Objective　To investigate the contamination status of Bacillus cereus in food, the profile of virulence factors carried, and drug sensitivity characteristics in Liaoning Province. Methods　A total of 620 samples were isolated from 15 monitoring sites, covering four major sample types of food products in Liaoning Province between 2021 and 2022. Bacillus cereus was isolated and identified by GB4789.14—2014, and its virulence was assessed using PCR detection of the groEL and gyrB genes specific to the bacterial group, as well as 11 other virulence genes. Broth microdilution method was used to detect the antibiotic sensitivity of the strain. Results　All 79 Bacillus cereus strains were found to carry virulence genes. The detection rates of non-hemolytic enterotoxin genes nheC, nheA, and nheB were 100% (79/79), 93.3% (74/79), and 83.5% (66/79), respectively. The detection rate of enterotoxin entFM gene was 98.7% (78/79). The 79 strains of Bacillus cereus carried 19 types of virulence gene patterns, with IX and XVII gene patterns being the most prevalent, each accounted for 16.5% (13/79). The resistance rates of 79 strains of Bacillus cereus to 10 antibacterial drugs were 100% for both ampicillin and penicillin. The resistance rates to cotrimoxazole, imipenem, erythromycin, clindamycin, ciprofloxacin, and tetracycline 65.8% (52/79), 10.1% (8/79), 73.4% (58/79), 60.8% (48/79), 75.9% (60/79), and 70.9% (56/79) respectively. The resistance rates to chloramphenicol and gentamicin were both 0%. Conclusion　Food from catering services in Liaoning Province shows contamination by Bacillus cereus, with isolated strains carrying at least four kinds of virulence genes. About 100% of the isolates were resistant to ampicillin and penicillin, whereas high sensitivity was observed to chloramphenicol and gentamicin. Active monitoring, timely targeted prevention and control interventions should be strengthened to provide a theoretical basis for future traceability studies.

Objective To screen the differentially expressed proteins in glomeruli during the treatment of lupus nephritis(LN)with hydroxychloroquine sulfate(HCQ).Methods Sixty female NZB/WF1 mice were used.At the age of 28 weeks,40 mice with proteinuria of 3+to 4+were divided into HCQ and control groups.After feeding for 36 weeks,the glomerular proteins were extracted by magnetic beads and analyzed by two-dimensional fluorescence difference gel electrophoresis(2D-DIGE)and matrix-assisted laser desorption/ioniza-tion time-of-flight mass spectrometry(MALDI-TOF-MS).The expression of the candidate proteins in the glomeruli of the LN mice was exa-mined by immunohistochemistry.Results A total of 31 differentially expressed proteins were identified between the two groups,including 24 upregulated and seven downregulated proteins.Conclusion The expression of proteins like RI and ENOA in the glomeruli of LN mice was significantly different during HCQ treatment.These proteins may be involved in the pathogenesis of LN and are therefore potential targets of HCQ in the treatment of LN.

Objective:Analyze the impact of smoking on the mortality and life expectancy of residents in Tianjin in 2019.Methods:Use mortality case-control study method to collect all cause of death cases of residents in Tianjin in 2019 for analysis. After adjusting for the 5-years-old age group, education level, and marital status, the smoking attributed deaths from different diseases of different genders, smoking attributed deaths in different age groups, and their impact on life expectancy were analyzed.Results:The total number of deaths in 2019 was 75 254, with 42 201 males (56.1%). Among male deaths, 3 215 (9.9%) were attributed to smoking, of which 2 157 (50.2%) lung cancer deaths were attributed to smoking; The risk of lung cancer death among smokers was 3.075 times higher than that of non-smokers (95% CI: 2.812-3.364); Among the 33 053 female deaths (43.9%), 1 396 (5.8%) were caused by smoking, with 744 (29.1%) lung cancer deaths attributed to smoking. The age group with the highest number of deaths attributed to smoking for women was the 75-<80 years old age group, followed by the 70-<75 and 80-<85 years old age groups. The age group with the highest proportion of deaths attributed to smoking for men was the 55-<60 years old age group. In addition, smoking accounts for more than 60% of deaths in the 60-<65, 45-<50, 55-<60, and 65-<70 years old age groups. In 2019, the loss of life expectancy attributed to smoking deaths among all residents in Tianjin was 1.13 years, with a loss of 1.15 years for males and 0.57 years for females. The expected life expectancy excluding deaths caused by smoking was 82.92 years, 80.77 years for males and 84.61 years for females. Conclusions:Smoking remains one of the important risk factors for death among residents. Promoting effective measures to reduce smoking rates is an effective way to increase life expectancy.

Objective: To investigate GATA3 expression and the regulatory mechanism of m6A modification in the re- sponse of alveolar epithelial cells to radiation, and to provide a new therapeutic target for radiation-induced lung injury based on its pathogenesis. Methods: Human lung epithelial cell line (A549) and mouse lung epithelial cell line (MLE-12) were exposed to X-ray irradiation with a single dose of 10 Gy (dose rate 1 Gy/min) and 6 Gy (dose rate 0.75 Gy/min), respect- ively. The expression of VIRMA gene (RNA methylase) was inhibited by lipofection of A549 cells and MLE-12 cells with shRNA-VIRMA plasmid and siRNA-VIRMA interfering fragment, respectively. Quantification of m6A RNA methylation was performed by colorimetry. Changes in the expression of mRNAs of VIRMA, GATA3, and epithelial-mesenchymal transition (EMT) markers in irradiated A549 and MLE-12 cells were determined by qRT-PCR. Changes in the expression of VIRMA,  GATA3,  and  EMT  marker  proteins  in  irradiated  A549  and  MLE-12  cells  were  determined  by  Western  blot. Results: Radiation up-regulated the expression of methylase VIRMA in A549 and MLE-12 cells, which in turn enhanced the m6A of total RNA and the expression of GATA3 gene and protein, resulting in EMT. Furthermore, in A549 and MLE-12 cells, interference of the VIRMA gene significantly reduced the expression of GATA3 gene and protein and the expression of EMT-related molecules. Conclusion　 Radiation induces m6A modification in alveolar epithelial cells, which up-regu- lates the expression of GATA3 gene and induces EMT, thus playing an important role in the process of radiation-induced lung injury.

Infection is one of the most common causes of death in patients with multiple myeloma, which can lead to early death. The incidence of infection in patients with (multiple myeloma, MM) complicated with diabetes is further increased. Proteasome inhibitors, immunomodulatory drugs and other new drug therapy improve the prognosis of MM, but glucocorticoids used in combination chemotherapy are easy to induce glucocorticoid diabetes, lead to elevated blood glucose, increase the risk of MM infection, and affect the prognosis of patients. This article reviews the relationship between infection and disease progression in patients with MM complicated with diabetes mellitus, infection-related factors, and prevention and treatment measures of different types of pathogen infection.

In the USA, there were about 1 ‍806 ‍590 new cancer cases in 2020, and 606 520 cancer deaths are expected to have occurred in 2021. Lung cancer has become the leading cause of death from cancer in both men and women (Siegel et al., 2020). Clinical studies show that the five-year survival rate of lung cancer patients after early diagnosis and treatment intervention can reach 80%, compared with that of patients having advanced lung cancer. Thus, the early diagnosis of lung cancer is a key factor to reduce mortality.
Male ; Humans ; Female ; Tomography, X-Ray Computed/methods* ; Algorithms ; Lung Neoplasms/pathology* ; Cluster Analysis

Objective:To evaluate the change trend of smoking prevalence and the composition of main causes of death in Tianjin from 2010 to 2019.Methods:The study was based on the data collected in the Tianjin All Cause of Death Surveillance System from 2010 to 2019. A total of 699 372 cases were included in the study. The smoking prevalence, ex-smoking and never smoking prevalence were calculated according to different death years and gender. The proportion of smoking status was calculated according to the year of birth and gender. The distribution of smokers dying of smoking related diseases were calculated by different age-group. The change trend of smoking rate was analyzed by Joinpoint regression 4.9.0.0 statistical software. The diseases that smokers died of were expressed by proportion.Results:From 2010 to 2019, the smoking rate of dead residents decreased from 28.7% to 21.4%, and the average annual change percentage (AAPC) was -3.0% (95% CI: -3.9%, -2.2%). The same trend was shown in dead residents of different genders. The proportion of ex-smokers showed an upward trend, from 10.5% in 2010 to 14.7% in 2019; The proportion of light smokers increased with the postponement of birth year. The proportion of light smokers born at and before year 1950 was 14.7%, while the proportion of light smokers born at and after year 1981 was as high as 85.9%; the proportion of heavy smokers decreased with the year of birth. Lung cancer and cardiovascular and cerebrovascular diseases were the main causes of death of smokers and quitters. 13.86% of quitters died of lung cancer, which was slightly lower than that of smokers (16.39%) and significantly higher than that of non-smokers (5.55%). With the increase of smoking degree, the proportion of smokers who died of lung cancer also increased gradually, from 9.19% to 19.11%. Conclusions:From 2010 to 2019, the smoking prevalence of dead residents in Tianjin shows a downward trend, which is consistent with the change trend of the overall smoking prevalence of residents in Tianjin. Lung cancer, acute myocardial infarction and ischemic heart disease rank among the top three causes of death among smokers.

Objective:To investigate the prevalence the influencing factors of metabolic syndrome among different age groups in Tianjin district.Methods:According to the ‘2015 Chinese Adult Chronic Disease and Nutrition Surveillance Program’, multi-stage cluster random sampling method was used to extract 42 communities from 7 districts in Tianjin. A total of 1 753 adult residents in the 42 communities were selected as the research subjects to analyze the relationship between the influencing factors and metabolic syndrome with descriptive epidemiological analysis. The χ2 test, non-conditional logistic regression and classification tree models were used to analyze the data. Results:The prevalence rate of metabolic syndrome in Tianjin was 30.6%, and the standardized rate was 24.5%. The prevalence rate of metabolic syndrome in Tianjin was increased with age, which was the highest in elderly people (38.9%, ≥60 years). According to the classification tree model, in youth group (18―44 years old), gender, smoking, subjective feeling of health, annual household income and sleep duration were important influencing factors of metabolic syndrome, And the standardized importance was 100%, 95.59%, 22.55%, 20.94% and 9.89%, respectively. In the middle-aged group (45―59 years old), secondhand smoke exposure, subjective feeling of health, sleep duration, the Chinese Food Pagoda (CHFP) score and living region were important influencing factors of metabolic syndrome, and the standardized importance was 100%, 98.08%, 91.04%, 45.74% and 20.15%, respectively. In the elderly group (≥60 years old), sleep duration, secondhand smoke exposure, gender, the CHFP score and living region were important influencing factors of metabolic syndrome, and the standardized importance was 100%, 46.75%, 41.87%, 41.82% and 7.60%, respectively.Conclusions:The prevalence rate of metabolic syndrome is quite high in Tianjin. Tobacco hazard (smoking and secondhand smoke exposure) and sleep duration are the common influencing factors of metabolic syndrome in all age groups. There are different emphases among different age groups in the distributions of those influencing factors, so pointed interventions should be adopted accordingly.