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Airborne microorganisms (AM) have significant environmental and health implications. Extensive studies have been conducted to investigate the factors influencing the composition and diversity of AM. However, the knowledge of AM with anthropogenic activities has not reach a consensus. In this study, we took advantage of the dramatic decline of outdoor anthropogenic activities resulting from COVID-19 lockdown to reveal their associations. We collected airborne particulate matter before and during the lockdown period in two cities. The results showed that it was fungal diversity and communities but not bacteria obviously different between pre-lockdown and lockdown samples, suggesting that airborne fungi were more susceptible to anthropogenic activities than bacteria. However, after the implementation of lockdown, the co-occurrence networks of both bacterial and fungal community became more complex, which might be due to the variation of microbial sources. Furthermore, Mantel test and correlation analysis showed that air pollutants also partly contributed to microbial alterations. Airborne fungal community was more affected by air pollutants than bacterial community. Notably, some human pathogens like Nigrospora and Arthrinium were negatively correlated with air pollutants. Overall, our study highlighted the more impacts of anthropogenic activities on airborne fungal community than bacterial community and advanced the understanding of associations between anthropogenic activities and AM.

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OBJECTIVE: Air pollution is increasing and threatening human health. The objective of this study is to investigate the population distribution characteristics of elderly osteoporosis fractures in Hebei Province and Xinjiang Uygur Autonomous Region and to analyze the effects of air pollutants on the number of elderly osteoporosis fracture inpatients in the two regions. METHOD: A retrospective collection of elderly osteoporosis fracture cases was conducted in selected hospitals in Hebei Province and Xinjiang Uygur Autonomous Region from January 1, 2018 to December 31, 2022. The chi-square test was used to compare the distributional characteristics of the population in the two regions. Additionally, we used a distributed lag nonlinear model (DLNM) in order to assess the effect of air pollutants on the number of daily hospital admissions of elderly osteoporosis fracture patients in different regions. RESULT: A total of 19,203 elderly osteoporosis fracture patients were included in the study. The average age of these patients was 76.66 ± 7.55 years, and the majority of them were female (13,514 instances, 70.37%). The disparities in age distribution (χ2 = 133.9 p < 0.001), fracture site (χ2 = 62.0 p < 0.001), and hospitalization cost (Z = -15.635 p < 0.001) between the two regions were statistically significant. The lag effect curves of PM2.5, PM10, and NO2 on the number of elderly osteoporosis fracture hospitalizations in Xinjiang Uygur Autonomous Region exhibited a similar pattern resembling a "W"-shaped curve. All three pollutants reached their highest values after a lag time of 14 days (PM2.5: RR = 1.053, 95% CI: 1.031, 1.074; PM10: RR = 1.031, 95% CI: 1.018, 1.043; NO2: RR = 1.125, 95% CI: 1.070, 1.182). In Hebei Province, the largest impacts of PM2.5 and PM10 were observed after a lag of 14 days (PM2.5: RR = 1.022, 95% CI: 1.013, 1.028; PM10: RR = 1.013, 95% CI: 1.008, 1.018). Similarly, the maximum effect of NO2 was observed after a lag of 11 days (RR = 1.020, 95% CI: 1.010, 1.028). CONCLUSION: There were differences in the epidemiological characteristics of hospitalized patients with osteoporosis fractures between the two regions, PM2.5, PM10, and NO2 increased the number of hospitalizations for osteoporosis fractures. Exposure to air pollutants such as PM2.5 increases the risk of osteoporosis fractures in the elderly population.

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Assessing the iron and steel industry's (ISI) impact on climate change and environmental health is vital, particularly in China, where this sector significantly influences air quality and CO2 emissions. There is a lack of comprehensive analyses that consider the environmental and health burdens of manufacturing processes for ISI enterprises. Here, we present an integrated emission inventory that encompasses air pollutants and CO2 emissions from 811 ISI enterprises and five key manufacturing processes in 2020. Our analysis shows that sintering is the primary source of air pollution in the ISI. It contributes 71% of SO2, 73% of NO x , and 54% of PM2.5 emissions. On the other hand, 81% of total CO2 emissions come from blast furnaces. Significantly, the contributions of ISI have resulted in an increase of 3.6 µg m-3 in national population-weighted PM2.5 concentration, causing approximately 59,035 premature deaths in 2020. Emissions from Hebei, Jiangsu, Shandong, Shanxi, and Inner Mongolia provinces contributed to 48% of PM2.5-related deaths in China. Moreover, the transportation of air pollutants across provincial borders highlights a concerning trend of environmental health inequality. Based on the research findings, it is crucial for ISI manufacturers to prioritize the removal of outdated production capacities and adopt energy-efficient and advanced techniques, along with ultra-low emission technologies. This is particularly important for those manufacturers with substantial environmental footprints. These transformative actions are essential in mitigating the environmental and health impacts in the affected regions.

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Air pollution is a leading environmental health risk factor, and in situ toxicity assessment is urgently needed. Bacteria-based bioassays offer cost-effective and rapid toxicity assessments. However, the application of these bioassays for air toxicity assessment has been challenging, due to the instability of bacterial survival and functionality when directly exposed to air pollutants. Here, we developed an approach employing self-assembly passive colonization hydrogel (SAPCH) for in situ air toxicity assessment. The SAPCH features a core-shell structure, enabling the quantitatively immobilization of bacteria on its shell while continuously provides nutrients from its core. An antimicrobial polyelectrolyte layer between the core and shell confines bacteria to the air-liquid interface, synchronizing bacterial survival with exposure to air pollutants. The SAPCH immobilized a battery of natural and recombinant luminescent bacteria, enabling simultaneous detection of various toxicological endpoints (cytotoxicity, genotoxicity and oxidative stress) of air pollutants within 2 h. Its sensitivity was 3-5 orders of magnitude greater than that of traditional liquid-phase toxicity testing, and successfully evaluating the toxicity of volatile organic compounds and combustion smoke. This study presents a method for in situ, rapid, and economical toxicity assessment of air pollution, making a significant contribution to future air quality monitoring and control.

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We estimated the joint effect of particulate matter ≤ 2.5 µm in diameter (PM2.5), nitrogen dioxide (NO2), seasonal temperature, noise, greenness, light at night, and neighborhood socioeconomic status (NSES) on body mass index (BMI) in a mixture context among 194,966 participants from the Nurses' Health Study (NHS) and Nurses' Health Study II (NHSII) over 30 years. BMI was calculated from self-reported weight and height. Single- and multi-exposure generalized estimating equations models were used to estimate the difference in BMI per interquartile range (IQR) increase of environmental factors, and quantile g-computation methods were used to estimate joint associations. In both cohorts, we consistently observed positive associations of BMI with PM2.5 and NO2 concentrations as well as negative associations with light at night and NSES regardless modeling approach. A positive association with noise was only observed in NHS. Negative associations with greenness and winter temperature were only observed in NHSII. Overall, the changes in BMI per quintile increase in all eight exposures were -0.11 (-0.13, -0.08) in NHS and -0.39 (-0.41, -0.37) in NHSII, which were largely driven by air pollution and nighttime noise (18-45 %) in the positive direction and NSES (>70 %) in the negative direction. Future intervention on environmental factors, especially reducing PM2.5, NO2 and noise or improving the NSES, might be helpful to lower BMI.

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The booming of China's primary aluminum industry (PAI) brought substantial emissions of hazardous air pollutants (HAPs) and greenhouse gases (GHGs). By using life cycle assessment and bottom-up method, a comprehensive emission inventory for multiple typical HAPs and GHGs from China's PAI during 1990-2021 was developed and explored for the first time. Our results show that spatial-temporal emissions trends of HAPs and GHGs from PAI in China diverse significantly. The conventional atmospheric pollutants (including SO2, NOx and particulate matter (PM)), fluoride and per fluorinated compound (PFCs) had been effectively suppressed since 2007 due to the implementation of various environmental policies; while, emissions of CO, VOCs, CH4, heavy metals and CO2 had increased at different rates unexpectedly. From the spatial distribution perspective, Henan, Shanxi, Guizhou, Guangxi and Shandong dominated the emissions of PAI in China, but with consumption expansion and environmental constrains, PAI plants start to expand to northwest and southwest areas where are richer in sufficient and cheaper power resources, thus bring significant emission increasing there, particular for conventional atmospheric pollutants in northwest and CO and VOCs in southwest China. By underlying driving forces of PAI emissions, results show that end-of-pipe control measures at various stages have played different roles to reduce emissions of the concerned species at each period, but its reduction effect diminished gradually. Future reduction should seek underlying changes in production technology and energy system. Under constrains of environmental regulation and resource endowment, promoting circular economic development for PAI would be a key strategy to reduce HAPs and GHGs emissions simultaneously in PAI.

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With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

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Depression is a major contributor to the global burden of disease. There is limited understanding of how environmental exposures may contribute to depression etiology. We used Wave IV of the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine associations between low-level ambient air pollution exposure and depressed mood in a generally healthy population of over 10,000 24-32 year olds. Annual mean PM2.5 levels in the 2008-2009 study were close to the current U.S. standard. In fully adjusted quasi-binomial logistic regression models, there were no meaningful associations between IQR increases in air pollutant and change in depressed mood status regardless of specific pollutant or moving average lags. In interaction effects models, an IQR increase in lag day 0-30 PM2.5 resulted in 1.20 (95% CI, 1.02-1.41) times higher likelihood of having depressed mood, but only for persons with chronic lung disease (interaction P=0.04); the association was null for participants without chronic lung disease (OR 0.98, 95% CI, 0.91, 1.05). Our findings suggest that among persons with a lifetime history of chronic lung disease, greater exposure to even low-level PM2.5, PM10, and sulfate may be associated with modest increases in the likelihood of having depressed mood.

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BACKGROUND: Human exposure to air pollution involves complex mixtures of multiple correlated air pollutants. To date, very few studies have assessed the combined effects of exposure to multiple air pollutants on breast cancer (BC) risk. OBJECTIVES: We aimed to assess the association between combined exposures to multiple air pollutants and breast cancer risk. METHODS: The study was based on a case-control study nested within the French E3N cohort (5222 incident BC cases/5222 matched controls). For each woman, the average of the mean annual exposure to eight pollutants (benzo(a)oyrene, cadmium, dioxins, polychlorinated biphenyls (PCB153), nitrogen dioxide (NO2), ozone, particulate matter and fine particles (PMs)) was estimated from cohort inclusion in 1990 to the index date. We used the Bayesian Profile Regression (BPR) model, which groups individuals according to their exposure and risk levels, and assigns a risk to each cluster identified. The model was adjusted on a combination of matching variables and confounders to better consider the design of the nested case-control study. Odds ratios (OR) and their 95 % credible intervals (CrI) were estimated. RESULTS: Among the 21 clusters identified, the cluster characterised by low exposures to all pollutants, except ozone, was taken as reference. A consistent increase in BC risk compared to the reference cluster was observed for 3 clusters: cluster 9 (OR=1.61; CrI=1.13,2.26), cluster 16 (OR=1.59; CrI=1.10,2.30) and cluster 15 (OR=1.38; CrI=1.00,1.88) characterised by high levels of NO2, PMs and PCB153. The other clusters showed no consistent association with BC. DISCUSSION: This is the first study assessing the effect of exposure to a mixture of eight air pollutants on BC risk, using the BPR approach. Overall, results showed evidence of a positive joint effect of exposure to high levels to most pollutants, particularly high for NO2, PMs and PCB153, on the risk of BC.

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Background: Traffic-related air pollution (TRAP) has significant public health implications and a wide range of adverse health effects, including cardiovascular, respiratory, pulmonary, and other health problems. This study aimed to determine the public health impacts of traffic-related air pollution across the world that can be used as an input for protecting human health. Methods: This study considered studies conducted across the world and full-text articles written in English. The articles were searched using a combination of Boolean logic operators (AND, OR, and NOT), MeSH, and keywords from the included electronic databases (SCOPUS, PubMed, EMBASE, Web of Science, CINAHL, and Google Scholars). The quality assessment of the articles was done using JBI tools to determine the relevance of each included article to the study. Results: In this study, 1 282 032 participants ranging from 19 to 452 735 were included in 30 articles published from 2010 to 2022. About 4 (13.3%), 9 (30.0%), 12 (40.0%), 8 (26.7%), 2 (6.7%), 15 (50.0%), 3 (10.0%), 3 (10.0%) 1 (3.3%), and 3 (10.0%) of articles reported the association between human health and exposure to CO, PM10, PM2.5, NOx, NO, NO2, black carbon, O3, PAH, and SO2, respectively. Respiratory diseases, cancer, cognitive function problems, preterm birth, blood pressure and hypertension, diabetes, allergies and sensitization, coronary heart disease, dementia incidence, and hemorrhagic stroke were associated with exposure to TRAP. Conclusions: Exposure to nitrogen dioxide, nitrogen oxide, sulfur dioxide, and fine particulate matter was associated with various health effects. This revealed that there is a need for the concerned organizations to respond appropriately.

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Poor indoor air quality poses significant health risks. This study addresses the gap in knowledge regarding the prevalence of indoor air pollutants in remote and rural First Nation communities in north-central British Columbia, Canada. Dust samples from 75 homes were collected and analysed for house dust mites, pet allergens, mould antigens, and bacterial endotoxins. Indoor air quality parameters, including carbon monoxide, carbon dioxide, particulate matter, temperature, and humidity, were measured. A detailed questionnaire on household characteristics and potential pollutant sources was administered. Homes exhibited exposure to multiple pollutants, with wood stove smoke identified as a primary source. Felis domesticus (cat allergen) and Canis familiaris (dog allergen) were prevalent, with detectable levels in 64% and 60% of homes, respectively. Bacterial endotoxins were present in all households. One-third of homes exceeded recommended thresholds for 3 or more pollutants. This study provides critical insights into the prevalence and magnitude of indoor air pollutants, contributing to a broader initiative to characterise respiratory health in First Nations communities. While many homes in First Nations communities had acceptable air quality, one-third of homes exceeded thresholds for 3 or more pollutants. The results can guide ongoing community efforts to address housing concerns and advocate for increased federal funding.

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Background: Tracheal, Bronchus, and Lung (TBL) cancer continues to represent the majority of cancer-related incidence and mortality in United States (U.S.). While air pollutants are considered essential risk factors, both global and national average concentrations of major harmful air pollutants have significantly decreased over the decades. Green space may have a beneficial effect on human health. Methods: We obtained data on national and state-level burden of TBL cancer, the annual average concentration of main air pollutants, and levels of green spaces in 2007, 2013, and 2019. According to generalized estimating equation (GEE), we examine the associations among incidence and mortality of TBL cancer, air pollutants, and greenspaces, represented by the Normalized Difference Vegetation Index (NDVI) in different age groups with models adjusted with meteorological, and socio-demographic. We observed additional effects of the interaction between the NDVI, Ozone, PM2.5, and other factors, which helped us to interpret and understand our results. Also, we collated states that witnessed net increments in forest coverage and conducted the same analysis separately. Results: In our analysis, the majority of associations between NDVI and air pollutants with TBL cancer remained significantly positive, particularly noticeable among individuals aged 20 to 54. However, our findings did not explore air pollution as a potential mediator between greenspace exposure and TBL cancer. While the associations of PM2.5 with TBL cancer remained positive, the other four pollutants showed positive but statistically insignificant associations. Our interaction analysis yielded that there were positive associations between NDVI and ozone, PM2.5, and tobacco use. Max NDVI acts as a protective factor along with high HDI. Additionally, PM2.5 and HDI also showed a negative association. In 18 states with more forest, NDVI acts as a protective factor along with higher health care coverage, better health status, and participation in physical activities. Conclusion: In the state-level of U.S., the effects of total greenspace with TBL cancer are mixed and could be modified by various socio-economic factors. PM2.5 has a direct correlation with TBL cancer and the effects can be influenced by underlying socioeconomic conditions.

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The health outcomes of an individual are shaped by a combination of genetic predisposition and environmental influences. While some diseases stem solely from environmental factors, others like atopic eczema, also known as neurodermatitis or atopic dermatitis, are multifaceted, with environmental variables playing a significant role in its initiation and severity. Atopic eczema is a prevalent chronic condition observed globally, particularly in Western industrialized nations where its prevalence is estimated to range from 2.5% to 3.5% in adults and 10% to 15% among children. The increasing incidence of atopic eczema in industrialized countries over recent decades suggests that this trend may be due to environmental changes rather than genetic predispositions. Therefore, by thoroughly examining environmental factors and their role in atopic dermatitis, one may be able to gain a better understanding of its disease pattern and develop possible preventative measures. This article provides a comprehensive analysis of how the surrounding environment contributes to the pathogenesis of atopic eczema.

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Driving co-abatement of Greenhouse Gas (GHGs) and Air Pollutants (APs) in the city level is crucial for fostering societal green and low-carbon transitions, yet comprehensive and refined researches at this level remain limited. To facilitate urban fine management of GHGs control and APs reduction, this study targeted nine categories of anthropogenic emission sources in Shanghai, a typical megacity of China, analyzing the co-benefits of three types of GHGs (CO2, CH4, N2O) and seven types of APs (SO2, NOx, CO, VOCs, NH3, PM2.5, PM10) via emissions flow, spatial distribution, hotspot regions identification, and scenario prediction. Results highlighted the source heterogeneity of different types and significant contributions of energy consumption. CO2 emissions showed a strong spatial correlation with SO2, NOx, and CO, followed by VOCs and PM. Hotspot regions for CO2-VOCs, CO2-NOx and CO2-SO2 co-abatement included power plants, petrochemical enterprises and chemical industrial parks in the southern coastal areas, iron and steel enterprises and power plants in the northern coastal areas, and airport areas in the central and eastern coastal areas, presenting great potential maximum reduction benefits. Achieving positive co-benefits in industrial sector would depend on the steady decline of CO2 emissions in power generation and steel industries. Introducing carbon capture devices and improving energy efficiency would be more beneficial to CO2 emission reduction, while increasing the share of clean energy and phasing out outdated vehicles, machinery, or production capacities are more effective in reducing APs. These mitigation measures could achieve 68.8 % and 47.6 % reduction for CO2 and APs by 2050, respectively, and the co-effect of CO2 and APs emission reduction would gradually increase with the continuous implementation of these measures.

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The study examines the relationship between air quality, meteorological factors, and COVID-19 cases in Cheras, Kuala Lumpur, and Kelapa Gading, North Jakarta. Analyzing data from 2020 and 2021, the research found notable correlations: COVID-19 cases in Cheras were positively associated with relative humidity (RH) and carbon monoxide (CO) but negatively with ozone (O3) and RH in different years. In Kelapa Gading, COVID-19 cases were positively correlated with pollutants like sulfur dioxide (SO2) and CO, while ambient temperature (AT) showed a negative correlation. The enforcement of social restrictions notably reduced air pollution, affecting COVID-19 spread. Predictive models for PM2.5 levels using robust regression techniques showed strong performance in Kuala Lumpur (R² > 0.9) but exhibited overfitting tendencies in Jakarta, suggesting the need for a longer study period for more accurate results.

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The burden of rheumatoid arthritis (RA) has gradually elevated, increasing the need for medical resource redistribution. Forecasting RA patient arrivals can be helpful in managing medical resources. However, no relevant studies have been conducted yet. This study aims to construct a long short-term memory (LSTM) model, a deep learning model recently developed for novel data processing, to forecast RA patient arrivals considering meteorological factors and air pollutants and compares this model with traditional methods. Data on RA patients, meteorological factors and air pollutants from 2015 to 2022 were collected and normalized to construct moving average (MA)- and autoregressive (AR)-based and LSTM models. After data normalization, the root mean square error (RMSE) was adopted to evaluate models' forecast ability. A total of 2422 individuals were enrolled. Not using the environmental data, the RMSEs of the MA- and AR-based models' test sets are 0.131, 0.132, and 0.117 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they are 0.110, 0.130, and 0.112 for the univariate LSTM models. Considering meteorological factors and air pollutants, the RMSEs of the MA- and AR-based model test sets were 0.142, 0.303, and 0.164 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they were 0.108, 0.119, and 0.109 for the multivariable LSTM models. Our study demonstrated that LSTM models can forecast RA patient arrivals more accurately than MA- and AR-based models for datasets of all three sizes. Considering the meteorological factors and air pollutants can further improve the forecasting ability of the LSTM models. This novel method provides valuable information for medical management, the optimization of medical resource redistribution, and the alleviation of resource shortages.

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BACKGROUND: Particulate matter exposure (PM) is a cause of aerodigestive disease globally. The destruction of the World Trade Center (WTC) exposed first responders and inhabitants of New York City to WTC-PM and caused obstructive airways disease (OAD), gastroesophageal reflux disease (GERD) and Barrett's Esophagus (BE). GERD not only diminishes health-related quality of life but also gives rise to complications that extend beyond the scope of BE. GERD can incite or exacerbate allergies, sinusitis, bronchitis, and asthma. Disease features of the aerodigestive axis can overlap, often necessitating more invasive diagnostic testing and treatment modalities. This presents a need to develop novel non-invasive biomarkers of GERD, BE, airway hyperreactivity (AHR), treatment efficacy, and severity of symptoms. METHODS: Our observational case-cohort study will leverage the longitudinally phenotyped Fire Department of New York (FDNY)-WTC exposed cohort to identify Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BAD-BURN). Our study population consists of n = 4,192 individuals from which we have randomly selected a sub-cohort control group (n = 837). We will then recruit subgroups of i. AHR only ii. GERD only iii. BE iv. GERD/BE and AHR overlap or v. No GERD or AHR, from the sub-cohort control group. We will then phenotype and examine non-invasive biomarkers of these subgroups to identify under-diagnosis and/or treatment efficacy. The findings may further contribute to the development of future biologically plausible therapies, ultimately enhance patient care and quality of life. DISCUSSION: Although many studies have suggested interdependence between airway and digestive diseases, the causative factors and specific mechanisms remain unclear. The detection of the disease is further complicated by the invasiveness of conventional GERD diagnosis procedures and the limited availability of disease-specific biomarkers. The management of reflux is important, as it directly increases risk of cancer and negatively impacts quality of life. Therefore, it is vital to develop novel noninvasive disease markers that can effectively phenotype, facilitate early diagnosis of premalignant disease and identify potential therapeutic targets to improve patient care. TRIAL REGISTRATION: Name of Primary Registry: "Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BADBURN)". Trial Identifying Number: NCT05216133 . Date of Registration: January 31, 2022.

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By linking medical real-world data with geographic information, it is possible to evaluate the impact on hospitalization based on these characteristics, such as patient residence information and disease and medical information. In this study, environmental exposure to air pollutants was reported as a risk factor, and predictive models were used to examine factors affecting health. The importance of the characteristics appeared according to the disease, and overall, the patient profile at the time of admission, such as ADL, was shown to be high, but for respiratory diseases, the cumulative concentration of air pollutants NO2, SPM, and NOx for one year before the onset of admission was the top risk factor for long-term hospitalization, suggesting the influence of exposure due to environmental factors.

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Published studies on outdoor air pollution and tuberculosis risk have shown heterogeneous results. Discrepancies in prior studies may be partially explained by the limited geographic scope, diverse exposure times, and heterogeneous statistical methods. Thus, we conducted a multi-province, multi-city time-series study to comprehensively investigate this issue. We selected 67 districts or counties from all geographic regions of China as study sites. We extracted data on newly diagnosed pulmonary tuberculosis (PTB) cases, outdoor air pollutant concentrations, and meteorological factors in 67 sites from January 1, 2014 to December 31, 2019. We utilized a generalized additive model to evaluate the relationship between ambient air pollutants and PTB risk. Between 2014 and 2019, there were 172,160 newly diagnosed PTB cases reported in 67 sites. With every 10-µg/m3 increase in SO2, NO2, PM10, PM2.5, and 1-mg/m3 in CO, the PTB risk increased by 1.97% [lag 0 week, 95% confidence interval (CI): 1.26, 2.68], 1.30% (lag 0 week, 95% CI: 0.43, 2.19), 0.55% (lag 8 weeks, 95% CI: 0.24, 0.85), 0.59% (lag 10 weeks, 95% CI: 0.16, 1.03), and 5.80% (lag 15 weeks, 95% CI: 2.96, 8.72), respectively. Our results indicated that ambient air pollutants were positively correlated with PTB risk, suggesting that decreasing outdoor air pollutant concentrations may help to reduce the burden of tuberculosis in countries with a high burden of tuberculosis and air pollution.

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BACKGROUND: Existing epidemiological studies have indicated a correlation between air pollutants and the occurrence of mental disorders. However, it is difficult to estimate the causal relationship between the two because of the limitations of traditional epidemiological research. In our study, we aimed to extensively explore the causal relationship between five types of air pollutants and four types of mental disorders. METHODS: Based on the IEU OPEN GWAS database, we performed a two-sample MR analysis. The primary analysis method utilized was the inverse variance weighted (IVW) method, supplemented by the MR-Egger method and the weighted median method. Additionally, we conducted sensitivity analyses with the Cochran's Q statistic method, the leave-one-out method, and the MR-Egger intercept. We chose at least 4 GWAS datasets for each of the four psychiatric diseases and conducted a meta-analysis of our results of the MR analysis. RESULTS: The meta-analysis's findings demonstrated a causal link between depression and PM2.5 (OR=1.020, 95 %CI: (1.010,1.030), P=0.001). PM10 and schizophrenia are also causally related (OR=1.136, 95 %CI: (1.034,1.248), P=0.008). Nitrogen oxides and bipolar disorder have a causal relationship (OR=1.002, 95 %CI: (1.000,1.003), P=0.022). Nitrogen oxides and schizophrenia have a high causal association (OR=1.439, 95 %CI: (1.183,1.752), P<0.001). CONCLUSION: This study observed a causal association between increased concentrations of PM2.5, PM10, and nitrogen oxides and the occurrence of depression, schizophrenia, and bipolar disorder. Our research findings have certain guiding implications for treating and preventing mental disorders.

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