related health burdens, targeted strategies are particularly needed for different countries and regions, integrating epidemiological evidence on region-specific POC and ER curves that consider across-TC variability.

Global, regional, and national burden of heatwave-related mortality from 1990 to 2019: A three-stage modelling study.

BACKGROUND: The regional disparity of heatwave-related mortality over a long period has not been sufficiently assessed across the globe, impeding the localisation of adaptation planning and risk management towards climate change. We quantified the global mortality burden associated with heatwaves at a spatial resolution of 0.5°×0.5° and the temporal change from 1990 to 2019. METHODS AND FINDINGS: We collected data on daily deaths and temperature from 750 locations of 43 countries or regions, and 5 meta-predictors in 0.5°×0.5° resolution across the world. Heatwaves were defined as location-specific daily mean temperature ≥95th percentiles of year-round temperature range with duration ≥2 days. We first estimated the location-specific heatwave-mortality association. Secondly, a multivariate meta-regression was fitted between location-specific associations and 5 meta-predictors, which was in the third stage used with grid cell-specific meta-predictors to predict grid cell-specific association. Heatwave-related excess deaths were calculated for each grid and aggregated. During 1990 to 2019, 0.94% (95% CI: 0.68-1.19) of deaths [i.e., 153,078 cases (95% eCI: 109,950-194,227)] per warm season were estimated to be from heatwaves, accounting for 236 (95% eCI: 170-300) deaths per 10 million residents. The ratio between heatwave-related excess deaths and all premature deaths per warm season remained relatively unchanged over the 30 years, while the number of heatwave-related excess deaths per 10 million residents per warm season declined by 7.2% per decade in comparison to the 30-year average. Locations with the highest heatwave-related death ratio and rate were in Southern and Eastern Europe or areas had polar and alpine climates, and/or their residents had high incomes. The temporal change of heatwave-related mortality burden showed geographic disparities, such that locations with tropical climate or low incomes were observed with the greatest decline. The main limitation of this study was the lack of data from certain regions, e.g., Arabian Peninsula and South Asia. CONCLUSIONS: Heatwaves were associated with substantial mortality burden that varied spatiotemporally over the globe in the past 30 years. The findings indicate the potential benefit of governmental actions to enhance health sector adaptation and resilience, accounting for inequalities across communities.

Floods and cause-specific mortality in the UK: a nested case-control study.

BACKGROUND: Floods are the most frequent weather-related disaster, causing significant health impacts worldwide. Limited studies have examined the long-term consequences of flooding exposure. METHODS: Flood data were retrieved from the Dartmouth Flood Observatory and linked with health data from 499,487 UK Biobank participants. To calculate the annual cumulative flooding exposure, we multiplied the duration and severity of each flood event and then summed these values for each year. We conducted a nested case-control analysis to evaluate the long-term effect of flooding exposure on all-cause and cause-specific mortality. Each case was matched with eight controls. Flooding exposure was modelled using a distributed lag non-linear model to capture its nonlinear and lagged effects. RESULTS: The risk of all-cause mortality increased by 6.7% (odds ratio (OR): 1.067, 95% confidence interval (CI): 1.063-1.071) for every unit increase in flood index after confounders had been controlled for. The mortality risk from neurological and mental diseases was negligible in the current year, but strongest in the lag years 3 and 4. By contrast, the risk of mortality from suicide was the strongest in the current year (OR: 1.018, 95% CI: 1.008-1.028), and attenuated to lag year 5. Participants with higher levels of education and household income had a higher estimated risk of death from most causes whereas the risk of suicide-related mortality was higher among participants who were obese, had lower household income, engaged in less physical activity, were non-moderate alcohol consumers, and those living in more deprived areas. CONCLUSIONS: Long-term exposure to floods is associated with an increased risk of mortality. The health consequences of flooding exposure would vary across different periods after the event, with different profiles of vulnerable populations identified for different causes of death. These findings contribute to a better understanding of the long-term impacts of flooding exposure.

RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins.

RNA binding proteins (RBPs) are critical for the post-transcriptional control of RNAs and play vital roles in a myriad of biological processes, such as RNA localization and gene regulation. Therefore, computational methods that are capable of accurately identifying RBPs are highly desirable and have important implications for biomedical and biotechnological applications. Here, we propose a two-stage deep transfer learning-based framework, termed RBP-TSTL, for accurate prediction of RBPs. In the first stage, the knowledge from the self-supervised pre-trained model was extracted as feature embeddings and used to represent the protein sequences, while in the second stage, a customized deep learning model was initialized based on an annotated pre-training RBPs dataset before being fine-tuned on each corresponding target species dataset. This two-stage transfer learning framework can enable the RBP-TSTL model to be effectively trained to learn and improve the prediction performance. Extensive performance benchmarking of the RBP-TSTL models trained using the features generated by the self-supervised pre-trained model and other models trained using hand-crafting encoding features demonstrated the effectiveness of the proposed two-stage knowledge transfer strategy based on the self-supervised pre-trained models. Using the best-performing RBP-TSTL models, we further conducted genome-scale RBP predictions for Homo sapiens, Arabidopsis thaliana, Escherichia coli, and Salmonella and established a computational compendium containing all the predicted putative RBPs candidates. We anticipate that the proposed RBP-TSTL approach will be explored as a useful tool for the characterization of RNA-binding proteins and exploration of their sequence-structure-function relationships.

Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations.

Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.

Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues.

MOTIVATION: RNA N6-methyladenosine (m6A) in Homo sapiens plays vital roles in a variety of biological functions. Precise identification of m6A modifications is thus essential to elucidation of their biological functions and underlying molecular-level mechanisms. Currently available high-throughput single-nucleotide-resolution m6A modification data considerably accelerated the identification of RNA modification sites through the development of data-driven computational methods. Nevertheless, existing methods have limitations in terms of the coverage of single-nucleotide-resolution cell lines and have poor capability in model interpretations, thereby having limited applicability. RESULTS: In this study, we present CLSM6A, comprising a set of deep learning-based models designed for predicting single-nucleotide-resolution m6A RNA modification sites across eight different cell lines and three tissues. Extensive benchmarking experiments are conducted on well-curated datasets and accordingly, CLSM6A achieves superior performance than current state-of-the-art methods. Furthermore, CLSM6A is capable of interpreting the prediction decision-making process by excavating critical motifs activated by filters and pinpointing highly concerned positions in both forward and backward propagations. CLSM6A exhibits better portability on similar cross-cell line/tissue datasets, reveals a strong association between highly activated motifs and high-impact motifs, and demonstrates complementary attributes of different interpretation strategies. AVAILABILITY AND IMPLEMENTATION: The webserver is available at http://csbio.njust.edu.cn/bioinf/clsm6a. The datasets and code are available at https://github.com/zhangying-njust/CLSM6A/.

MULGA, a unified multi-view graph autoencoder-based approach for identifying drug-protein interaction and drug repositioning.

MOTIVATION: Identifying drug-protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety of computational approaches for DPI prediction have been proposed, key challenges, such as extendable and unbiased similarity calculation, heterogeneous information utilization, and reliable negative sample selection, remain to be addressed. RESULTS: To address these issues, we propose a novel, unified multi-view graph autoencoder framework, termed MULGA, for both DPI and drug repositioning predictions. MULGA is featured by: (i) a multi-view learning technique to effectively learn authentic drug affinity and target affinity matrices; (ii) a graph autoencoder to infer missing DPI interactions; and (iii) a new "guilty-by-association"-based negative sampling approach for selecting highly reliable non-DPIs. Benchmark experiments demonstrate that MULGA outperforms state-of-the-art methods in DPI prediction and the ablation studies verify the effectiveness of each proposed component. Importantly, we highlight the top drugs shortlisted by MULGA that target the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2), offering additional insights into and potentially useful treatment option for COVID-19. Together with the availability of datasets and source codes, we envision that MULGA can be explored as a useful tool for DPI prediction and drug repositioning. AVAILABILITY AND IMPLEMENTATION: MULGA is publicly available for academic purposes at https://github.com/jianiM/MULGA/.

Associations between long-term ozone exposure and small airways function in Chinese young adults: a longitudinal cohort study.

BACKGROUND: Increasing evidence is appearing that ozone has adverse effects on health. However, the association between long-term ozone exposure and lung function is still inconclusive. OBJECTIVES: To investigate the associations between long-term exposure to ozone and lung function in Chinese young adults. METHODS: We conducted a prospective cohort study among 1594 college students with a mean age of 19.2 years at baseline in Shandong, China from September 2020 to September 2021. Lung function indicators were measured in September 2020 and September 2021, including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), forced expiratory flow at the 25th, 50th, and 75th percentile of the FVC (FEF25, FEF50, and FEF75) and mean flow rate between 25% and 75% of the FVC (FEF25-75) were measured. Daily 10 km×10 km ozone concentrations come from a well-validated data-fusion approach. The time-weighted average concentrations in 12 months before the lung function test were defined as the long-term ozone exposure. The associations between long-term ozone exposure and lung function indicators in Chinese young adults were investigated using a linear mixed effects model, followed by stratified analyses regarding sex, BMI and history of respiratory diseases. RESULTS: Each interquartile range (IQR) (8.9 µg/m3) increase in long-term ozone exposure were associated with a -204.3 (95% confidence interval (CI): -361.6, -47.0) ml/s, -146.3 (95% CI: -264.1, -28.4) ml/s, and - 132.8 (95% CI: -239.2, -26.4) ml/s change in FEF25, FEF50, and FEF25-75, respectively. Stronger adverse associations were found in female participants or those with BMI ≥ 24 kg/m2 and history of respiratory diseases. CONCLUSION: Long-term exposure to ambient ozone is associated with impaired small airway indicators in Chinese young adults. Females, participants with BMI ≥ 24 kg/m2 and a history of respiratory disease have stronger associations.

Tumor Targeted Cuprous-Based Nanocomposite as Responsive Cascade Nanocatalyst for Efficient Tumor Synergistic Therapy.

The single-functionality of traditional chemodynamic therapy (CDT) reagents usually limits the therapeutic efficacy of cancer treatment. Synergistic nanocomposites that involve cascade reaction provide a promising strategy to achieve satisfactory anticancer effects. Herein, a cuprous-based nanocomposite (CCS@GOx@HA) is fabricated, which owns the tumor targeting ability and can undergo tumor microenvironment responsive cascade reaction to enhance the tumor therapeutic efficiency significantly. Surface modification of nanocomposite with hyaluronic acid enables the targeted delivery of the nanocomposite to cancer cells. Acid-triggered decomposition of nanocomposite in cancer cell results in the release of Cu+ , Se2- and GOx. The Cu+ improves the Fenton-like reaction with endogenous H2 O2 to generate highly toxic • OH for CDT. While GOx can not only catalyze the in situ generation of endogenous H2 O2 , but also accelerate the consumption of intratumoral glucose to reduce nutrient supply in tumor site. In addition, Se2- further improves the therapeutic effects of CDT by upregulating the reactive oxygen species (ROS) in tumor cells. Meanwhile, the surface modification endows the nanocomposite the good water dispersibility and biocompatibility. Moreover, in vitro and in vivo experiments demonstrate satisfactory anti-cancer therapeutic performance by the synergistic cascade function of CCS@GOx@HA than CDT alone.

Hollow CeO2-Base Nanozyme with Self-Accelerated Cascade Reactions for Combined Tumor Therapy.

Nanozymes have obvious advantages in improving the efficiency of cancer treatment. However, due to the lack of tissue specificity, low catalytic efficiency, and so on, their clinical applications are limited. Herein, the nanoplatform CeO2@ICG@GOx@HA (CIGH) with self-accelerated cascade reactions is constructed. The as-prepared nanozyme shows the superior oxidase (OXD)-like, superoxide dismutase (SOD)-like, catalase (CAT)-like, and peroxidase (POD)-like activities. At the same time, under 808 nm near-infrared (NIR) irradiation, the photodynamic and photothermal capabilities are also significantly enhanced due to the presence of indocyanine green (ICG). We demonstrate that the nanozyme CIGH can efficiently accumulate in the tumor and exhibit amplified cascade antitumor effects with negligible systemic toxicity through the combination of photodynamic therapy (PDT), photothermal therapy (PTT), chemodynamic therapy (CDT) and starvation therapy. The nanozyme prepared in this study provides a promising candidate for catalytic nanomedicines for efficient tumor therapy.

Licochalcone B specifically inhibits the NLRP3 inflammasome by disrupting NEK7-NLRP3 interaction.

The activation of the nucleotide oligomerization domain (NOD)-like receptor (NLR) family, pyrin domain-containing protein 3 (NLRP3) inflammasome is related to the pathogenesis of a wide range of inflammatory diseases, but drugs targeting the NLRP3 inflammasome are still scarce. In the present study, we demonstrated that Licochalcone B (LicoB), a main component of the traditional medicinal herb licorice, is a specific inhibitor of the NLRP3 inflammasome. LicoB inhibits the activation of the NLRP3 inflammasome in macrophages but has no effect on the activation of AIM2 or NLRC4 inflammasome. Mechanistically, LicoB directly binds to NEK7 and inhibits the interaction between NLRP3 and NEK7, thus suppressing NLRP3 inflammasome activation. Furthermore, LicoB exhibits protective effects in mouse models of NLRP3 inflammasome-mediated diseases, including lipopolysaccharide (LPS)-induced septic shock, MSU-induced peritonitis and non-alcoholic steatohepatitis (NASH). Our findings indicate that LicoB is a specific NLRP3 inhibitor and a promising candidate for treating NLRP3 inflammasome-related diseases.

Associations of Ambient Particulate Matter with Maternal Thyroid Autoimmunity and Thyroid Function in Early Pregnancy.

This prospective birth cohort study evaluated the association of exposure to PM2.5 (diameter ≤2.5 µm), PM1-2.5 (1-2.5 µm), and PM1 (≤1 µm) with maternal thyroid autoimmunity and function during early pregnancy. A total of 15,664 pregnant women were included at 6 to 13+6 gestation weeks in China from 2018 to 2020. Single-pollutant models using generalized linear models (GLMs) showed that each 10 µg/m3 increase in PM2.5 and PM1-2.5 was related with 6% (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.01, 1.12) and 15% (OR = 1.15, 95% CI: 1.08, 1.22) increases in the risk of thyroid autoimmunity, respectively. The odds of thyroid autoimmunity significantly increased with each interquartile range increase in PM2.5 and PM1-2.5 exposure (P for trend <0.001). PM1 exposure was not significantly associated with thyroid autoimmunity. GLM with natural cubic splines demonstrated that increases in PM2.5 and PM1-2.5 exposure were associated with lower maternal FT4 levels, while a negative association between PM1 and FT4 levels was found when exposure exceeded 32.13 µg/m3. Only PM2.5 exposure was positively associated with thyrotropin (TSH) levels. Our findings suggest that high PM exposure is associated with maternal thyroid disruption during the early pregnancy.

Long-term exposure to ambient ozone and adult-onset asthma: A prospective cohort study.

BACKGROUND: The association between long-term exposure to ozone (O3) and adult-onset asthma (AOA) remains inconclusive, and analysis of causality is lacking. OBJECTIVES: To examine the causal association between long-term O3 exposure and AOA. METHODS: A prospective cohort study of 362,098 participants was conducted using the UK Biobank study. Incident cases of AOA were identified using health administrative data of the National Health Services. O3 exposure at participants' residential addresses was estimated by a spatio-temporal model. Instrumental variable (IV) modelling was used to analyze the causal association between O3 exposure and AOA, by incorporating wind speed and planetary boundary layer height as IVs into time-dependent Cox model. Negative control outcome (accidental injury) was also used to additionally evaluate unmeasured confounding. RESULTS: During a mean follow-up of 11.38 years, a total of 10,973 incident AOA cases were identified. A U-shaped concentration-response relationship was observed between O3 exposure and AOA in the traditional Cox models with HR of 0.916 (95% CI: 0.888, 0.945) for O3 at low levels (<38.17 ppb), and 1.204 (95% CI: 1.168, 1.242) for O3 at high levels (≥38.17 ppb). However, in the IV analysis we only found a statistically significant association between high-level O3 exposure and AOA risk, but not for low-level O3 exposure. No significant associations between O3 exposure and accidental injury were observed. CONCLUSION: Our findings suggest a potential causal relationship between long-term exposure to high-level ambient O3 and increased risks of AOA.

Exposure to blue space surroundings and depressive symptoms in young Chinese adults: The mediating role of sleep.

OBJECTIVE: Existing evidence suggests that the natural environment can influence mental health. However, limited research has focused on the relationship between blue space and depressive symptoms in young adults. To investigate the association between blue space surroundings and depressive symptoms in young adults in China and explore the underlying mechanisms. METHODS: The study was conducted between September and November 2019, including 2,743 young adults from China. We assessed the exposure to blue space around participants' living environments during June, July, and August 2019 using the Modified Normalized Difference Water Index (MNDWI). Blue indexes were calculated for 300 m, 1000 m, and 3000 m circular buffer zones near residential environments. Logistic regression models were employed to explore the associations between blue space exposures (quartiles) and depressive symptoms, exploring potential mechanisms through structural equation modeling (SEM), while accounting for potential confounders. Stratification analysis was used to identify sensitive populations. RESULTS: Depressive symptoms were found in 148 (5.3%) of the 2,743 young adults in the study. We observed a negative correlation between depressive symptoms and average MNDWIs at participants' addresses (OR: 0.84; 95%CI: 0.72-0.98), within 300m (OR: 0.81; 95%CI: 0.70-0.95), 1000m (OR: 0.80; 95%CI: 0.69-0.93), and 3000m (OR:0.77; 95%CI: 0.66-0.89) buffer zones. Within the 1000m buffer zone, sleep was found to mediate 21% of the relationship between the presence of blue space and depressive symptoms. The stratified analysis revealed a stronger association between low MNDWI levels within the 1000m buffer zone and depressive symptoms in females (P < 0.05). Additionally, average MNDWI levels within the 3000m buffer zone were associated with depressive symptoms in both females and males. CONCLUSIONS: Blue space could improve depressive symptoms, particularly in females, with sleep playing a mediating role. Incorporating blue spaces into environmental planning is important for improving mental health.

Urban greenspaces and child blood pressure in China: Evidence from a large population-based cohort study.

BACKGROUND: With the world's population steadily shifting toward urban living, children's engagement with the natural environment seems to be diminishing. This raises significant concerns about the influence of urban greenspaces on the cardiovascular health of children. OBJECTIVE: To assess the association between urban greenspaces exposure and blood pressure (BP) in Chinese primary schoolchildren. METHODS: This prospective cohort study used data from the Children's growth environment, lifestyle, physical, and mental health development (COHERENCE) project in Guangzhou, China. Participants included 164,853 primary schoolchildren starting from 2016/17 to 2019/20 academic year. We assessed the surrounding greenspaces at home and school by using Sentinel-2 satellite data on the normalized difference vegetation index. Prehypertension and hypertension status were defined with BP above 90th to less than the 95th percentile, at or above the 95th percentile, respectively. The association of surrounding greenness with children's BP levels and risk of prehypertension/hypertension were examined using linear mixed-effects models and Cox proportional hazards model. RESULTS: Among 164,853 eligible children aged 7.21 (0.74) years, 89,190 (54.1%) were boys. Our results showed that average systolic and diastolic BP increased by 0.48 and 0.42 standard deviations, respectively, over the 3-year follow-up. We identified 23,225 new cases of prehypertension and 35,067 of hypertension status. An interquartile range increase both in home-, school- and home-school NDVI100m was significantly associated with a reduction of 0.018-0.037 in BP z-scores and a 2.7%-7.6% lower risk of hypertension. Additionally, family socioeconomic status modified the impact of home-school greenness on BP levels. Air pollution exhibited mediating effects solely in school-greenness-BP associations, while physical activity and children's BMI mainly mediated the relationships between home-greenness and BP. CONCLUSION: The findings of this large cohort study suggest that surrounding greenspaces are associated with lower BP levels and a decreased risk of prehypertension and hypertension in Chinese schoolchildren.