Objective: To investigate the trends in incidence and mortality of lung cancer in Huangpu District, Shanghai Municipality from 2002 to 2019, so as to provide the evidence for formulating lung cancer prevention and control measures. Methods: Data of lung cancer incidence and mortality among residents in Huangpu District from 2002 to 2019 were collected through the Shanghai Cancer Registration and Reporting Management System. The crude incidence and mortality of lung cancer was calculated, and standardized by the data from the Chinese Fifth National Population Census in 2000 (Chinese-standardized rate) and the Segi's world standard population in 1960 (world-standardized rate). The trends in incidence and mortality of lung cancer among residents by age and gender were evaluated using annual percent change (APC). Results: A total of 12 965 cases of lung cancer were reported in Huangpu District from 2002 to 2019, and the crude incidence rate was 80.66/105, the Chinese-standardized incidence rate was 34.54/105, and the world-standardized incidence rate was 31.30/105, all showing upward trends (APC=4.588%, 2.933% and 3.247%, all P<0.05). A total of 10 102 deaths of lung cancer were reported, and the crude mortality rate was 62.30/105, showing an upward trend (APC=0.959%, P<0.05); the Chinese-standardized mortality was 25.93/105, and the world-standardized mortality was 22.05/105, both showing downward trends (APC=-1.282% and -1.263%, both P<0.05). The crude incidence and mortality rates of lung cancer in males were higher than those in females (101.39/105 vs. 60.52/105, 85.45/105 vs. 39.87/105, both P<0.05). The crude incidence and mortality rates of lung cancer showed upward trends with age (both P<0.05), reaching their peaks in the age groups of 80-<85 years (341.37/105) and 85 years or above (355.97/105), respectively. Conclusions The incidence of lung cancer showed an upward trend, while the mortality showed a downward trend in Huangpu District from 2002 to 2019. Elderly men were the high-risk group for lung cancer incidence and mortality.

ObjectiveTo explore the effect of Shenge Bushen Capsules （SBC） on sexual development in normal 3-week-old mice. MethodsThe experiment consisted of two parts. In the first part， mice were divided into four groups： The control group and the low， medium， and high-dose SBC groups （234.7， 469.4， 938.7 mg·kg^-1， respectively）. In the second part， mice were divided into four groups： Control group， Pseudostellariae Radix polysaccharide （PRP） group， total flavonoids group， and SBC group， all receiving a dose of 469.4 mg·kg^-1. After 7 days of administration， the vaginal opening of female mice and the descent of testes and scrotum in male mice， as well as the ovarian and testicular organ indices， were observed. After 4 weeks of administration， female and male mice were housed together for 2 days， and the pregnancy rate of females was monitored. After delivery， the pregnant female mice continued receiving the treatment for 4 weeks， and the sexual development of their offspring， including vaginal opening， testicular descent， and organ indices of ovaries and testes， was observed. Serum sex hormones were measured by enzyme-linked immunosorbent assay （ELISA）， and the expression of gonadotropin-releasing hormone （GnRH） and growth hormone （GH） proteins in the hypothalamus was assessed by Western blot. ResultsCompared with the control group， there was no significant effect on the vaginal opening of female mice or the descent of testes in male mice after 7 days of SBC administration. After 4 weeks of administration， the pregnancy rate in the low-dose group was significantly reduced （P<0.05）， but no significant effects were observed in the other groups. The three doses of SBC did not significantly affect the ovarian or testicular organ indices， and there was no significant upregulation in the expression of GnRH or GH in the hypothalamus. The primary component of SBC， Pseudostellariae Radix polysaccharide， significantly reduced the vaginal opening in female mice after 7 days of administration （P<0.05）. After 4 weeks， the serum estradiol levels of non-pregnant female mice were decreased （P<0.05）， but there was no significant effect on the expression of GnRH or GH proteins in the hypothalamus of either male or female mice. Additionally， there were no significant effects on precocious puberty indicators， such as vaginal opening and testicular descent， in the offspring mice. ConclusionSBC does not significantly promote precocious puberty in young mice， and it does not have any noticeable effects on the pregnancy rate of adult mice or the sexual development of their offspring.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective:To explore the association between uric acid and new-onset chronic kidney disease (CKD) in middle-aged and elderly hypertensive patients.Methods:This was a retrospective cohort study. Middle-aged and elderly hypertensive patients who had attended at least two annual health examinations at Yongshun Community Health Service Center in Tongzhou District, Beijing, from June 2016 to December 2020 were enrolled. The time interval between the two physical examinations was three years. The first physical examination time served as the baseline, and the second as the end of follow-up. Based on the uric acid level at baseline, the participants were divided into the normal uric acid group and the hyperuricemia group. The relevant clinical data of the participants were collected. The endpoint of the study was new-onset CKD. A multivariate logistic regression model was used to analyze the association between uric acid and new-onset CKD in hypertensive patients.Results:A total of 2 472 middle-aged and elderly hypertensive patients with an average age of (62.43±7.02) years were included. Of these, 733(29.7%) were male. There were 710 patients with hyperuricemia (hyperuricemia group) and 1 762 patients with normal uric acid levels (normal uric acid group).After adjusting for age, sex, body mass index (BMI), systolic blood pressure, diabetes mellitus, estimated glomerular filtration rate (eGFR), and uric acid-lowering treatment, multivariate logistic regression analysis showed that combined with hyperuricemia was an independent risk factor for new-onset CKD in middle-aged and elderly hypertensive patients ( OR=3.00, 95% CI: 1.87-4.80, P<0.001). The results of multivariate logistic analysis showed that elevated uric acid level was an independent risk factor for new-onset CKD in both male and female middle-aged and elderly hypertensive patients (both P<0.05), and there was no sex interaction ( P for interactio n>0.05). The results of multivariate logistic analysis showed that the combination of asymptomatic hyperuricemia was an independent risk factor for new-onset CKD in middle-aged and elderly hypertensive patients ( OR=3.00, 95% CI: 1.87-4.80, P<0.001), and there was no gender interaction ( P for interactio n>0.05). Conclusions:Hyperuricemia is an independent risk factor for new-onset CKD in middle-aged and elderly hypertensive patients, and elevated uric acid levels increase the risk of new-onset CKD in both male and female patients. Moreover, asymptomatic hyperuricemia may increase the risk of new-onset CKD.

Objective:This study identifies independent predictive indicators to distinguish autoimmune gastritis from Helicobacter pylori ( H. pylori)-induced atrophic gastritis and validates their diagnostic performance to compare laboratory indicators of autoimmune gastritis and H. pylori-induced atrophic gastritis. Methods:A retrospective comparison of laboratory examination indicators was conducted for chronic atrophic gastritis patients with involvement of the gastric fundus and corpus, who were followed up at the Department of Gastroenterology, Xijing Hospital, from January 2014 to September 2024. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cutoff points and corresponding diagnostic thresholds. In addition, multivariate logistic regression analysis was conducted to identify independent predictive indicators for autoimmune gastritis, with further assessment in a validation cohort.Results:A total of 139 patients with autoimmune gastritis and 209 patients with H. pylori-induced atrophic gastritis were included. Pepsinogen (PG) Ⅰ levels and the PG Ⅰ/PG Ⅱ ratio in patients with autoimmune gastritis were significantly lower than in those with H. pylori-induced atrophic gastritis [11.0 (4.8, 22.5) vs. 41.8 (32.2, 59.9) μg/L, U=722.00, P<0.001; 1.24 (0.75, 3.54) vs. 5.76 (4.31, 7.12), U=817.00, P<0.001], while gastrin levels were significantly higher [375 (84, 738) vs. 49 (35, 81) ng/L, U=378.00, P<0.001]. PG Ⅰ was identified as an independent predictive variable, with an area under the ROC curve of 0.847 (95% CI 0.791-0.904), sensitivity of 77.6%, specificity of 91.8%, positive predictive value of 80.5%, and negative predictive value of 90.5%. Conclusions:Significant differences in laboratory indicators were observed between autoimmune gastritis and H. pylori-induced atrophic gastritis in chronic atrophic gastritis involving gastric fundus and corpus. Besides, PG Ⅰ demonstrated good diagnostic performance in identifying autoimmune gastritis and can effectively differentiate between different types of atrophic gastritis.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective:To explore the association between uric acid and new-onset chronic kidney disease (CKD) in middle-aged and elderly hypertensive patients.Methods:This was a retrospective cohort study. Middle-aged and elderly hypertensive patients who had attended at least two annual health examinations at Yongshun Community Health Service Center in Tongzhou District, Beijing, from June 2016 to December 2020 were enrolled. The time interval between the two physical examinations was three years. The first physical examination time served as the baseline, and the second as the end of follow-up. Based on the uric acid level at baseline, the participants were divided into the normal uric acid group and the hyperuricemia group. The relevant clinical data of the participants were collected. The endpoint of the study was new-onset CKD. A multivariate logistic regression model was used to analyze the association between uric acid and new-onset CKD in hypertensive patients.Results:A total of 2 472 middle-aged and elderly hypertensive patients with an average age of (62.43±7.02) years were included. Of these, 733(29.7%) were male. There were 710 patients with hyperuricemia (hyperuricemia group) and 1 762 patients with normal uric acid levels (normal uric acid group).After adjusting for age, sex, body mass index (BMI), systolic blood pressure, diabetes mellitus, estimated glomerular filtration rate (eGFR), and uric acid-lowering treatment, multivariate logistic regression analysis showed that combined with hyperuricemia was an independent risk factor for new-onset CKD in middle-aged and elderly hypertensive patients ( OR=3.00, 95% CI: 1.87-4.80, P<0.001). The results of multivariate logistic analysis showed that elevated uric acid level was an independent risk factor for new-onset CKD in both male and female middle-aged and elderly hypertensive patients (both P<0.05), and there was no sex interaction ( P for interactio n>0.05). The results of multivariate logistic analysis showed that the combination of asymptomatic hyperuricemia was an independent risk factor for new-onset CKD in middle-aged and elderly hypertensive patients ( OR=3.00, 95% CI: 1.87-4.80, P<0.001), and there was no gender interaction ( P for interactio n>0.05). Conclusions:Hyperuricemia is an independent risk factor for new-onset CKD in middle-aged and elderly hypertensive patients, and elevated uric acid levels increase the risk of new-onset CKD in both male and female patients. Moreover, asymptomatic hyperuricemia may increase the risk of new-onset CKD.

Objective:This study identifies independent predictive indicators to distinguish autoimmune gastritis from Helicobacter pylori ( H. pylori)-induced atrophic gastritis and validates their diagnostic performance to compare laboratory indicators of autoimmune gastritis and H. pylori-induced atrophic gastritis. Methods:A retrospective comparison of laboratory examination indicators was conducted for chronic atrophic gastritis patients with involvement of the gastric fundus and corpus, who were followed up at the Department of Gastroenterology, Xijing Hospital, from January 2014 to September 2024. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cutoff points and corresponding diagnostic thresholds. In addition, multivariate logistic regression analysis was conducted to identify independent predictive indicators for autoimmune gastritis, with further assessment in a validation cohort.Results:A total of 139 patients with autoimmune gastritis and 209 patients with H. pylori-induced atrophic gastritis were included. Pepsinogen (PG) Ⅰ levels and the PG Ⅰ/PG Ⅱ ratio in patients with autoimmune gastritis were significantly lower than in those with H. pylori-induced atrophic gastritis [11.0 (4.8, 22.5) vs. 41.8 (32.2, 59.9) μg/L, U=722.00, P<0.001; 1.24 (0.75, 3.54) vs. 5.76 (4.31, 7.12), U=817.00, P<0.001], while gastrin levels were significantly higher [375 (84, 738) vs. 49 (35, 81) ng/L, U=378.00, P<0.001]. PG Ⅰ was identified as an independent predictive variable, with an area under the ROC curve of 0.847 (95% CI 0.791-0.904), sensitivity of 77.6%, specificity of 91.8%, positive predictive value of 80.5%, and negative predictive value of 90.5%. Conclusions:Significant differences in laboratory indicators were observed between autoimmune gastritis and H. pylori-induced atrophic gastritis in chronic atrophic gastritis involving gastric fundus and corpus. Besides, PG Ⅰ demonstrated good diagnostic performance in identifying autoimmune gastritis and can effectively differentiate between different types of atrophic gastritis.