Dietary pattern is quite distinct among the inhabitants of high-altitude areas because of environmental and geographical uniqueness; hence, it is important to investigate this data as accurately as possible. However, very few data are related to these populations up to now. Based on the food frequency questionnaire (FFQ) used in the Chinese population, a revised Tibetan edition was developed with respect to the lifestyle in high-altitude areas. After assessment of validity and reproducibility, a nutrition intake survey was conducted among 1,071 randomly sampled Tibetan people. In addition, the Bland–Altman approach was used to compare the agreement between the two dietary tools. For the reproducibility analysis, intraclass correlation coefficients (ICC) were calculated to examine the agreement of food groups and nutrients from the two FFQs (FFQ1 and FFQ2). Nutrient intake was calculated using food composition tables. For the validity analysis, Pearson's correlation of food groups intakes varied from 0.22 to 0.91 (unadjusted). The correlations of nutrients ranged from 0.24 to 0.76 (unadjusted). In the analysis of reliability, the ICC of food groups varied from 0.27 to 0.70 (unadjusted). The ICC of nutrient intakes ranged from 0.22 to 0.87 (unadjusted). The results of nutritional analysis showed that ~25% of foods consumed frequently were traditional Tibetan foods. However, traditional Han foods were frequently consumed. In addition, the energy, iron, and protein intakes for male or female subjects were close to the Chinese Dietary Nutrient Reference Intake (Chinese DRIs); however, fat and sodium intakes were significantly higher than the Chinese DRIs. Interestingly, lower intakes of other types of nutrition, such as vitamin C were detected in people living in high-altitude areas. Our data indicated that excess consumption of fat and sodium and insufficient intake of vitamin C were common among Tibetan people, as compared with the most Chinese people living in the plateau areas. More investigations are needed to reveal the association between the food intake style and high-altitude endemic diseases.

Objective This study described the epidemic characteristics of varicella in Dalian from 2009 to 2019, explored the fitting effect of Grey model first-order one variable( GM(1,1)), Markov model, and GM(1,1)-Markov model on varicella data, and found the best fitting method for this type of data, to better predict the incidence trend. Methods For this Cross-sectional study, this article was completed in 2020, and the data collection is up to 2019. Due to the global epidemic, the infectious disease data of Dalian in 2020 itself does not conform to the normal changes of varicella and is not included. The epidemiological characteristics of varicella from 2009 to 2019 were analyzed by epidemiological descriptive methods. Using the varicella prevalence data from 2009 to 2018, predicted 2019 and compared with actual value. First made GM (1,1) prediction and Markov prediction. Then according to the relative error of the GM (1,1), made GM (1,1)-Markov prediction. Results This study collected 37,223 cases from China Information System for Disease Control and Prevention's “Disease Prevention and Control Information System” and the cumulative population was 73,618,235 from 2009 to 2019. The average annual prevalence was 50.56/100000. Varicella occurred all year round, it had a bimodal distribution. The number of cases had two peaks from April to June and November to January of the following year. The ratio of males to females was 1.17:1. The 4 to 25 accounted for 60.36% of the total population. The age of varicella appeared to shift backward. Students, kindergarten children, scattered children accounted for about 64% of all cases. The GM(1,1) model prediction result of 2019 would be 53.64, the relative error would be 14.42%, the Markov prediction result would be 56.21, the relative error would be 10.33%, and the Gray(1,1)-Markov prediction result would be 59.51. The relative error would be 5.06%. Conclusions Varicella data had its unique development characteristics. The accuracy of GM (1,1)—Markov model is higher than GM(1.1) model and Markov model. The model can be used for prediction and decision guidance.

This study focused on the association of dietary patterns and Tibetan featured foods with high-altitude polycythemia (HAPC) in Naqu, Tibet, to explore the risk factors of HAPC in Naqu, Tibet, to raise awareness of the disease among the population and provide evidence for the development of prevention and treatment interventions. A 1:2 individual-matched case-control study design was used to select residents of three villages in the Naqu region of Tibet as the study population. During the health examination and questionnaire survey conducted from December 2020 to December 2021, a sample of 1,171 cases was collected. And after inclusion and exclusion criteria and energy intake correction, 100 patients diagnosed with HAPC using the “Qinghai criteria” were identified as the case group, while 1,059 patients without HAPC or HAPC -related diseases were identified as the control group. Individuals were matched by a 1:2 propensity score matching according to gender, age, body mass index (BMI), length of residence, working altitude, smoking status, and alcohol status. Dietary patterns were determined by a principal component analysis, and the scores of study subjects for each dietary pattern were calculated. The effect of dietary pattern scores and mean daily intake (g/day) of foods in the Tibetan specialty diet on the prevalence of HAPC was analyzed using conditional logistic regression. After propensity score matching, we found three main dietary patterns among residents in Naqu through principal component analysis, which were a “high protein pattern,” “snack food pattern,” and “vegetarian food pattern.” All three dietary patterns showed a high linear association with HAPC (p < 0.05) and were risk factors for HAPC. In the analysis of the relationship between Tibetan featured foods and the prevalence of HAPC, the results of the multifactorial analysis following adjustment for other featured foods showed that there was a positive correlation between the average daily intake of tsampa and the presence of HAPC, which was a risk factor. Additionally, there was an inverse correlation between the average daily intake of ghee tea and the presence of HAPC, which was a protective factor.

Background Curcumin, as a lipid-lowering drug, has been reported to be effective in the treatment of breast cancer. However, the underlying molecular mechanisms have not been completely investigated. Methods MTT assay was used to determine the effect of curcumin on survival rate of MCF-7 cells. The effects of curcumin on tumor growth were observed in animal models of breast cancer. The positive reactions of Caspase-1, IL-1β and IL-18 were detected by immunohistochemistry. LC3, p62, CTSB, ASC, Pro-Caspase-1, GSDMD, NLRP3, Caspase-1, GSDMD-N, IL-1β and IL-18 were determined by Western blot in vitro and vivo. The release of extracellular IL-1β and IL-18 was determined by ELISA. LDH release was measured. The expression level of CTSB in cytoplasm were determined by immunofluorescence assay. Cell proliferation, cell migration and tube formation assays were used to determine the abilities of cells. In this study, NLRP3 inflammasome inhibitor MCC950, cathepsin B inhibitor CA-074 ME and autophagy inhibitor 3-MA were used to act on cells to investigate the role of NLRP3 inflammasome, cathepsin B and autophagy in curcumin-induced pyroptosis of MCF-7 breast cancer cells. Results In mouse model of breast cancer, we observed that curcumin treatment significantly induced cell autophagy and pyroptosis. In human breast cancer MCF-7 cells, we found that curcumin induced pyroptotic cell death was dependent on the activation of NLRP3/Caspase-1/GSDMD signaling pathway, which was CTSB-dependent. In addition, curcumin-induced cell autophagy caused lysosomal rupture and CTSB release. Furthermore, NLRP3 inhibitor (MCC950) significantly suppressed curcumin-induced pyroptosis, as well as CTSB inhibitor (CA074 Me) and autophagy inhibitor (3-MA). Besides, we also found that curcumin suppressed cell proliferation, cell migration and tube formation, which could be reversed by inhibitors. Conclusions In summary, our results demonstrated that curcumin induced MCF-7 cell pyroptosis by the activation of autophagy/CTSB/NLRP3/Caspase-1/GSDMD signaling pathway. These findings offer novel insights into the potential molecular mechanisms of curcumin in treatment of breast cancer.

Objective：This study described the epidemic characteristics of varicella in Dalian from 2009 to 2019, explored the fitting effect of Grey model first-order one variable( GM(1,1)), Markov model, and GM(1,1)-Markov model on varicella data, and found the best fitting method for this type of data, to better predict the incidence trend.Methods: In this study, the epidemiological characteristics of varicella from 2009 to 2019 were analyzed by epidemiological descriptive methods. Using the varicella incidence data from 2009 to 2018, predicted 2019 and compared with actual value. First made GM (1,1) prediction and Markov prediction. Then according to the relative error of the GM(1,1), made GM(1,1)-Markov prediction. Results: This study collected 37223 cases from 2009 to 2019. The average annual incidence was 50.56/100000. Varicella occurred all year round, it had a bimodal distribution. The number of cases had two peaks from April to June and November to January of the following year. The ratio of males to females was 1.167:1. The 4 to 25 accounted for 60.36% of the total population. The age of varicella appeared to shift backward. Students, kindergarten children, scattered children accounted for about 64% of all cases. The GM(1,1) model prediction result of 2019 would be 53.6425, the relative error would be 14.42%, the Markov prediction result would be 56.2075, the relative error would be 10.33%, and the Gray(1,1)-Markov prediction result would be 59.508. The relative error would be 5.06%.Conclusions: Varicella data had its unique development characteristics. The accuracy of GM (1,1) - Markov model is higher than GM(1.1) model and Markov model. The model can be used for prediction and decision guidance.