Deep learning automatically assesses 2-µm laser-induced skin damage OCT images.

The present study proposed a noninvasive, automated, in vivo assessment method based on optical coherence tomography (OCT) and deep learning techniques to qualitatively and quantitatively analyze the biological effects of 2-µm laser-induced skin damage at different irradiation doses. Different doses of 2-µm laser irradiation established a mouse skin damage model, after which the skin-damaged tissues were imaged non-invasively in vivo using OCT. The acquired images were preprocessed to construct the dataset required for deep learning. The deep learning models used were U-Net, DeepLabV3+, PSP-Net, and HR-Net, and the trained models were used to segment the damage images and further quantify the damage volume of mouse skin under different irradiation doses. The comparison of the qualitative and quantitative results of the four network models showed that HR-Net had the best performance, the highest agreement between the segmentation results and real values, and the smallest error in the quantitative assessment of the damage volume. Based on HR-Net to segment the damage image and quantify the damage volume, the irradiation doses 5.41, 9.55, 13.05, 20.85, 32.71, 52.92, 76.71, and 97.24 J/cm² corresponded to a damage volume of 4.58, 12.56, 16.74, 20.88, 24.52, 30.75, 34.13, and 37.32 mm³. The damage volume increased in a radiation dose-dependent manner.

Ambient air pollution exposure associated with glucose homeostasis during pregnancy and gestational diabetes mellitus.

BACKGROUND: To investigate the effects of air pollution exposure during pregnancy on the indicators of glucose homeostasis and gestational diabetes mellitus (GDM). METHODS: We conducted a birth cohort study in Foshan, China during 2015-2019. Oral glucose tolerance test (OGTT) was administered to each participant during pregnancy. GDM was defined according to the International Association of Diabetes and Pregnancy Study Groups criteria (IADPSG). Air pollutant (fine particulate matter (PM2.5), particulate matter with an aerodynamic diameter of 10 µm or less (PM10), sulfate dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3)) concentrations from the air monitoring stations in Foshan were used to estimate individual air pollutant exposure during the first two trimesters. Linear and logistic regression models were employed to estimate the associations between air pollution exposure during the first two trimesters and OGTT glucose levels and GDM. RESULTS: Of 12,842 pregnant women, 3055 (23.8%) had GDM. A 10 µg/m3 increase in PM2.5, PM10 and SO2 during trimester 1, trimester 2 and two trimesters were associated with 0.07 mmol/L to 0.29 mmol/L increment in OGTT-fasting glucose levels in single-pollutant model. A 10 µg/m3 increase in NO2 and O3 during two trimesters were associated with 0.15 mmol/L and 0.12 mmol/L decrease in OGTT-fasting glucose in single-pollutant model. However, no significant or weaker effects of O3 during two trimesters on OGTT-fasting glucose were observed in two-pollutant models. Moreover, exposure to PM2.5, PM10 and SO2 were associated with increased risk of GDM in both single- and two-pollutant models. CONCLUSIONS: Our study suggests PM2.5, PM10 and SO2 exposure during the first two trimesters might increase the risk of GDM.

Projections of hepatitis A virus infection associated with flood events by 2020 and 2030 in Anhui Province, China.

Assessing and responding to health risk of climate change is important because of its impact on the natural and societal ecosystems. More frequent and severe flood events will occur in China due to climate change. Given that population is projected to increase, more people will be vulnerable to flood events, which may lead to an increased incidence of HAV infection in the future. This population-based study is going to project the future health burden of HAV infection associated with flood events in Huai River Basin of China. The study area covered four cities of Anhui province in China, where flood events were frequent. Time-series adjusted Poisson regression model was developed to quantify the risks of flood events on HAV infection based on the number of daily cases during summer seasons from 2005 to 2010, controlling for other meteorological variables. Projections of HAV infection in 2020 and 2030 were estimated based on the scenarios of flood events and demographic data. Poisson regression model suggested that compared with the periods without flood events, the risks of severe flood events for HAV infection were significant (OR = 1.28, 95 % CI 1.05-1.55), while risks were not significant from moderate flood events (OR = 1.16, 95 % CI 0.72-1.87) and mild flood events (OR = 1.14, 95 % CI 0.87-1.48). Using the 2010 baseline data and the flood event scenarios (one severe flood event), increased incidence of HAV infection were estimated to be between 0.126/105 and 0.127/105 for 2020. Similarly, the increased HAV infection incidence for 2030 was projected to be between 0.382/105 and 0.399/105. Our study has, for the first time, quantified the increased incidence of HAV infection that will result from flood events in Anhui, China, in 2020 and 2030. The results have implications for public health preparation for developing public health responses to reduce HAV infection during future flood events.

Effect and attributable burden of hot extremes on bacillary dysentery in 31 Chinese provincial capital cities.

BACKGROUND: High atmospheric temperature has been associated with the occurrence of bacillary dysentery (BD). Recent studies have suggested that hot extremes may influence health outcomes, however, none have examined the association between hot extremes and BD risk, especially at the national level. OBJECTIVES: To assess the effect and attributable burden of hot extremes on BD cases and to identify populations at high risk of BD. METHODS: Daily incident BD data of 31 provincial capital cities from 2010 to 2018 were collected from the Chinese Center for Disease Control and Prevention, weather data was obtained from the fifth generation of the European Re-Analysis Dataset. Three types of hot extremes, including hot day, hot night, and hot day and night, were defined according to single or sequential occurrence of daytime hot and nighttime hot within 24 h. A two-stage analytical strategy combined with distributed lag non-linear models (DLNM) was used to evaluate city-specific associations and national pooled estimates. RESULTS: Hot extremes were significantly associated with the risk of BD on lagged 1-6 days. The overall cumulative relative risk (RR) was 1.136 [95% confidence interval (CI): 1.022, 1.263] for hot day, 1.181 (95% CI: 1.019, 1.369) for hot night, and 1.154 (95% CI: 1.038, 1.283) for hot day and night. Northern residents, females, and children younger than or equal to 14 years old were vulnerable under hot night, southern residents were vulnerable under hot day, and males were vulnerable under hot day and night. 1.854% (95% CI: 1.294%, 2.205%) of BD cases can be attributable to hot extremes, among which, hot night accounted for a large proportion. CONCLUSIONS: Hot extremes may significantly increase the incidence risk and disease burden of BD. Type-specific protective measures should be taken to reduce the risk of BD, especially in those we found to be particularly vulnerable.

Daily Temperature and Bacillary Dysentery: Estimated Effects, Attributable Risks, and Future Disease Burden in 316 Chinese Cities.

BACKGROUND: Bacillary dysentery (BD) remains a significant public health issue, especially in developing countries. Evidence assessing the risk of BD from temperature is limited, particularly from national studies including multiple locations with different climatic characteristics. OBJECTIVES: We estimated the effect of temperature on BD across China, assessed heterogeneity and attributable risks across cities and regions, and projected the future risk of BD under climate change. METHODS: Daily BD surveillance and meteorological data over 2014-2016 were collected from the Chinese Center for Disease Control and Prevention and the China Meteorology Administration, respectively. A two-stage statistical model was used to estimate city-specific temperature-BD relationships that were pooled to derive regional and national estimates. The risk of BD attributable to temperature was estimated, and the future burden of BD attributable to temperature was projected under different climate change scenarios. RESULTS: A positive linear relationship for the pooled effect was estimated at the national level. Subgroup analyses indicate that the estimated effect of temperature on BD was similar by age (≤5y or >5y) and gender. At baseline, estimated attributable risks for BD due to average daily mean temperatures above the 50th percentile were highest for the Inner Mongolia (16%), Northeast China (14%), and Northern China (13%). Most of the individual cities in the same regions and most of the cities in the Northwest, Southern, and Southwest regions, had high attributable risks (≥5%). The Northern, Northeast, Inner Mongolia, Northwest, and Southern China regions were identified as high risk for future BD, with estimated increases by the 2090s compared with baseline of 20% (95% confidence interval: 11%, 27%), 15% (6%, 20%), 15% (-1%, 22%), 12% (1%, 19%), and 11% (5%, 15%), respectively, under Representative Concentration Pathway 8.5. CONCLUSIONS: The positive association between temperature and BD in different climatic regions of China, and the projection for increased risk due to climate change, support efforts to mitigate future risks. https://doi.org/10.1289/EHP5779.

Hourly associations between ambient temperature and emergency ambulance calls in one central Chinese city: Call for an immediate emergency plan.

BACKGROUND: Most studies examining the short-term effects of temperature on health were based on the daily scale, few were at the hourly level. Revealing the relationship between unfavorable temperatures on an hourly basis and health is conducive to the development of more accurate extreme temperature early warning systems and reasonable dispatch of ambulances. METHODS: Hourly data on temperature, air pollution (including PM2.5, O3, SO2 and NO2) and emergency ambulance calls (EACs) for all-cause, cardiovascular and respiratory diseases from January 16, 2014 to December 31, 2016 were obtained from Luoyang, China. A distributed lag non-linear model (DLNM) was used to assess the association between hourly temperature and ambulance calls after adjusting for potential confounding factors. The fractions of EACs attributable to non-optimum temperatures were also estimated. RESULTS: Hourly temperature was associated with increased ambulance calls with a varying lag pattern. Extreme hot temperature (>32.1 °C) was positively associated with all-cause, cardiovascular diseases at lag 0-30 h and lag 0-9 h, while no significant effects were found for respiratory morbidity. Extreme cold temperature (<-2.5 °C) was positively associated with all-cause, cardiovascular and respiratory morbidity at lag 56-157 h, 50-145 h and 123-170 h. An overall EACs fraction of 6.84% [Backward estimate, 95% confidence interval (CI): 5.01%, 8.59%] could be attributed to non-optimum temperatures, and more contributions were caused by cold [Backward estimate: 6.06% (95% CI: 5.10%, 8.48%)] than by heat [Backward estimate: 0.79% (95% CI: 0.12%, 1.45%)]. CONCLUSIONS: Extreme hot temperature may lead to increased ambulance calls within a few hours, while extreme cold temperature may not increase ambulance calls until more than 2 days later. Effective measures, such as forming hourly temperature warning standards, optimizing ambulance services at extreme temperatures, etc., should be taken to reduce the unfavorable temperature - associated EACs burden.

Maternal air pollution exposure associated with risk of congenital heart defect in pre-pregnancy overweighted women.

OBJECTIVES: Prenatal exposure to air pollutant has been associated with congenital heart defect (CHD). However, no study has investigated this effect in pre-pregnancy overweighted women. This study aimed to evaluate gestational exposure to particulate pollutant (PM2.5) and gaseous air pollutants (O3 and NO2) on the risk of CHD, and explore the potential effect modifiers including maternal age, pre-pregnancy BMI and pregestational diseases. METHODS: In this birth cohort study, a total of 63,213 pregnant women in Foshan, China were initially recruited and followed from their first hospital visit for pregnancy to delivery during 2015-2019. CHD cases were confirmed by the records in hospital- and population- based birth defect surveillance systems. Air pollutant exposures were estimated by the daily concentrations measured in air monitoring stations in each participant's residential county. Mixed-effects regression models, adjusted for potential confounding factors were applied to estimate the associations between air pollutant and CHD during the first three months of the pregnancy. RESULTS: A total of 985 (1.6%) newborns were identified as CHD cases. For each 10 µg/m3 increase in ambient O3 during the 1st month, the OR values for CHD were 1.03 (95% CI: 0.94, 1.13) in pre-pregnancy normal weighted women and 1.24 (95% CI: 1.01, 1.53) in pre-pregnancy overweighted women. For each 10 µg/m3 increase in NO2 during the 3rd month, the OR values for CHD were 1.09 (95% CI: 1.01, 1.18) in pre-pregnancy normal weighted women and 1.27 (95% CI: 1.07, 1.51) in pre-pregnancy overweighted women. No significant associations were found between PM2.5 exposure and CHD in our analysis. CONCLUSIONS: This study demonstrates that gaseous air pollutants (O3 and NO2) exposure during the cardiac embryogenesis period is associated with an increased risk of CHD, particularly for pre-pregnancy overweighted women.

The mediation effect of maternal glucose on the association between ambient air pollution and birth weight in Foshan, China.

Maternal blood glucose level is associated with fetal growth, therefore, its role in the associations between air pollution and birth weight deserves investigation. We examined the mediation effect of maternal blood glucose on the associations between maternal air pollution exposure and birth weight. A total of 10,904 pregnant women in Foshan, China during 2015-2019 were recruited. Oral glucose tolerance test (OGTT) was administered to each participant after late trimester 2. Air pollution data at the monitoring stations in residential districts was used to estimate exposures of each participant during trimester 1 and trimester 2. Mixed-effects linear models were used to estimate the associations between air pollution and birth weight. After controlling for ten covariates, the direct effect of PM2.5 and SO2 (each 10 µg/m3 increment) on birth weight was -15.7 g (95% CI: -29.4, -4.8 g) and -83.6 g (95% CI: -134.8, -33.0 g) during trimester 1. The indirect effect of PM2.5 and SO2 (each 10 µg/m3 increment) on birth weight by increasing maternal fasting glucose level was 6.6 g (95% CI: 4.6, 9.1 g) and 22.0 g (95% CI: 4.1, 44.0 g) during trimester 1. Our findings suggest that air pollution might affect the birth weight through direct and indirect pathway, and the indirect effect might be mediated by maternal blood glucose.

Hourly associations between ambient air pollution and emergency ambulance calls in one central Chinese city: Implications for hourly air quality standards.

BACKGROUND: Most studies on the short-term health effects of air pollution have been conducted on a daily time scale, while hourly associations remain unclear. METHODS: We collected the hourly data of emergency ambulance calls (EACs), ambient air pollution, and meteorological variables from 2014 to 2016 in Luoyang, a central Chinese city in Henan Province. We used a generalized additive model to estimate the hourly effects of ambient air pollutants (PM2.5, PM10, SO2, and NO2) on EACs for all natural causes and cardiovascular and respiratory morbidity, with adjustment for potential confounding factors. We further examined the effect modification by temperature, relative humidity, wind speed, and atmospheric pressure using stratified analyses. RESULTS: In the single-pollutant models, PM2.5, PM10, SO2, and NO2 were associated with an immediate increase in all-cause morbidity at 0, 0, 12, 10 h, separately, after exposure to these pollutants (excess risks: 0.19% (95% confidence interval (CI): 0.03%, 0.35%), 0.13% (95% CI: 0.02%, 0.24%), 0.28% (95% CI: 0.01%, 0.54%) and 0.52% (95% CI: 0.06%, 0.99%), respectively). These effects remained generally stable in two-pollutant models. SO2 and NO2 were significantly associated with an immediate increase in risk of cardiovascular morbidity, but the effects on respiratory morbidity were relatively more delayed. The stratified analyses suggested that temperature could modify the association between PM2.5 and EACs, humidity and atmospheric pressure could modify the association between SO2 and EACs. CONCLUSIONS: Our study provides new evidence that higher concentrations of PM2.5, PM10, SO2, and NO2 may have transiently acute effects on all-cause morbidity and subacute effects on respiratory morbidity. SO2 and NO2 may also have immediate effects on cardiovascular morbidity. Findings of this study have important implications for the formation of hourly air quality standards.

[Air pollutants emissions of locomotives in China railways in recent 33 years].

Although railway has played a great role in transportation in China, there has been no any study on air pollutants emissions of locomotives until now. Based on the annual statistical data collected by the Chinese Railway Statistic Center, using the emission factor method derived from fuel consumption, the air pollutants emissions of locomotives during 1975-2007 were calculated, and their emission intensities and dynamic characteristics were analyzed. The results show that the emissions of soot, SO2, CO and CnHm from steam and diesel locomotives in china decreased from 3510.6 thousand, 399.7 thousand, 846.0 thousand and 845.3 thousand ton in 1975 to 78.7 thousand, 11.4 thousand, 36.7 thousand and 26.4 thousand ton in 2007, indicating a decrease of 107.0 thousand, 12.1 thousand, 25.3 thousand and 25.6 thousand ton per year, respectively. However, the emissions of NO,showed an increase trend from 34.8 thousand ton in 1975 to 98.3 thousand ton in 2007 with an increase rate of 2.0 thousand ton per year. The emission intensities of soot, SO2, CO and CnHm decreased significantly from 686.2, 78.1, 165.4 and 165.2 g/(hundred converted ton-km) in 1975 to 5.4, 0.8, 2.5 and 1.8 g/(hundred converted ton-km) in 2007, respectively. The emission intensity of NOx fluctuated in the range of 5.0-8.0 g/(hundred converted ton-km). Air pollutants emissions of locomotives accounted for very little of the total emissions in China, and the emission intensities decreased persistently. It shows that railway transportation is a perfect way of saving energy and reducing emission in the sector of transportation.

[Characteristics of atmospheric CO2 variations and some affecting factors in urban area of Beijing].

The diurnal variation of the atmospheric CO2 at the urban area in Beijing displayed a very clear cycle, generally with the lowest value being recorded at about 15:00 local time and the highest in the nighttime. The differences between the maximums and the minimums ranged from 23.2 to 39.0 mumol.mol-1 in the four seasons and the ones of summer and fall were bigger than that of winter and spring. It showed a large seasonal cycle with a maximum in winter, from 421.5 to 441.0 mumol.mol-1, and a minimum in summer, between 367.4 and 371.6 mumol.mol-1. The peak-to-peak amplitude of atmospheric CO2 in Beijing, controlled mostly by the anthropogenic activities and seasonal variation of vegetation, was higher than that in Xinglong Station, Hebei Province and China Global Atmosphere Watch Baseline Observatory (Waliguan). The concentration of atmospheric CO2 in Beijing increased rapidly at a mean growth rate of 3.7%.a-1 from 1993 to 1995. After displaying a peak of 409.7 +/- 25.9 mumol.mol-1 in 1995, it decreased slowly.