Search

In order to determine the characteristic flavor of Jingyang Fu brick tea, a national geographical indication product, the volatile compounds of Jingyang Fu brick tea, Anhua Fu brick tea and four other dark teas were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS) and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) in this study. The results showed that in total 154 compounds were identified by GC-IMS and HS-SPME-GC-MS, and the flavor composition of Jingyang Fu brick tea was clearly distinguished from that of Anhua Fu-brick tea and that of the other dark teas. Totally 12 characteristic substances of Jingyang Fu brick tea were obtained by comparative analysis with the other dark tea, seven of which were validated by GC-IMS, including 1-octen-3-one, n-hexanol, guaiacol, β-pinene, methyl butyrate, n-propanol and 2-heptanone, and the remaining five were characterized by HS-SPME-GC-MS based on aroma activity values, including hexanal, decanal, (E,E)-3,5-octadien-2-one, methyl salicylate and α-viologenone. The identification of characteristic flavor substances can provide a theoretical basis for the identification, origin tracing and processing optimization of Jingyang Fu brick tea.

Highlights 1. ORF3a was successfully transfected into SW480 cells, and its expression was localized in the cytoplasm of SW480 cells. 2. ORF3a inhibited SW480 cell proliferation, invasion and migration, induced their apoptosis, and blocked them in the G1 phase. 3. ORF3a inhibited colorectal tumor growth and accelerated cancer cell death in vivo. 4. Our study provides new insights into the use of viruses in anti-tumor regimens in colorectal cancer.

Increasing evidence suggested that cholesterol is an important integrant of cell membranes, that plays a key role in tumor progression, immune dysregulation, and pathological changes in epigenetic mechanisms. Based on these theories, there is a growing interest on targeting cholesterol in the treatment of cancer. Here, we comprehensively reviewed the major function of cholesterol on oncogenicity, the therapeutic targets of cholesterol and its metabolites in cancer, and provide detailed insight into the essential roles of cholesterol in mediating immune and epigenetic mechanisms of the tumor microenvironment. It is also worth mentioning that the gut microbiome is an indispensable component of cancer mediation because of its role in cholesterol metabolism. Finally, we summarized recent studies on the potential targets of cholesterol and their metabolism, to provide more therapeutic interventions in oncology.

Background Healthcare workers have played a crucial role in preventing and controlling the COVID-19 pandemic. However, the heightened risk of infection and intense work schedules have not only induced occupational burnout among them but also significantly impacted their mental health and lifestyles. A large number of foreign studies have shown that the COVID-19 pandemic has led to unreasonable diet, reduced exercise, irregular work and rest, and decreased sleep quality among HCWs, increasing the risk of overweight and obesity. Despite this, research on weight and lifestyle changes among Chinese healthcare workers during the pandemic is limited, and the key lifestyle factors contributing to these weight changes remain unclear. Objective To analyze the predictors of overweight and obesity in Chinese healthcare workers by constructing a Bayesian network model, and to provide a scientific basis for the prevention and control of overweight and obesity. Methods In August 2022, Chinese healthcare workers in 100 medical institutions from five provinces/autonomous regions/municipalities were randomly sampled, and the questionnaire (Cronbach's α=0.820, AVCR=63.55%) was prepared by the researchers to collect data. All respondents were required to scan QR code generated by the "Wenjuanxing" to answer the e-questionnaire and submit. The "bnlearn" package of R 4.3.0 software was used to construct a Bayesian network model, and Netica 6.09 software was used for Bayesian network risk prediction. Results The study surveyed a total of 20 261 healthcare workers, of whom females accounted for 67.57% (13 690/20 261) ; The average age was (40.2±9.2) years old; 73.28% (14 848/20 261) had a college or undergraduate education level. In 2019 and 2022, the overweight/obesity rates were 43.06% (8 726/20 261) and 45.71% (9 262/20 261), respectively. From 2019 to 2022, 12.64% (1 458/11 535) of survey respondents' BMI changed from underweight/normal to overweight/obese. The Bayesian network model included a total of 15 nodes, and the amount of consumption of vegetables and fruits, breakfast frequency, alcohol drinking, and appetite were the parent nodes of BMI changing from underweight/normal to overweight/obesity, and when there were "a reduction" in the consumption of vegetables and fruits, "no change" in frequency of eating breakfast, alcohol drinking consumption "no change", and "a great increase" in the appetite the risk of BMI changing from underweight/normal to overweight/obese was the highest (75.00%). And when there were "a great increase" in consumption of vegetables and fruits, "an increase" in the frequency of eating breakfast, "never or rarely" in alcohol drinking and "a reduction" in appetite, the risk of becoming overweight/obese was the lowest (2.04%) . Conclusion Consumption of vegetables and fruits, eating breakfast frequently, drinking alcohol and appetite are the direct predictors of overweight/obesity of Chinese healthcare workers. During the epidemic of major infectious diseases such as the COVID-19, on the premise of ensuring the normal operation of medical and health institutions, a reasonable rotation system is implemented to provide psychological support and lifestyle behavior intervention services, which is conducive to the prevention and control of obesity of healthcare workers.

The evaluation of the scientific research projects is an important procedure before the scientific research projects are approved. The BP neural network and linear neural network are adopted to evaluate the scientific research projects in this paper. The evaluation index system with 12 indexes is set up. The basic principle of the neural network is analyzed and then the BP neural network and linear neural network models are constructed and the output error function of the neural networks is introduced. The Matlab software is applied to set the parameters and calculate the neural networks. By computing a real-world example, the evaluation results of the scientific research projects are obtained and the results of the BP neural network, linear neural network and linear regression forecasting are compared. The analysis shows that the BP neural network has higher efficiency than the linear neural network and linear regression forecasting in the evaluation of the scientific research projects problem. The method proposed in this paper is an effective method to evaluate the scientific research projects.