Protease inhibitors, which are widely distributed in all types of life forms, are generally considered to be one of the most abundant proteins and a defense mechanism. A protease inhibitor from tartary buckwheat seeds (TBTI-Ⅱ ), with specific trypsin-inhibitory activity, was obtained by Resource Q anion-exchange chromatography and Superdex G 75 gel filtration. SDS-PAGE analysis indicated that the approximate molecular weight was 9.0 kD. Amino-acid analysis showed that the TBTI- Ⅱ was composed of 80 amino-acid residues with a high content of glutamate, aspartate and arginine. The inhibitor had high thermostability and retained 67.6% of its activity after heating at 100℃ for 10 min. The inhibition constant Ki was determined to be 1.01× 10-4 mol/L. It was demonstrated that the inhibitor was able to have an effect on the growth of cotton bollworm larva, after being fed with the artificial diets mixed with the target inhibitor. The present study indicates that the trypsin inhibitor from tartary buckwheat seeds could be a new potential anti-insect factor.

In the past decades, people's work and life styles have dramatically changed during the rapid economic development and urbanization in China. A national survey reported that Chinese adults spend an average of 81% of daily time in indoor environment. Exposure to indoor air pollution plays key roles for human health but is likely to be neglected due on the relatively lower concentration levels and lower awareness among common people. Till now, published studies focus more on the pollution levels or the toxicological effects of indoor air pollutants but there is a lack of disease burden assessment attributable to indoor air pollution. In this review, several international studies were introduced on the disease burden estimation attributable to indoor air pollution, as well as the estimation methods. The current situation of national study was also reviewed. The strengths and limitations of the representative international studies were discussed. This review is helpful in providing data to guide the research on disease burden assessment attributable to indoor air pollution in China, and further helps to prioritize the indoor air pollution control based on disease burden ranking among pollutants and motivate public policies to protect the public health.

Background Improper settings of outdoor smoking points in public places may increase the risk of secondhand smoke exposure among the population. Conducting research on air pollution in and around smoking spots and related influencing factors can provide valuable insights for optimizing the setting of outdoor smoking points. Objective To investigate the influence of the number of smokers at outdoor smoking points and the distance on the diffusion characteristics of surrounding air pollutants, in order to optimize the setting of outdoor smoking points. Methods Surrounding the exhibition halls in the China International Import Expo (CIIE), two outdoor smoking points were randomly selected, one on the first floor (ground level) and the other on the second floor (16 m above ground), respectively. At 0, 3, 6, and 9 m from the smoking points in the same direction, validated portable air pollutant monitors were used to measure the real-time fine particulate matter (PM_2.5) and carbon dioxide (CO₂) concentrations for consecutive 5 d during the exhibition, as well as the environmental meteorological factors at 0 m with weather meters including wind speed, wind direction, and air pressure. An open outdoor atmospheric background sampling point was selected on each of the two floors to carry out parallel sampling. Simultaneously, the number of smokers at each smoking point were double recorded per minute. The relationships between the number of smokers, distance from the smoking points, and ambient PM_2.5 and CO₂ concentrations were evaluated by generalized additive regression models for time-series data after adjustment of confounders such as temperature, relative humidity, and wind speed. Results The median numbers of smokers at smoking points on the first and second floors were 6 [interquartile range (IQR): 3, 9] and 9 (IQR: 6, 13), respectively. Windless (wind speed <0.6 m·s⁻¹) occupied most of the time (85.9%) at both locations. The average concentration of ambient PM_2.5 at the smoking points (0 m) [mean ± standard deviation, (106±114) μg·m⁻³] was 4.2 times higher than that of the atmospheric background [(25±7) μg·m⁻³], the PM_2.5 concentration showed a gradient decline with the increase of distance from the smoking points, and the average PM_2.5 concentration at 9 m points [(35±22) μg·m⁻³] was close to the background level (1.4 times higher). The maximum concentration of CO₂ [(628±23) μmol·mol⁻¹] was observed at 0 m, and its average value was 1.3 times higher than that of the atmospheric background [(481±40) μmol·mol⁻¹], and there was no gradient decrease in CO₂ concentration with increasing distance at 0, 3, 6, and 9 m points. The regression analyses showed that, taking smoking point as the reference, every 3 m increase in distance was associated with a decrease of ambient PM_2.5 by 24.6 [95% confidence interval (95%CI): 23.5, 25.8] μg·m⁻³ (23.2%) and CO₂ by 54.1 (95%CI: 53.1, 55.1) μmol·mol⁻¹ (8.6%). Every one extra smoker at the smoking point was associated with an average increase of PM_2.5 and CO₂ by 2.0 (95%CI: 1.7, 2.8) μg·m⁻³ and 1.0 (95%CI: 0.7，1.2) μmol·mol⁻¹, respectively. The sensitivity analysis indicated that, under windless conditions, the concentrations of PM_2.5 and CO₂ at the smoking points were even higher but the decreasing and dispersion characteristics remained consistent. Conclusion Outdoor smoking points could significantly increase the PM_2.5 concentrations in the surrounding air and the risks of secondhand smoke exposure, despite of the noticeable decreasing trend with increasing distance. Considering the inevitable poor dispersion conditions such as windless and light wind, outdoor smoking points are recommended to be set at least 9 m or farther away from non-smoking areas.

Background Public places are frequently polluted by cigarette smoking, and there is a lack of accurate, real-time, and intelligent monitoring technology to identify smoking behavior. It is necessary to develop a tool to identify cigarette smoking behavior in public places for more efficient control of cigarette smoking and better indoor air quality. Objective To construct a model for recognizing cigarette smoking behavior based on real-time indoor concentrations of PM_2.5 in public places. Methods Real-time indoor PM_2.5 concentrations were measured for at least 7 continuous days in 10 arbitrarily selected places (6 public service providers and and 4 office or other places) from Oct. to Nov. 2022 in Pudong New Area, Shanghai. Indoor nicotine concentrations were monitored with passive samplers simultaneously. Outdoor PM_2.5 concentration data were obtained from three municipal environmental monitoring stations which were nearest to each monitoring point during the same period. Mann-Whitney U test was used to compare indoor and outdoor means of PM_2.5 concentrations, and Spearman rank correlation was used to analyze indoor PM_2.5 and nicotine concentrations. An interactive plot and a random forest model was applied to examine the association between video observation validated indoor smoking behavior and real-time indoor PM_2.5 concentrations in an Internet cafe. Results The average indoor PM_2.5 concentration in the places providing public services [(97.5±149.3) µg·m⁻³] was significantly higher than that in office and other places [(19.8±12.2) µg·m⁻³] (P=0.011). The indoor/outdoor ratio (I/O ratio) of PM_2.5 concentration in the public service providers ranged from 1.1 to 19.0. Furthermore, the indoor PM_2.5 concentrations in the 10 public places were significantly correlated with the nicotine concentrations (r_s=0.969, P<0.001). Among them, the top 3 highly polluted places were Internet cafes, chess and card rooms, and KTV. The results of random forest modeling showed that, for synchronous real-time PM_2.5 concentration, the area under the curve (AUC) was 0.66, while for PM_2.5 concentration at a lag of 4 min after the incidence of smoking behavior, the AUC increased to 0.72. Conclusion The indoor PM_2.5 concentrations in public places are highly correlated with smoking behavior. Based on real-time indoor PM_2.5 monitoring, a preliminary recognition model for smoking behavior is constructed with acceptable accuracy, indicating its potential values applied in smoking control and management in public places.