Objective:To analyze the epidemiological characteristics of severe fever with thrombocytopenia syndrome (SFTS) in Beijing in 2024, to investigate the infection status of reservoir hosts, vector organisms, and baseline human populations, and provide a scientific basis for formulating prevention and control strategies.Methods:Epidemiological surveys were conducted on all confirmed cases. Serum samples from healthy populations and reservoir hosts were collected for SFTSV antibody detection. Questing ticks were monitored using the flagging method. Real-time fluorescent quantitative PCR was employed to detect SFTSV in cases, reservoir hosts, and ticks. Positive samples underwent whole-genome sequencing and genetic evolution analysis.Results:In 2024, Beijing reported 15 locally infected cases with 4 deaths. The age of onset ranged from 50 to 80 years (median: 66 years). Cases showed a certain degree of geographical clustering, with June being the peak month of onset. The affected population was predominantly farmers, with a male-to-female ratio of 3∶2. Animal contact history emerged as a significant risk factor alongside tick bites. Parthenogenetic tick populations were identified in Pinggu district, while SFTSV-carrying ticks were detected in endemic areas (Mentougou, Shijingshan, and Fengtai Districts). Viral presence was also confirmed in ticks or dogs from non-endemic areas. Sequencing and phylogenetic analysis revealed stable clustering of strains into two distinct genotypes (A and B). Antibody-positive individuals were identified in healthy populations from non-endemic areas.Conclusions:The incidence of SFTS in Beijing is increasing, with natural viral circulation already established in non-endemic regions. Enhanced surveillance and adjusted prevention strategies are urgently needed.

Objective:To investigate the role of the Brock, Mayo, and PKUPH combined model in risk stratification of solitary pulmonary nodules (SPNs) in health check-up population.Methods:An ambispective cohort study was conducted on 668 eligible SPNs cases from the health management center in Chongqing General Hospital from June 2018 to June 2019. The exposure condition was prospectively followed or historically retrospected, and the clinical outcomes were prospectively followed. SPNs were classified into benign and malignant groups. Descriptive statistics and univariate analysis were performed to assess the differences in risk characteristics between two groups. Receiver operating characteristic (ROC) curve, clinical decision curve, and Hosmer-Lemeshow goodness-of-fit test were used to evaluate and compare the predictive performance, clinical utility, and calibration of the combined model versus individual Brock, Mayo, and PKUPH models.Results:Among the 668 SPNs cases, 82 (12.28%) were diagnosed as malignant. Age, sex, smoking history, extrapulmonary tumor history, diameter, upper lobe, clear border and spicule sign in the malignant group were significantly different from those in the benign group (all P<0.05). The combined model demonstrated superior predictive performance, clinical utility, and calibration compared to the best-performing individual Brock model [Area under the curve (AUC): 0.88 (95% CI: 0.84-0.92) vs 0.86 (95% CI: 0.82-0.91)]. Besides, multi-grade risk stratification enabled by the combined model was better than binary classification, with the malignant rate of the four risk levels were 0.60%, 4.62%, 14.58% and 56.07%, respectively. Conclusion:The combined model addresses the limitations of individual models in SPNs risk stratification for health examination populations, improving predictive performance, clinical utility, and calibration, while proposing a superior multi-grade risk stratification system.

Objective:To investigate the role of the Brock, Mayo, and PKUPH combined model in risk stratification of solitary pulmonary nodules (SPNs) in health check-up population.Methods:An ambispective cohort study was conducted on 668 eligible SPNs cases from the health management center in Chongqing General Hospital from June 2018 to June 2019. The exposure condition was prospectively followed or historically retrospected, and the clinical outcomes were prospectively followed. SPNs were classified into benign and malignant groups. Descriptive statistics and univariate analysis were performed to assess the differences in risk characteristics between two groups. Receiver operating characteristic (ROC) curve, clinical decision curve, and Hosmer-Lemeshow goodness-of-fit test were used to evaluate and compare the predictive performance, clinical utility, and calibration of the combined model versus individual Brock, Mayo, and PKUPH models.Results:Among the 668 SPNs cases, 82 (12.28%) were diagnosed as malignant. Age, sex, smoking history, extrapulmonary tumor history, diameter, upper lobe, clear border and spicule sign in the malignant group were significantly different from those in the benign group (all P<0.05). The combined model demonstrated superior predictive performance, clinical utility, and calibration compared to the best-performing individual Brock model [Area under the curve (AUC): 0.88 (95% CI: 0.84-0.92) vs 0.86 (95% CI: 0.82-0.91)]. Besides, multi-grade risk stratification enabled by the combined model was better than binary classification, with the malignant rate of the four risk levels were 0.60%, 4.62%, 14.58% and 56.07%, respectively. Conclusion:The combined model addresses the limitations of individual models in SPNs risk stratification for health examination populations, improving predictive performance, clinical utility, and calibration, while proposing a superior multi-grade risk stratification system.

Strengthening the cultivation of innovation ability is the new requirement put forward by the state for higher educa-tion.High-quality curriculum design is the primary means of achieving high-quality talent cultivation.By constructing the"disease case library"and"problem graph"of immune system and related diseases,and adopting the teaching method of"combining large and small cases and integrating online and offline",this study not only consolidates students'basic knowledge,but also builds a bridge for students from theory to practice,from knowledge accumulation to creation and application.It further exercises students'ability to dis-cover,analyze and solve problems,and enhances students'innovation awareness and ability.

Strengthening the cultivation of innovation ability is the new requirement put forward by the state for higher educa-tion.High-quality curriculum design is the primary means of achieving high-quality talent cultivation.By constructing the"disease case library"and"problem graph"of immune system and related diseases,and adopting the teaching method of"combining large and small cases and integrating online and offline",this study not only consolidates students'basic knowledge,but also builds a bridge for students from theory to practice,from knowledge accumulation to creation and application.It further exercises students'ability to dis-cover,analyze and solve problems,and enhances students'innovation awareness and ability.

Objective:To analyze the epidemiological characteristics of severe fever with thrombocytopenia syndrome (SFTS) in Beijing in 2024, to investigate the infection status of reservoir hosts, vector organisms, and baseline human populations, and provide a scientific basis for formulating prevention and control strategies.Methods:Epidemiological surveys were conducted on all confirmed cases. Serum samples from healthy populations and reservoir hosts were collected for SFTSV antibody detection. Questing ticks were monitored using the flagging method. Real-time fluorescent quantitative PCR was employed to detect SFTSV in cases, reservoir hosts, and ticks. Positive samples underwent whole-genome sequencing and genetic evolution analysis.Results:In 2024, Beijing reported 15 locally infected cases with 4 deaths. The age of onset ranged from 50 to 80 years (median: 66 years). Cases showed a certain degree of geographical clustering, with June being the peak month of onset. The affected population was predominantly farmers, with a male-to-female ratio of 3∶2. Animal contact history emerged as a significant risk factor alongside tick bites. Parthenogenetic tick populations were identified in Pinggu district, while SFTSV-carrying ticks were detected in endemic areas (Mentougou, Shijingshan, and Fengtai Districts). Viral presence was also confirmed in ticks or dogs from non-endemic areas. Sequencing and phylogenetic analysis revealed stable clustering of strains into two distinct genotypes (A and B). Antibody-positive individuals were identified in healthy populations from non-endemic areas.Conclusions:The incidence of SFTS in Beijing is increasing, with natural viral circulation already established in non-endemic regions. Enhanced surveillance and adjusted prevention strategies are urgently needed.

With rapid development of information technology,blended teaching model has gradually become mainstream teaching model in most colleges and universities.How to evaluate students'learning effect and analyze factors that affect students'per-formance is a key research direction of this teaching model.Taking blended teaching of Medical Immunology course in Xinxiang Medical University as an example,this paper introduces implementation process and evaluation system of blended teaching,analyzes main factors affecting students'performance and learning effect,and focuses on discussing influence of formative evaluation and"flipped classroom"on students'academic performance.

Objective:To evaluate the effectiveness of the artificial intelligence-assisted diagnosis and treatment system in distinguishing benign and malignant lung nodules.Methods:Clinical data of 1 650 patients with pulmonary nodules admitted to the Tianjin Chest Hospital Affiliated to Tianjin University from January 2015 to October 2022 were retrospectively analyzed, which included a total of 1 650 nodules. The malignant probability was compared to and analyzedpredict the benign and malignant nodules.Results:A total of 1 650 nodules were included, which were divided into a malignant tumor group(n=1 391, 84.3%) and a benign lesion group(n=259, 15.7%). Between the malignant tumor group and the benign lesion group, the malignant probability was significantly different, and the malignant probability could better distinguish malignant nodules and benign lesions[(78.85±22.91)% vs.(54.91±28.68)%, P<0.001]. The area under the curve( AUC) was 0.768. The critical value of malignant probability for diagnosis of lung cancer was 81.3% with a sensitivity of 0.620 and specificity of 0.815. Stratified analysis results showed that the accuracy of the AI intelligent system for diagnosing pulmonary nodules with the sizes of 0-1 cm, 1-2 cm and 2-3 cm was also increased, and the areas under ROC curve were 0.717, 0.769 and 0.804, respectively. Conclusion:The malignant probability of lung nodules calculated by the artificial intelligence-assisted diagnosis and treatment system can be used in the assessment of the preoperative benign and malignant lung nodules.

Objective:To analyze the epidemiological characteristics and transmission chain of an epidemic of COVID-19 in Haidian district, Beijing.Methods:Descriptive epidemiological method was used to analyze the epidemiological characteristics of the epidemic, and field investigation and big data technology were used to analyze the transmission chain of the epidemic.Results:From April 27 to May 13, 2022, an epidemic of COVID-19 occurred in Haidian district. The strains isolated from the cases were identified by whole genome sequencing as Omicron variant (BA.2.2 evolutionary branch). A total of 38 infection cases were detected, including 34 confirmed cases and 4 asymptomatic cases. Most cases were mild ones (88.2%), no severe, critical or death cases occurred. The early clinical symptoms were mainly sore throat (50.0%) and cough (29.4%). The epidemic lasted for 17 days, resulting in 7 generations of the cases and involving 3 community transmissions, 2 working place transmissions and 8 family transmissions; the main infection routes were co-residence (47.6%) and co-space exposure (31.6%). The intergenerational interval M( Q1, Q3)was 3 (1, 6) days. The overall secondary attack rate was 1.5% (37/2 482), and the family secondary attack rate was 36.7% (18/49). Conclusions:The cases in this COVID-19 epidemic caused by Omicron variant had mild clinical symptoms, but the case clustering in families and communities was obvious, the transmission was rapid, and the risk for co-space exposure was high. It is necessary to use information technology to identify close contacts in the local population for the rapid and effective blocking of the epidemic spread.

Objective    To investigate the clinical value of artificial intelligence (AI)-assisted chest computed tomography (CT) in the diagnosis of peripheral lung shadow. Methods    The CT image data of 810 patients with peripheral pulmonary shadow treated by thoracic surgery in Tianjin Chest Hospital Affiliated to Tianjin University from January 2018 to July 2019 were retrospectively analyzed using AI-assisted chest CT imaging diagnosis system. There were 339 males and 471 females with a median age of 63 years. The malignant probability of preoperative AI-assisted diagnosis of peripheral pulmonary shadow was compared with the results of postoperative pathology. Results    The pathological diagnosis of 810 patients with peripheral pulmonary shadow was lung cancer in 627 (77.4%) patients, precancerous lesion in 30 (3.7%) patients and benign lesion in 153 (18.9%) patients. The median probability of malignant AI diagnosis before operation was 86.0% (lung cancer), 90.0% (precancerous lesion) and 37.0% (benign lesion), respectively. According to the analysis of receiver operating characteristic (ROC) curve of AI malignant probability distribution in this group of patients, the area under the ROC curve was 0.882. The critical value of malignant probability for diagnosis of lung cancer was 75.0%with a sensitivity of 0.856 and specificity of 0.814. A total of 571 patients were diagnosed with AI malignancy probability≥ 75.0%, among whom 537 patients were pathologically diagnosed as lung cancer with a positive predictive value of 94.0%(537/571). Conclusion    The AI-assisted chest CT diagnosis system has a high accuracy in the diagnosis of peripheral lung cancer with malignant probability≥75.0% as the diagnostic threshold.