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This study aimed to investigate the treatment-related annual cost of childhood epilepsy and its related factors in mainland China. A total of 244 cases were collected at the outpatient clinics of Peking University First Hospital Pediatrics from April 2010 to August 2013. The median annual epilepsy treatment–related costs per patient were estimated to be RMB7822 (US$1160), accounting for 43.72% (median) of urban residents' disposable income in China. Those who lived far away or with uncontrolled seizures yielded much higher costs. This study concluded that the treatment of children with epilepsy produces a heavy burden on both families and society in mainland China. The constitution ratio of antiepileptic drugs and travel expenses are much higher than those of other countries. Adjusting medical insurance coverage, balancing medical resource distribution, and taking good control of seizures might be effective in reducing the economic burden of childhood epilepsy in China. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) pollution severely affects plant growth and development, posing risks to human health throughout the food chain. Improved iron (Fe) nutrients could mitigate Cd toxicity in plants, but the regulatory network involving Cd and Fe interplay remains unresolved. Here, a transcription factor gene of alfalfa, MsbHLH115 was verified to respond to iron deficiency and Cd stress. Overexpression of MsbHLH115 enhanced tolerance to Cd stress, showing better growth and less ROS accumulation in Arabidopsis thaliana . Overexpression of MsbHLH115 significantly enhanced Fe and Zn accumulation and did not affect Cd, Mn, and Cu concentration in Arabidopsis. Further investigations revealed that MsbHLH115 up-regulated iron homeostasis regulation genes, ROS-related genes, and metal chelation and detoxification genes, contributing to attenuating Cd toxicity. Y1H, EMSA, and LUC assays confirmed the physical interaction between MsbHLH115 and E-box, which is present in the promoter regions of most of the above-mentioned iron homeostasis regulatory genes. The transient expression experiment showed that MsbHLH115 interacted with MsbHLH121pro . The results suggest that MsbHLH115 may directly regulate the iron-deficiency response system and indirectly regulate the metal detoxification response mechanism, thereby enhancing plant Cd tolerance. In summary, enhancing iron accumulation through transcription factor regulation holds promise for improving plant tolerance to Cd toxicity, and MsbHLH115 is a potential candidate for addressing Cd toxicity issues., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Gao, Dong, Chang, Zhu, Guo, Guo and Bi.)

Objective: To verify the equivalence of hawthorn leaves standard decoction and formula granules., Methods: In this experiment, liquid chromatograph mass spectrometer (LC-MS) was used to examine the chemical composition of hawthorn leaves standard decoction and formula granules, separately. In addition, oxidative stress test was used to explore the antioxidant capacity of them., Results: 71 chemical components were identified by LC-MS. Among them, 64 and 56 compounds were identified in the standard decoction and formula granules, respectively. There were a total of 49 common components, with no significant difference in content. Oxidative stress test showed that hawthorn leaves standard decoction and formula granules had no obvious toxicity to human umbilical vein endothelial cells. Compared with the model group, the same dose of hawthorn leaves formula granule and standard decoction could inhibit the secretion of lactate dehydrogenase and malondialdehyde (P < 0.05), and increase the content of superoxide dismutase (P < 0.01), with no statistically significant difference., Conclusions: There is no significant difference in the main active ingredients between the standard decoction and the formula granules, and the antioxidant activity in vitro is equivalent, providing an important theoretical basis for the further development of hawthorn leaves formula granules., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The spatial distribution, accumulation features, and driving factors of O 3 pollution were analyzed using spatial autocorrelation and hotspot analysis and the STIRPAT model based on the high spatiotemporal resolution online monitoring data from 2016 to 2020 in Tianjin. The results showed that the variation characteristics of O 3 concentration in Tianjin from 2016 to 2020 had the trend of pollution occurring in advance and the scope of the pollution expanding. The distribution of O 3 pollution showed significant aggregation from June to October. High-high value clustering areas included six urban districts, Beichen District, Jinnan District, and Jinghai District. O 3 concentration formed high value hot spots in the southwest and low value cold spots in the northeast. Meteorological factors such as temperature, breeze percentage, and sunshine duration, as well as social factors such as NO x emission, VOCs emission, and motor vehicle ownership had significant effects on O 3 concentration. The regression fitting effect of the integrated drive STIRPAT model was better than that of the single meteorological factor or social factor models. In order to promote scientific and efficient prevention and control of ozone pollution during the 14 th Five-Year Plan period, meteorological conditions require attention; under the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," it is necessary for Tianjin to further improve the emission performance of steel, petrochemicals, thermal power, building materials, and other industries, Additionally, clean upgrading, transformation, and green development should be guided for enterprises to reduce VOCs and NO x emissions. At same time, the increase in fuel vehicle numbers should be controlled, and new energy vehicles should be vigorously promoted to reduce vehicle emissions.

The change trend, relationship, and influencing factors of PM 2.5 and O 3 concentrations were analyzed by using a Kolmogorov-Zurbenko (KZ) filter coupled with stepwise multiple linear regression analysis and the spatiotemporal resolution monitoring data of PM 2.5 and O 3 and meteorological data observed in Tianjin from 2013 to 2020. The results showed that a significant decreasing trend of PM 2.5 concentrations by 50.0% was observed from 2013 to 2020, whereas an increasing trend for O 3 concentrations by 25.8% was observed from 2013 to 2020. Compared with that in 2013 to 2017, the monthly difference in PM 2.5 concentrations gradually narrowed from 2018 to 2020, whereas the concentration of O 3 had increased significantly since April, and the occurrence time of O 3 pollution was advanced. The correlation coefficient patterns of O 3 and PM 2.5 showed obvious seasonal distribution characteristics. The correlation coefficients were negatively correlated in winter and positively correlated in the summer, and the correlation coefficients in summer were generally higher than those in other seasons. The correlation coefficients between O 3 and PM 2.5 in different seasons were positively proportional to the fitting slope. The ratios of the fitting slope to correlation coefficients showed an increasing trend, which might reflect that the inhibitory effect of PM 2.5 on O 3 formation in the PM 2.5 -O 3 interaction mechanism might have been weakened due to the impact of emission reduction. A significant decreasing trend was observed for the long-term trend components of the PM 2.5 concentration time series; emission reduction played a leading role, and meteorological factors contributed -3 to 6 μg·m -3 . The changes in the relationship between the PM 2.5 /CO ratio versus NO 2 /SO 2 from negative to positive were observed from 2013-2017 to 2018-2020 in Tianjin, which could indicate the enhanced contribution potential of nitrogen oxides to the main secondary component formation of PM 2.5 under the current emission reduction scenarios, and the main secondary components of PM 2.5 in Tianjin gradually changed from sulfate to nitrate. An overall upward trend was observed for the long-term trend components of the O 3 concentration time series from 2013 to 2020, and the contribution of precursor emissions to the long-term component of O 3 increased from 2013 to 2018 and began to decrease after 2019. The contribution of meteorological factors to the long-term component of O 3 presented an obvious stage change, showing a downward trend from 2013 to 2016 and an upward trend from 2016 to 2020. The O 3 concentration presented a non-linear relationship with NO 2 during the period of intense atmospheric photochemical processes (11:00-16:00) in summer. Compared with that in 2013-2015, the fitting curve of O 3 and NO 2 showed an obvious offset to the low value of NO 2 from 2016 to 2020, which reflected that the NO x emission reduction in this period achieved certain results. Compared with that in 2018, the fitting curve of O 3 and NO 2 moved downward from 2019 to 2020, which may reflect that NO x and VOCs emission reduction had a non-negligible effect on the O 3 decline at this stage.

The characteristics, pollutant concentration distribution, and key meteorological factors of PM 2.5 -O 3 compound pollution in Tianjin were analyzed based on the high-resolution online monitoring data of PM 2.5 , O 3, and meteorological data observed in Tianjin from 2013 to 2019. Total PM 2.5 -O 3 compound pollution was 94 days and showed a decreasing trend by year; a significant decreasing trend of PM 2.5 -O 3 compound pollution days were observed in the early stage, with a decline rate of 52.2% from 2013 to 2015. By contrast, in the later period from 2016 to 2019, a fluctuating increasing trend of PM 2.5 -O 3 compound pollution days of 16.7% was observed. PM 2.5 -O 3 compound pollution days mainly occurred from March to September each year with substantial variation by year, mainly occurring in June to August from 2013 to 2016 and in April and September from 2017 to 2019. The peak value of ρ (O 3 ) (301-326 μg·m -3 ) appeared when ρ (PM 2.5 ) ranged from 75 μg·m -3 to 85 μg·m -3 . PM 2.5 -O 3 compound pollution days accounted for 34.4% of total O 3 pollution events in Tianjin, which showed a decreasing trend by year. The peak O 3 concentration and average O 3 concentration during PM 2.5 -O 3 compound pollution were higher than those during simplex O 3 pollution, and the number of days with PM 2.5 and O 3 as the primary pollutant decreased and increased in compound pollution days by year, respectively. The weather situation of PM 2.5 -O 3 compound pollution was categorized into five weather types, namely low pressure, weak high pressure, rear of high pressure, front of cold front, and equalized pressure. The low pressure, front of cold front, and weak high pressure were observed most frequently, accounting for 92.5% of the total weather situation. The occurrence of PM 2.5 -O 3 compound pollution was most probable when the dominant wind direction was the southwest and south, the average wind speed was less than 2 m·s -1 , the temperature was between 20-35℃, and the humidity was between 40%-60%.

The characteristics and sources of PM 2.5 -O 3 compound pollution were analyzed based on the high-resolution online monitoring data of PM 2.5 , O 3 and volatile organic compounds(VOCs) observed in Tianjin from 2017 to 2019. The results showed that total PM 2.5 -O 3 compound pollution was 34 days, which only appeared between March and September and slightly increased by year. The peak value of ρ (O 3 )(301-326 μg·m -3 ) appeared when ρ (PM 2.5 ) ranged from 75 μg·m -3 to 85 μg·m -3 . During PM 2.5 -O 3 compound pollution, the average ρ (VOCs) was 72.59 μg·m -3 , and the chemical compositions of VOCs were alkanes, aromatics, alkenes, and alkynes, accounting for 61.51%, 20.38%, 11.54%, and 6.57% of VOCs concentration on average, respectively. The concentration of the top 20 species of VOCs increased, among which the proportion of alkane species such as ethane, n-butane, isobutane, and isopentane increased; the proportion of alkenes and alkynes decreased slightly; and the proportion of benzene and 1,2,3-trimethylbenzene of aromatic hydrocarbons increased slightly. The ozone formation potential(OFP) contribution of alkanes, alkenes, aromatics, and alkynes were 19.68%, 39.99%, 38.08%, and 2.25%, respectively; the contributions of alkanes, alkenes, and aromatics to secondary organic aerosol(SOA) formation potential were 7.94%, 2.17%, and 89.89%, respectively. Compared with that of non-compound pollution, the contribution of alkanes and aromatics to OFP increased 13.8% and 4.3%, and that to SOA formation potential increased 2.3% and 0.2%, respectively. The contribution of alkenes to OFP and SOA formation potential decreased 9.4% and 15.6%, respectively, and the contribution of alkynes to OFP increased 7.7% in compound pollution. The contributions of main species such as 1-pentene, n -butane, methyl cyclopentane, isopentane, 1,2,3-trimethylene, propane, toluene, acetylene, o -xylene, ethylbenzene, m -ethyltoluene, and m / p -xylene to OFP increased, and that of isoprene to OFP decreased. The contribution of benzene, 1,2,3-trimethylbenzene, toluene, and o -xylene to the potential formation of SOA increased during compound pollution. Positive matrix factorization was applied to estimate the contributions of sources to OFP and SOA formation potential in compound pollution, solvent usage, automobile exhaust, petrochemical industrial emission, natural source, liquefied petroleum gas(LPG) evaporation, combustion source, gasoline evaporation, and other industrial process sources were identified as major sources of OFP and SOA formation potential; the contributions of each source to OFP were 21.9%, 16.9%, 16.7%, 12.4%, 8.3%, 7.7%, 2.9%, and 13.2%, respectively, and to SOA formation potentials were 46.8%, 14.4%, 7.1%, 11.9%, 5.9%, 6.6%, 1.6%, and 5.7%, respectively. Solvent usage, automobile exhaust, and petrochemical industrial emissions were main sources for PM 2.5 -O 3 compound pollution.

To further study the effect of volatile organic compounds (VOCs) on ozone pollution, the characteristics and sources of VOCs at different ozone (O 3 ) concentration levels were analyzed, using high-resolution online monitoring data obtained from Tianjin in the summer of 2019. Results showed that VOCs concentrations were 32.94, 38.10, 42.41, and 47.12 μg ·m -3 , when the O 3 concentration levels were categorized as excellent, good, light pollution, and moderate pollution, respectively. VOCs were composed of alkanes, alkenes, alkynes and aromatics, which accounted for 61.72%-63.36%, 14.96%-15.51%, 2.73%-4.13%, and 18.53%-19.10%, respectively, of VOCs concentrations at different O 3 concentration levels. Among them, the proportion of alkanes was slightly higher when O 3 concentration was categorized as good or light pollution, alkenes and alkynes accounted for the highest proportion when O 3 concentration was excellent, and the proportion of aromatics was highest during periods of moderate pollution. The main VOCs species were propane, ethane, ethylene, toluent, n -butane, isopentane, m / p -xylene, propylene, acetylene, n -hexane, isobutene, benzene, n -pentane, isoprene, and 1,2,3-trimethylbenzene. The concentration percentage of isopentane, n -pentane, benzene, ethylene, propylene, n -butane, and isobutane increased gradually as O 3 concentration increased. Significant increases in isoprene and 1,2,3-trimethylbenzene were observed during periods of light and moderate pollution. Alkenes and aromatics had higher ozone formation potential (OFP), and the contribution of alkenes to OFP declined as the O 3 level rose, whereas that of aromatics increased. Ethylene, propylene, m / p -xylene, 1,2,3-trimethylbenzene, toluene, isoprene, trans -2-butene, and cis -2-pentene were the key species for O 3 generation, and the contribution ratio of 1,2,3-trimethylbenzene, isoprene, propylene, and ethylene to OFP increased significantly during light or moderate O 3 pollution. Positive matrix factorization was applied to estimate the source contributions of VOCs. Automobile exhaust, solvent usage, liquefied petroleum gas (LPG)/gasoline evaporation, combustion, petrochemical industrial emissions, natural sources, and other industrial emissions were identified as major sources of VOCs in summer. As O 3 concentration level rose, the contribution percentage of automobile exhaust, LPG/gasoline evaporation, petrochemical industrial emissions, and natural sources increased gradually, whereas the contribution of combustion and other industrial emissions decreased overall. The contribution of solvent usage was lower when O 3 levels indicated light or moderate pollution than when it was good.

The characterization and source apportionment of atmospheric volatile organic compounds (VOCs) in Tianjin in 2019 were investigated based on high-resolution online monitoring data observed at an urban site in Tianjin. The results showed that the average annual concentration of VOCs was 48.9 μg·m -3 , and seasonal concentrations followed with winter (66.9 μg·m -3 ) > autumn (47.9 μg·m -3 ) > summer (42.0 μg·m -3 ) > spring (34.6 μg·m -3 ). The chemical compositions of the VOCs were alkanes, aromatics, alkenes, and alkynes, which accounted for 65.0%, 17.4%, 14.6%, and 3.0% of the VOCs concentrations on average, respectively. The proportion of alkanes, aromatics, and alkynes was the highest in autumn, summer, and winter, respectively, while a higher alkenes proportion was observed in summer and winter. The ozone formation potential contribution of alkanes, alkenes, aromatics, and alkynes in spring and summer was 16.9%, 48.6%, 33.5%, and 1.0%, respectively, and the species with higher contributions were ethene, propylene, m , p -xylene, 1,2,3-trimethylbenzene, toluene, isoprene, trans-2-butene, cis-2-pentene, o -xylene, and m -ethyltoluene. During autumn and winter, the aromatics contributed as much as 91.5% to the secondary organic aerosol (SOA) formation potential, and o -xylene, toluene, m , p -xylene, ethylbenzene, o -ethyltoluene, and benzene were the main contributing species. Positive matrix factorization was applied to estimate VOCs source contributions, and automobile exhaust, liquefied petroleum gas/natural gas (LPG/NG) and gasoline evaporation, solvent usage, petrochemical industrial emissions, combustion, and natural sources were identified as major sources of VOCs in spring and summer, accounting for 29.2%, 19.9%, 16.4%, 10.3%, 7.3%, and 6.6%, respectively. While in autumn and winter, the contributions of LPG/NG and gasoline evaporation, automobile exhaust, combustion, solvent usage, and petrochemical industrial emissions were 32.4%, 21.9%, 18.5%, 13.3%, and 8.4%, respectively. Compared to the source contributions in spring and summer, a significant increase was observed for LPG/NG and combustion emission of 62.8% and 153.4%, respectively, and other sources decreased by 18.4%-25.0% in autumn and winter. Source composition spectrums showed that the petrochemical industry and solvent usage were the main emission sources of alkenes and aromatics in spring and summer, and combustion and solvent usage were the main emission sources of aromatics in autumn and winter. Thus, focus should be played on the petrochemical industry and solvent usage in spring and summer and on combustion and solvent usage in autumn and winter to further prevent and control ozone and SOA in Tianjin.

The characteristics of secondary organic reactions were studied based on supersite monitoring data from January to March, 2019, in Tianjin. During heavy pollution episodes, SOC (secondary organic carbon) accounted for between 3.1% and 3.8% of PM 2.5 , and the growth rate of SOC was obviously higher than that of PM 2.5 , thus indicating that secondary organic reactions had a considerable effect on PM 2.5 . The growth rate of VOCs (volatile organic compounds) was lower than that of PM 2.5 , which was probably due to the fact that VOCs were consumed as precursors to secondary particles. The ratio of ethane to acetylene was higher than 2.0 during heavy pollution episodes indicating that air masses were old, and the ratio was lower than clean air days showing that the reaction activities were higher than before. During the heavy pollution episodes, the potential formation of SOA (secondary organic aerosol) from VOCs ranged from 0.49 to 1.21 μg·m -3 . Among the species, aromatic hydrocarbons contributed the most, whereby the highest contribution exceeded 90%, and their growth rates were also the highest; hence, aromatic hydrocarbons were the VOCs species that had the greatest effect on SOA.

Based on monitoring data collected at the supersite of Tianjin in 2017, seven typical heavy pollution episodes were investigated. The concentrations of air pollutants and secondary inorganic transformation products were analyzed to study the secondary inorganic pollution characteristics during the heavy pollution episodes. Compared to clean weather, concentrations of NO 3 - and SO 4 2- during the heavy pollution episodes increased at rapid growth rates. These rates were obviously higher than the rate for PM 2.5 increases, which indicates that the secondary inorganic reactions had an important influence on PM 2.5 pollution during the episodes. The concentrations of PM 2.5 and SO 2 during the episodes in the latter half of the year were lower than those in the first half of the year probably because a substantial amount of coal use had been controlled. During the heavy pollution episodes, the NO 2 /SO 2 values were 1.5 to 19.6, with higher values in the latter half of the year than the first half of the year suggestive of a greater influence from mobile sources. During most episodes, NO 3 - concentrations were higher than SO 4 2- concentrations, and SOR values were higher than NOR values, which shows that the secondary transformation of nitrate and sulfate both played important roles during the heavy pollution episodes. When SO 2 concentrations decreased significantly, SO 4 2- concentrations did not decrease obviously, thus indicating that besides the secondary inorganic reactions, other factors also had a large impact on the generation of sulfate.

An incubation test was conducted with the paddy soil samples collected from three national long-term experiment stations in Hunan Province to study the characteristics of organic carbon mineralization under different fertilization treatments and its relationships with organic carbon fractions, i.e., total organic carbon (TOC), microbial biomass carbon (MBC), and water-soluble organic carbon (WSOC). The results showed that in all fertilization treatments, the cumulative amounts of CO2 and CH4 ranged from 448.64 to 1516.77 microg x g(-1) and from 15.60 to 33.34 microg x g(-1), respectively. In the 58 days of incubation, the mineralized carbon accounted for 3.59%-5.57% of TOC. The CO2 production rate was higher in the early phase of incubation, decreased rapidly then, and tended to stable afterwards; while the CH4 production rate had a slow increase first and declined rapidly then. A combined application of chemical fertilizers and manure or straw increased the cumulative amounts of CO2 and CH4 significantly. In all fertilization treatments, the cumulative mineralized C had significant correlations with TOC, MBC and WSOC, but less correlation with its percentage in TOC.