1.Study on Processing of Fructus Crataegi
Chinese Traditional Patent Medicine 1992;0(04):-
In this experiment, the crude Fructus Crataegi, its baked product, the products baked to dark brown and baked to charcoal were compared regarding the gastrointestinal promote function of rat, the content of free acids in stomach, total acids, pepsin and nitrite as indices. As a result, the crude and the fried product possessed greater effects on the digestive ability of rats, but their nitrite content was lower. It was preliminary suggested that adding the crude and the fried product into digestant be an optimum selection
2.The inhibitory effects of chlorhexidine in the development of rat periodontitis models
Hongyan WANG ; Lisi TAN ; Chunliang MA ; Shuo GUAN ; Yaping PAN
Journal of Practical Stomatology 2016;32(3):303-307
Objective:To investigate the inhibitory effects of different concentrations of chlorhexidine in the development of peri-odontitis models in rats.Methods:periodontitis models were established by the ligation of bilateral first molars and orally challenge with P.gingivalis W83.0.05%,0.1%,0.2% and 0.5% chlorhexidine were used to wash the periodontal pocket and oral mucosa of the rats.4 weeks later,absolute real time quantitative PCR was used to count the copy of P.gingivalis W83 in rat periodontal pockets.Scanning electron microscopy was used to observe the distribution of P.gingivalis W83 on rat teeth surface.Immunohisto-chemical technique was used to detect the expression of TNF-αin gingival tissue of the rats.Results:0.2% and 0.5% chlorhexi-dine reduced the copy of P.gingivalis W83 on teeth surface and in periodontal pockets (P <0.05);0.1% -0.5% chlorhexidine reduced the expression of TNF-αin gingival tissue (P <0.05).Conclusion:0.1% -0.2% chlorhexidine can inhibit the develop-ment of chronic periodontitis in rats.
3.Influence of arousal in emotional stroop effect
Chunliang FENG ; Weiwei MA ; You WANG ; Yuejia LUO
Chinese Journal of Behavioral Medicine and Brain Science 2011;20(8):757-758
Objective To investigate the arousal effect in the emotional Stroop effect by systematically manipulating the valence and arousal of affective stimuli.Methods 27 college students were recruited to distinThe main effect of emotion on RT was significant (F(5.130) = 5.90, P < 0.01 ), RTs in positive (( 893±36 ) ms)main effect of Emotion on ACC was marginally significant (F(5.130) = 2.63, P = 0.05 ), ACC in high arousing negalence on RT was significant (F(1.26 = 7.03, P =0.013 ).Further analysis revealed that RTs in positive condition was significant (F(1.26) =5.63, P=0.025),ACC in high arousing condition (0.93 ±0.17) was lower than ACC in low arousing condition (0.95 ± 0.09 ).Conclusion The emotional Stroop effect mainly depends on the arousal information of affective stimuli.
4.Porphyromonas gingivalis bacteremia increases the permeability of the blood-brain barrier via the Mfsd2a/Caveolin-1 mediated transcytosis pathway.
Shuang LEI ; Jian LI ; Jingjun YU ; Fulong LI ; Yaping PAN ; Xu CHEN ; Chunliang MA ; Weidong ZHAO ; Xiaolin TANG
International Journal of Oral Science 2023;15(1):3-3
Bacteremia induced by periodontal infection is an important factor for periodontitis to threaten general health. P. gingivalis DNA/virulence factors have been found in the brain tissues from patients with Alzheimer's disease (AD). The blood-brain barrier (BBB) is essential for keeping toxic substances from entering brain tissues. However, the effect of P. gingivalis bacteremia on BBB permeability and its underlying mechanism remains unclear. In the present study, rats were injected by tail vein with P. gingivalis three times a week for eight weeks to induce bacteremia. An in vitro BBB model infected with P. gingivalis was also established. We found that the infiltration of Evans blue dye and Albumin protein deposition in the rat brain tissues were increased in the rat brain tissues with P. gingivalis bacteremia and P. gingivalis could pass through the in vitro BBB model. Caveolae were detected after P. gingivalis infection in BMECs both in vivo and in vitro. Caveolin-1 (Cav-1) expression was enhanced after P. gingivalis infection. Downregulation of Cav-1 rescued P. gingivalis-enhanced BMECs permeability. We further found P. gingivalis-gingipain could be colocalized with Cav-1 and the strong hydrogen bonding between Cav-1 and arg-specific-gingipain (RgpA) were detected. Moreover, P. gingivalis significantly inhibited the major facilitator superfamily domain containing 2a (Mfsd2a) expression. Mfsd2a overexpression reversed P. gingivalis-increased BMECs permeability and Cav-1 expression. These results revealed that Mfsd2a/Cav-1 mediated transcytosis is a key pathway governing BBB BMECs permeability induced by P. gingivalis, which may contribute to P. gingivalis/virulence factors entrance and the subsequent neurological impairments.
Animals
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Rats
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Bacteremia/metabolism*
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Blood-Brain Barrier/microbiology*
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Caveolin 1/metabolism*
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Gingipain Cysteine Endopeptidases/metabolism*
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Permeability
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Porphyromonas gingivalis/pathogenicity*
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Transcytosis
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Virulence Factors/metabolism*
5.Construction of AQHI based on joint effects of multi-pollutants in 5 provinces of China
Jinghua GAO ; Chunliang ZHOU ; Jianxiong HU ; Ruilin MENG ; Maigeng ZHOU ; Zhulin HOU ; Yize XIAO ; Min YU ; Biao HUANG ; Xiaojun XU ; Tao LIU ; Weiwei GONG ; Donghui JIN ; Mingfang QIN ; Peng YIN ; Yiqing XU ; Guanhao HE ; Xianbo WU ; Weilin ZENG ; Wenjun MA
Journal of Environmental and Occupational Medicine 2023;40(3):281-288
Background Air pollution is a major public health concern. Air Quality Health Index (AQHI) is a very important air quality risk communication tool. However, AQHI is usually constructed by single-pollutant model, which has obvious disadvantages. Objective To construct an AQHI based on the joint effects of multiple air pollutants (J-AQHI), and to provide a scientific tool for health risk warning and risk communication of air pollution. Methods Data on non-accidental deaths in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces from January 1, 2013 to December 31, 2018 were obtained from the corresponding provincial disease surveillance points systems (DSPS), including date of death, age, gender, and cause of death. Daily meteorological (temperature and relative humidity) and air pollution data (SO2, NO2, CO, PM2.5, PM10, and maximum 8 h O3 concentrations) at the same period were respectively derived from China Meteorological Data Sharing Service System and National Urban Air Quality Real-time Publishing Platform. Lasso regression was first applied to select air pollutants, then a time-stratified case-crossover design was applied. Each case was matched to 3 or 4 control days which were selected on the same days of the week in the same calendar month. Then a distributed lag nonlinear model (DLNM) was used to estimate the exposure-response relationship between selected air pollutants and mortality, which was used to construct the AQHI. Finally, AQHI was classified into four levels according to the air pollutant guidance limit values from World Health Organization Global Air Quality Guidelines (AQG 2021), and the excess risks (ERs) were calculated to compare the AQHI based on single-pollutant model and the J-AQHI based on multi-pollutant model. Results PM2.5, NO2, SO2, and O3 were selected by Lasso regression to establish DLNM model. The ERs for an interquartile range (IQR) increase and 95% confidence intervals (CI) for PM2.5, NO2, SO2 and O3 were 0.71% (0.34%–1.09%), 2.46% (1.78%–3.15%), 1.25% (0.9%–1.6%), and 0.27% (−0.11%–0.65%) respectively. The distribution of J-AQHI was right-skewed, and it was divided into four levels, with ranges of 0-1 for low risk, 2-3 for moderate risk, 4-5 for high health risk, and ≥6 for severe risk, and the corresponding proportions were 11.25%, 64.61%, 19.33%, and 4.81%, respectively. The ER (95%CI) of mortality risk increased by 3.61% (2.93–4.29) for each IQR increase of the multi-pollutant based J-AQHI , while it was 3.39% (2.68–4.11) for the single-pollutant based AQHI . Conclusion The J-AQHI generated by multi-pollutant model demonstrates the actual exposure health risk of air pollution in the population and provides new ideas for further improvement of AQHI calculation methods.