With the change of infectious disease incidence pattern and the development of related technologies, progresses have been made in the research of infectious disease epidemiology. In recent years, due to the change in the requirements of infectious disease prevention and control, the research focus has expanded from common infectious diseases to diseases which have been eliminated or might be eliminated, as well as emerging and re-emerging infectious diseases. Infectious disease data has been characterized by multiple sources and modalities. Along with the rapid development of pathogen detection methods, infectious disease surveillance has shifted from a single disease-targted one to a comprehensive one. Moreover, novel technologies such as multi-omics and artificial intelligence have been applied in infectious disease epidemiology research. The international cooperation in this field has become increasingly crucial, and the revision of the International Health Regulations and the negotiation of pandemic agreement will have a profound impact. In the future, infectious disease epidemiology research will develop with more powerful tools to improve its capabilities.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective To investigate the levels of gross α and gross β activities in different water types within a 40-kilometer radius around the Zhangzhou Nuclear Power Plant prior to its operation. Methods In 2018, drinking water samples were collected from the area surrounding the nuclear power plant during both the wet and dry seasons, including source water, treated water, tap water, and well water. The gross α and gross β activity concentrations were measured using a low-background α/β counter, followed by statistical analysis. Results A total of 80 water samples from different sources around the Zhangzhou Nuclear Power Plant were collected. The average gross α and gross β activity concentrations during the wet season were (0.110 ± 0.036) Bq/L and (0.643 ± 0.028) Bq/L, respectively, while those during the dry season were (0.124 ± 0.032) Bq/L and (0.624 ± 0.026) Bq/L, respectively. There were no significant differences in the gross α and gross β activity concentrations between the wet and dry seasons for the overall sample set (P > 0.05). However, there were statistically significant differences in the gross α and gross β activity concentrations between the wet and dry seasons for source water and well water (Z_wet = −2.005, −2.123; Z_dry = −1.943, −3.090; P < 0.05). Conclusion The radioactivity levels in different water types within various ranges around the Zhangzhou Nuclear Power Plant before its operation were determined. The measured activity concentrations were at the same level as those from previous investigations in other regions of Fujian Province.

Objective To investigate the diagnostic and prognostic value of serum leucine-rich alpha-2-glycoprotein 1(LRG1)and dipeptidyl peptidase 4(DPP4)levels in patients with acute cerebral watershed infarction(ACWI).Methods Selected 150 ACWI patients treated in the Baoding Second Central Hospital from January 2022 to December 2023 as the study subjects(ACWI group),and another 120 volunteers who underwent physical examinations were regarded as the control group.According to the prognosis of ACWI patients,they were separated into a good prognosis group(n=98)and a poor prognosis group(n=52).ELISA was used to detect serum LRG1,DPP4 and carcinoembryonic amtigen(CEA)levels,a biochemical analyzer was used to detect levels of albumin(ALB),adenosine deaminase(ADA),creatinine(Cre).Multivariate logistic regression was applied to analyze the influencing factors of poor prognosis in ACWI patients.Spearman correlation analysis of LRG1 and DPP4 levels with NIHSS and mRS scores in the ACWI group.The ROC curve was applied to analyze the diagnostic value of LRG1 and DPP4 levels for the occurrence of ACWI and prognosis,and Z-test was used to compare the differences in AUC.Results The serum LRG1(56.03±16.11pg/ml)and DPP4(9.90±3.25ng/L)levels in ACWI patients were higher than those in the control group(41.78±12.54pg/ml,7.34±2.32ng/L),the differences were statistically significant(t=7.951,7.272,all P＜0.001).ACWI patients with poor prognosis had higher National Institute of Health Stroke Scale NIHSS scores,mRS scores,larger infarct proportion,LRG1 and DPP4 levels than those with good prognosis(t/χ2=3.258～17.208),but had lower ALB levels than those with good prognosis(t=3.143),the differences were statistically significant(all P＜0.001).Multivariate logistic regression analysis showed that large area infarction,increased NIHSS score,mRS score,LRG1 and DPP4 levels were independent risk factors for poor prognosis in ACWI patients(Wald χ2=4.358～6.000,all P<0.05),while elevated ALB was an independent protective factor for poor prognosis in ACWI patients(Wald χ2=4.535,P<0.05).Spearman correlation analysis showed a positive correlation between serum LRG1 and DPP4 levels in ACWI patients(r=0.446,P<0.001).ROC curve analysis showed that the AUC(95%CI)for diagnosing ACWI with serum LRG1 and DPP4 were 0.788(0.734～0.835)and 0.790(0.736～0.837),respectively,while the AUC(95%CI)for combined diagnosis was 0.922(0.883～0.951),which was better than individual diagnosis(Z=5.798,5.612,all P<0.05).The AUC(95%CI)of LRG1 and DPP4 in diagnosing ACWI patients with poor prognosis was 0.796(0.722～0.857)and 0.800(0.727～0.861),respectively,and the AUC(95%CI)of combined diagnosis was 0.924(0.869～0.961),which was better than their respective individual diagnoses(Z=2.891,4.222,all P<0.05).Conclusion LRG1 and DPP4 levels are higher in the serum of ACWI patients and higher in patients with poor prognosis.The two levels are positively correlated,and the combination has a certain value in diagnosing the occurrence of ACWI and poor prognosis,which provides a theoretical basis for clinical diagnosis.

With the change of infectious disease incidence pattern and the development of related technologies, progresses have been made in the research of infectious disease epidemiology. In recent years, due to the change in the requirements of infectious disease prevention and control, the research focus has expanded from common infectious diseases to diseases which have been eliminated or might be eliminated, as well as emerging and re-emerging infectious diseases. Infectious disease data has been characterized by multiple sources and modalities. Along with the rapid development of pathogen detection methods, infectious disease surveillance has shifted from a single disease-targted one to a comprehensive one. Moreover, novel technologies such as multi-omics and artificial intelligence have been applied in infectious disease epidemiology research. The international cooperation in this field has become increasingly crucial, and the revision of the International Health Regulations and the negotiation of pandemic agreement will have a profound impact. In the future, infectious disease epidemiology research will develop with more powerful tools to improve its capabilities.

Objective To investigate the diagnostic and prognostic value of serum leucine-rich alpha-2-glycoprotein 1(LRG1)and dipeptidyl peptidase 4(DPP4)levels in patients with acute cerebral watershed infarction(ACWI).Methods Selected 150 ACWI patients treated in the Baoding Second Central Hospital from January 2022 to December 2023 as the study subjects(ACWI group),and another 120 volunteers who underwent physical examinations were regarded as the control group.According to the prognosis of ACWI patients,they were separated into a good prognosis group(n=98)and a poor prognosis group(n=52).ELISA was used to detect serum LRG1,DPP4 and carcinoembryonic amtigen(CEA)levels,a biochemical analyzer was used to detect levels of albumin(ALB),adenosine deaminase(ADA),creatinine(Cre).Multivariate logistic regression was applied to analyze the influencing factors of poor prognosis in ACWI patients.Spearman correlation analysis of LRG1 and DPP4 levels with NIHSS and mRS scores in the ACWI group.The ROC curve was applied to analyze the diagnostic value of LRG1 and DPP4 levels for the occurrence of ACWI and prognosis,and Z-test was used to compare the differences in AUC.Results The serum LRG1(56.03±16.11pg/ml)and DPP4(9.90±3.25ng/L)levels in ACWI patients were higher than those in the control group(41.78±12.54pg/ml,7.34±2.32ng/L),the differences were statistically significant(t=7.951,7.272,all P＜0.001).ACWI patients with poor prognosis had higher National Institute of Health Stroke Scale NIHSS scores,mRS scores,larger infarct proportion,LRG1 and DPP4 levels than those with good prognosis(t/χ2=3.258～17.208),but had lower ALB levels than those with good prognosis(t=3.143),the differences were statistically significant(all P＜0.001).Multivariate logistic regression analysis showed that large area infarction,increased NIHSS score,mRS score,LRG1 and DPP4 levels were independent risk factors for poor prognosis in ACWI patients(Wald χ2=4.358～6.000,all P<0.05),while elevated ALB was an independent protective factor for poor prognosis in ACWI patients(Wald χ2=4.535,P<0.05).Spearman correlation analysis showed a positive correlation between serum LRG1 and DPP4 levels in ACWI patients(r=0.446,P<0.001).ROC curve analysis showed that the AUC(95%CI)for diagnosing ACWI with serum LRG1 and DPP4 were 0.788(0.734～0.835)and 0.790(0.736～0.837),respectively,while the AUC(95%CI)for combined diagnosis was 0.922(0.883～0.951),which was better than individual diagnosis(Z=5.798,5.612,all P<0.05).The AUC(95%CI)of LRG1 and DPP4 in diagnosing ACWI patients with poor prognosis was 0.796(0.722～0.857)and 0.800(0.727～0.861),respectively,and the AUC(95%CI)of combined diagnosis was 0.924(0.869～0.961),which was better than their respective individual diagnoses(Z=2.891,4.222,all P<0.05).Conclusion LRG1 and DPP4 levels are higher in the serum of ACWI patients and higher in patients with poor prognosis.The two levels are positively correlated,and the combination has a certain value in diagnosing the occurrence of ACWI and poor prognosis,which provides a theoretical basis for clinical diagnosis.

OBJECTIVE To analyze the diagnosis of latent tuberculosis infection(LTBI)in hospitalized patients of a tertiary general hospital and investigate the current status of related comorbidities.METHODS The clinical data were collected from the 14 448 hospitalized patients who received tuberculin skin test(TST)or interferon-gamma release assay(IGRA)in China-Japan Friendship Hospital from Jan.1,2022 to Dec.31,2023,and the results were assessed.RESULTS The detection rate of LTBI was 23.62％(3413/14448)among the hospitalized patients who received the tests,and the rate of definite diagnosis was only 4.22％(144/3413).88.40％(3017/3413)of the hospitalized patients with LTBI had at least one type of comorbidity,and the top 5 comorbidities were in turn as follows:high blood pressure,hyperlipidemia,diabetes mellitus,malignant tumors and rheumatic immune disea-ses;the number of comorbidities was increased with the age(x2=291.199,P＜0.001).The rheumatic immune disease(73/144,50.69％)was the most common type of comorbidity among the hospitalized patients with definite diagnosis of LTBI,and less than half of the patients(66/144,45.83％)were treated in rheumatology and immu-nology department.CONCLUSION The two-way screening of LTBI and comorbidities is the core premise for the standardized management of LTBI.

OBJECTIVE To analyze the diagnosis of latent tuberculosis infection(LTBI)in hospitalized patients of a tertiary general hospital and investigate the current status of related comorbidities.METHODS The clinical data were collected from the 14 448 hospitalized patients who received tuberculin skin test(TST)or interferon-gamma release assay(IGRA)in China-Japan Friendship Hospital from Jan.1,2022 to Dec.31,2023,and the results were assessed.RESULTS The detection rate of LTBI was 23.62％(3413/14448)among the hospitalized patients who received the tests,and the rate of definite diagnosis was only 4.22％(144/3413).88.40％(3017/3413)of the hospitalized patients with LTBI had at least one type of comorbidity,and the top 5 comorbidities were in turn as follows:high blood pressure,hyperlipidemia,diabetes mellitus,malignant tumors and rheumatic immune disea-ses;the number of comorbidities was increased with the age(x2=291.199,P＜0.001).The rheumatic immune disease(73/144,50.69％)was the most common type of comorbidity among the hospitalized patients with definite diagnosis of LTBI,and less than half of the patients(66/144,45.83％)were treated in rheumatology and immu-nology department.CONCLUSION The two-way screening of LTBI and comorbidities is the core premise for the standardized management of LTBI.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.