The ninth edition of TNM staging for lung cancer has been announced at the 2023 World Lung Cancer Congress and implemented from January 1, 2024. The focus of the ninth TNM staging change is dividing N2 into N2a and N2b, as well as M1c into M1c1 and M1c2. Although the T staging has not changed, it has played an important role in verifying the eighth edition of the T staging. The subdivision of stage N2 has led some patients with ⅢA of the eighth edition to experience ascending or descending stages, which will more accurately help to assess the condition and prognosis of patients with mediastinal lymph node metastasis, as well as the design of related clinical studies. Modifying the M1c staging will help define oligometastasis and explore new treatment models in the future. The ninth edition of the TNM staging system provides a more detailed division of different tumor loads, but there is no clear explanation for the staging of lung cancer after neoadjuvant therapy. Further data analysis is needed, and it is expected to be answered in the tenth edition of TNM staging.

Objective:To analyze the epidemic characteristics of reported tuberculosis incidence in Kashgar from 2015 to 2022, and use the seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence, providing references for the local control of pulmonary tuberculosis.Methods:The reported incidence data of tuberculosis in the Kashgar area of Xinjiang from January 2015 to August 2023 were collected through the"Infectious Disease Monitoring System", a subsystem of the "Chinese Disease Prevention and Control Information System". The epidemic characteristics of reported incidence in this area from 2015 to 2022 were analyzed. Two SARIMA models of monthly reported incidence number and rate were established. The prediction performance of the two models was evaluated using the reported incidence data of tuberculosis from January 2023 to August 2023. The χ2 test was used to analyze population characteristics, and the Cochran-Armitage trend test was used to analyze annual incidence. Results:From 2015 to 2022, 133 972 cases of pulmonary tuberculosis were reported in Kashgar, with a yearly reported incidence rate of 383.64/100 000, showing a rising trend ( TCA=77.03, P<0.001) and then a declining trend ( TCA=176.16, P<0.001). The proportion of pathogenic positive pulmonary tuberculosis had increased yearly ( TCA=132.66, P<0.001). The reported onset time was concentrated from January to June each year, with a peak in April. Yengisar County, Zepu County and Yopurga County had the highest reported incidence rate in Kashgar. The sex ratio of men to women was 1.03∶1, and the reported incidence rate of men was higher than that of women ( χ2=27.04, P<0.001). The reported incidence rate of the group aged 60 years and older was the highest. The patient′s occupation was mainly farmers (84.99%). The average relative errors of the SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model in predicting the reported monthly incidence number and rate were 11.67% and -9.81%, respectively. Both models had good prediction accuracy (MAPE=33.55%, MAPE=38.22%). Conclusion:The average reported incidence rate of pulmonary tuberculosis in the Kashgar area shows a rising trend first and then a declining trend. The patients are mainly men and farmers, and attention should be paid to the prevention and control of tuberculosis among the elderly in winter and spring. The SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model can fit the trend of reported tuberculosis incidence in the Kashgar area well and have good predictive performance.

The ninth edition of TNM staging for lung cancer has been announced at the 2023 World Lung Cancer Congress and implemented from January 1, 2024. The focus of the ninth TNM staging change is dividing N2 into N2a and N2b, as well as M1c into M1c1 and M1c2. Although the T staging has not changed, it has played an important role in verifying the eighth edition of the T staging. The subdivision of stage N2 has led some patients with ⅢA of the eighth edition to experience ascending or descending stages, which will more accurately help to assess the condition and prognosis of patients with mediastinal lymph node metastasis, as well as the design of related clinical studies. Modifying the M1c staging will help define oligometastasis and explore new treatment models in the future. The ninth edition of the TNM staging system provides a more detailed division of different tumor loads, but there is no clear explanation for the staging of lung cancer after neoadjuvant therapy. Further data analysis is needed, and it is expected to be answered in the tenth edition of TNM staging.

Objective:To analyze the epidemic characteristics of reported tuberculosis incidence in Kashgar from 2015 to 2022, and use the seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence, providing references for the local control of pulmonary tuberculosis.Methods:The reported incidence data of tuberculosis in the Kashgar area of Xinjiang from January 2015 to August 2023 were collected through the"Infectious Disease Monitoring System", a subsystem of the "Chinese Disease Prevention and Control Information System". The epidemic characteristics of reported incidence in this area from 2015 to 2022 were analyzed. Two SARIMA models of monthly reported incidence number and rate were established. The prediction performance of the two models was evaluated using the reported incidence data of tuberculosis from January 2023 to August 2023. The χ2 test was used to analyze population characteristics, and the Cochran-Armitage trend test was used to analyze annual incidence. Results:From 2015 to 2022, 133 972 cases of pulmonary tuberculosis were reported in Kashgar, with a yearly reported incidence rate of 383.64/100 000, showing a rising trend ( TCA=77.03, P<0.001) and then a declining trend ( TCA=176.16, P<0.001). The proportion of pathogenic positive pulmonary tuberculosis had increased yearly ( TCA=132.66, P<0.001). The reported onset time was concentrated from January to June each year, with a peak in April. Yengisar County, Zepu County and Yopurga County had the highest reported incidence rate in Kashgar. The sex ratio of men to women was 1.03∶1, and the reported incidence rate of men was higher than that of women ( χ2=27.04, P<0.001). The reported incidence rate of the group aged 60 years and older was the highest. The patient′s occupation was mainly farmers (84.99%). The average relative errors of the SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model in predicting the reported monthly incidence number and rate were 11.67% and -9.81%, respectively. Both models had good prediction accuracy (MAPE=33.55%, MAPE=38.22%). Conclusion:The average reported incidence rate of pulmonary tuberculosis in the Kashgar area shows a rising trend first and then a declining trend. The patients are mainly men and farmers, and attention should be paid to the prevention and control of tuberculosis among the elderly in winter and spring. The SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model can fit the trend of reported tuberculosis incidence in the Kashgar area well and have good predictive performance.

Objective: To analyze the incidence of leptospirosis in Fujian province from 2015 to 2020 and provide the scientific evidences for the risk assessment, prevention and control of leptospirosis. Methods: The incidence data of leptospirosis in Fujian during 2015-2020 were collected from China Information System for Disease Control and Prevention for a descriptive analysis, and software ArcGIS 10.3.1 was used for spatial autocorrelation analysis, and rats were captured in 17 surveillance areas during the same period, and the rat organs were collected for pathogen culture, the level of Leptospira antibody was detected in serum samples of rats, healthy population and the serum samples of patients sent by the hospitals. The infection status of Leptospira in human and rats were analyzed. Results: The incidence of leptospirosis in Fujian showed a downward trend from 2015 to 2020. A total of 176 cases of leptospirosis were reported. There were obvious seasonality and bimodal distribution. The majority of cases were farmers, accounting for 49.43% (87/176). Most cases were aged 30-69 years (85.80%, 151/176). The male to female ratio of the cases was 3.51∶1 (137∶39). Spatial autocorrelation analysis showed that leptospirosis had high or low clustering areas. From 2015 to 2020, the average capture rate of rats in 17 surveillance areas was 6.96% (1 519/21 838), Rattus losea, Rattus flavipectus and Niviventer fulvescens were the main species. The average positive rate of Leptospira antibody in rats was 28.64% (252/880). Java and Autumnalis were the predominant serogroups, accounting for 56.75% (143/252) and 17.46% (44/252), respectively. The average positive rate of Leptospira antibody in healthy population was 16.13% (254/1 575), and Autumnalis and Australis were the predominant serogroups, accounting for 71.65% (182/254). The confirmation rate of leptospirosis in patient serum samples sent by the hospitals was 2.23% (188/8 431), Autumnalis (56.38%, 106/188) and Hebdomadis (19.68%, 37/188) were the major serogroups. Conclusions: The incidence of leptospirosis in Fujian showed a downward trend from 2015 to 2020, there were obvious area clustering and seasonality. The high clustering areas were mainly distributed in northern, western and central Fujian. Java and Autumnalis were the predominant serogroups in rats. The infection rate in healthy population decreased year by year. Autumnalis and Hebdomadis were the main serogroups in population in Fujian.
Animals ; Antibodies, Bacterial ; Female ; Humans ; Incidence ; Leptospira ; Leptospirosis/epidemiology* ; Male ; Rats ; Serogroup

ObjectiveTo isolate and study the biological characteristics of severe acute respiratory syndrome coronavirus 2 （SARS-CoV-2） from feces of coronavirus disease 2019 （COVID-19） patients. MethodsVero E6 cells were used for virus isolation and the isolated strains were tested by nucleic acid test， immunofluorescence test， virulence test and whole genome sequencing. 50% tissue culture infective dose （TCID₅₀） was calculated after the cell cultures of each generation were collected ResultsEight fecal specimens were inoculated with Vero E6 cells after treatment and cultured for 48 h. One specimen showed obvious cytopathic effect on Vero E6 cells. One SARS-CoV-2 out of 8 fecal samples from COVID-19 patients were isolated， and separation rate was 12.5%. The TCID₅₀ of P1， P2 and P3 were 10^4.0/0.2 mL， 10^4.5/0.2 mL and 10^4.75/0.2 mL， respectively. Only one of the 8 stool samples had SARS-CoV-2 virus replication and amplification， and the Ct value of the nucleic acid detection was about 10. The sequence of the isolation was more than 99.99% homologous with that of Wuhan-Hu-1（GenBank MN908947）. ConclusionThe SARS-CoV-2 strain is isolated from the fecal samples of COVID-19 cases and is confirmed by genomic sequencing and immunofluorescence test， which indicates the presence of live virus in feces of COVID-19 cases.

Objective:To observe the stability of severe acute respiratory syrdrome coronavirus （SARS-CoV-2） in cell cultures at different temperatures so as to provide basic data and scientific basis for the research and control of COVID-19 epidemic. Methods:The Vero E6 cells inoculated with SARS-CoV-2. According to TCID₅₀， SARS-CoV-2 with different dilution （10^-1， 10^-3， 10^-5， 10^-6）were stored at 37 °C， 22.5 °C， and 4 °C for one to seven days， and then infectious titer was determined by micro cytopathogenic effect assay， observing cytopathic effect （CPE）， and real-time fluorescence quantitative testing. Results:SARS-CoV-2 was stable under 4 °C. The infectivity of high concentration （10^-1 dilution） under 22.5 °C for seven days gradually decreased， while lower concentration completely lost infectivity after one day. The virus lost infectivity when stored at 37 °C for more than one day. Conclusion:SARS-CoV-2 is highly stable at 4 °C， sensitive to heat， and related to virus concentration.