Objective To describe and analyze the epidemiological characteristics of scrub typhus in Huai'an, Jiangsu Province from 2006 to 2024 and explore the long-term incidence trend and distribution of high-risk areas, and to formulate targeted prevention and control strategies. Methods The scrub typhus case report data of Huai'an from 2006 to 2024 in the Chinese Disease Prevention and Control Information System were extracted for descriptive analysis. Results A total of 898 cases of scrub typhus were reported in Huai'an, with an average annual incidence rate of 0.96 per 100 000 from 2006 to 2024. There was a turning point in the incidence trend of scrub typhus in 2011. From 2006 to 2011, the annual percentage change (APC) was 47.09% (95% CI: 7.53 - 859.39), and the upward trend was statistically significant (P < 0.05). From 2012 to 2024, the APC was -2.12% (95% CI: -29.09 - 3.75), and the downward trend was not statistically significant. October and November were the high-incidence months, and the total concentration from 2006 to 2024 was 0.93, indicating that scrub typhus had strict seasonality. The circular distribution method estimated that the peak period of the epidemic was from October 11th to November 25th, and the peak day of incidence was November 3rd. Jinhu County was a high-incidence area. The ratio of male to female cases was 1.03. The age group with the highest reported incidence was 40 to < 80 years old. The occupation with the highest proportion was farmers, accounting for 78.03%. Conclusion From 2006 to 2024, scrub typhus in Huai'an shows a peak every 3 - 4 years. Middle-aged and elderly farmers are the key population at risk, and Jinhu County is a key area. In the future, targeted health education should be carried out to effectively control the prevalence of scrub typhus.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Objective:Analyze the risk factors for acute lung injury of postoperative acute lung injury(ALI)in patients with Stanford type A aortic dissection(STAAD),and construct a nomogram predictive model.Methods:A retrospective cohort study design was adopted.A total of 112 patients with STAAD who underwent surgical treatment in our hopital from January 2021 to August 2024 were included.They were divided into two groups according to the occurrence of ALI after the surgical:non-ALI group(73 cases)and ALI group(39 cases).Clinical data were collected from both groups of patients.The influencing factors of postoperative ALI in patients with STAAD were analyzed by multivariate logistic regression.Established nomogram prediction model based on influencing factors and validated.Results:Among 112 patients with STAAD who underwent surgical treatment,39 case postoperative ALI occurred,with an incidence rate of 34.82％.Age,preoperative creatinine,body mass index(BMI),preoperative white blood cell count,preoperative lactate and other aspects compared,The difference were statistically significant(P＜0.05).The length of stay in the intensive care unit(ICU)of the ALI group was longer than that of the non ALI group(P＜0.05).The intraoperative red blood cell transfusion volume and extracorporeal circulation time in the ALI group were higher than those in the non ALI group(P＜0.05).Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count were risk factors for postoperative ALI(P＜0.05).The receiver operating characteristic(ROC)curve analysis results show that,the Area under the curve(AUC)of the nomogram prediction model was 0.871.When the optimal critical value was 0.472,its sensitivity and specificity wew 0.887 and 0.776,respectively.The internal validation results of Bootstrap show that,the C-index of the column chart prediction model was 0.862,with an absolute error of 0.032.The calibration curve is close to the ideal curve and the original curve,with a slope close to 1.Conclusions:Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count are independent risk factors for postoperative ALI in patients with STAAD.The nomogram model constructed based on the above risk factors can effectively evaluate the risk of postoperative ALI in patients with STAAD.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To investigate effect of comprehensive management intervention based on WeChat multimedia classroom on cardiac function and prognosis in elderly patients with acute myocardial infarction(AMI).Methods:Clinical data of 118 elderly patients diagnosed with AMI in the Second Affiliated Hospital of Chinese PLA Air Force Military Medical University between August 2021 and December 2022 were retrospectively collected.According to nursing way after operation,they were divided into control group(n=60,comprehensive management interven-tion)and intervention group(n=58,comprehensive management intervention based on WeChat multimedia class-room),both groups were intervened for 5 months.Cardiac function,exercise tolerance,compliance to rehabilita-tion management,self-efficacy and healthy behavior were compared between two groups.Kaplan-Meier survival curve was employed to compare incidence of adverse cardiovascular events during follow-up between two groups.Results:After 5-month intervention,compared with patients in control group,those in intervention group had sig-nificant higher left ventricular ejection fraction(LVEF)[(58.14±1.88)％vs.(54.48±1.34)％],6min walking distance(6MWD)[(490.41±59.59)m vs.(394.97±28.20)m],scores of compliance to rehabilitation manage-ment[(6.97±2.03)points vs.(5.03±1.40)points],General Self-Efficacy Scale(GSES)[(30.34±4.67)points vs.(23.55±4.86)points]and Health Promoting Lifestyle Profile-Ⅱ[(137.62±30.17)points vs.(115.95±22.66)points],and significant lower left ventricular end-diastolic volume(LVEDV)[(117.90±4.22)ml vs.(131.28±3.61)ml],left ventriadar end-systolic volume(LVESV)[(54.46±2.10)ml vs.(63.15±2.06)ml],serum brain natriuretic peptide(BNP)[(362.32±25.36)pg/ml vs.(567.58±21.90)pg/ml]and incidence of ad-verse cardiovascular events(6.90％vs.21.67％)(P＜0.05 or＜0.01).Conclusion:Comprehensive management intervention based on the WeChat multimedia classroom could significantly improve cardiac function,exercise toler-ance,compliance to rehabilitation,self-efficacy and healthy behavior,and reduce incidence of adverse cardiovas-cular events after operation in elderly AMI patients.

Objective:Analyze the risk factors for acute lung injury of postoperative acute lung injury(ALI)in patients with Stanford type A aortic dissection(STAAD),and construct a nomogram predictive model.Methods:A retrospective cohort study design was adopted.A total of 112 patients with STAAD who underwent surgical treatment in our hopital from January 2021 to August 2024 were included.They were divided into two groups according to the occurrence of ALI after the surgical:non-ALI group(73 cases)and ALI group(39 cases).Clinical data were collected from both groups of patients.The influencing factors of postoperative ALI in patients with STAAD were analyzed by multivariate logistic regression.Established nomogram prediction model based on influencing factors and validated.Results:Among 112 patients with STAAD who underwent surgical treatment,39 case postoperative ALI occurred,with an incidence rate of 34.82％.Age,preoperative creatinine,body mass index(BMI),preoperative white blood cell count,preoperative lactate and other aspects compared,The difference were statistically significant(P＜0.05).The length of stay in the intensive care unit(ICU)of the ALI group was longer than that of the non ALI group(P＜0.05).The intraoperative red blood cell transfusion volume and extracorporeal circulation time in the ALI group were higher than those in the non ALI group(P＜0.05).Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count were risk factors for postoperative ALI(P＜0.05).The receiver operating characteristic(ROC)curve analysis results show that,the Area under the curve(AUC)of the nomogram prediction model was 0.871.When the optimal critical value was 0.472,its sensitivity and specificity wew 0.887 and 0.776,respectively.The internal validation results of Bootstrap show that,the C-index of the column chart prediction model was 0.862,with an absolute error of 0.032.The calibration curve is close to the ideal curve and the original curve,with a slope close to 1.Conclusions:Long intraoperative cardiopulmonary bypass time,high BMI,high intraoperative red blood cell transfusion volume and high preoperative white blood cell count are independent risk factors for postoperative ALI in patients with STAAD.The nomogram model constructed based on the above risk factors can effectively evaluate the risk of postoperative ALI in patients with STAAD.

Surgical treatment is one of the key approaches for non-small cell lung cancer (NSCLC). Regular postoperative follow-up is crucial for early detection and timely management of tumor recurrence, metastasis, or second primary tumors. A scientifically sound and reasonable follow-up strategy not only extends patient survival but also significantly improves quality of life, thereby enhancing overall prognosis. This consensus aims to build upon the previous version by incorporating the latest clinical research advancements and refining postoperative follow-up protocols for early-stage NSCLC patients based on different treatment modalities. It provides a scientific and practical reference for clinicians involved in the postoperative follow-up management of NSCLC. By optimizing follow-up strategies, this consensus seeks to promote the standardization and normalization of lung cancer diagnosis and treatment in China, helping more patients receive high-quality care and long-term management. Additionally, the release of this consensus is expected to provide insights for related research and clinical practice both domestically and internationally, driving continuous development and innovation in the field of postoperative management for NSCLC.

Tracheal intubation in emergency and complex scenarios is difficult for critical care medicine residents.Virtual reality(VR)technology has not been used in the training of tracheal intubation in critical care scenarios in China.This project team has developed an emergency tracheal intubation training system for the intensive care unit(ICU)based on virtual simulation technology,and has obtained the computer software copyright registration certificate from the National Copyright Administration(registration number:2024SR1139484).This system uses a case script of acute respiratory distress syndrome(ARDS)secondary to severe acute pancreatitis,sets the roles of patients,family members,nurses and residents,collects digital resources of ICU rescue scenes,computer-aided design(CAD)drawings,instrument models and equipment photos,models instruments and equipment,and uses PICO 4 Pro VR helmets to display the ICU environment in the virtual scene.The key points of skill assessment include tracheal intubation operation,the ability to interpret laboratory results,the ability to judge diseases and the ability to work in teams.The user center contains 3 submodules,namely,the score center,skill analysis and user management.There are 3 user roles in the system,namely,residents,teachers and administrators.The system can track and record the entire operation process,including video recording and playback,and score and comprehensively evaluate each step,thereby realizing an objective and quantitative training and assessment system.By simulating the three-dimensional clinical operation environment of the ICU,the entire process of real tracheal intubation is fully reproduced.Resident doctors are placed in the ICU rescue scene,focusing on training tracheal intubation skills,the ability of doctor-patient communication,on-the-spot response,and teamwork,which is expected to become an important type of standardized teaching in critical care medicine.

Objective To explore the effectiveness of a tracheal intubation training system based on virtual reality(VR)technology in cultivating the clinical practice abilities of resident physicians.Methods Twenty-five first-year resident physicians who were enrolled in the residency programme at Peking University Third Hospital from August 2024 to February 2025 were recruited for this study.All participants completed a questionnaire after receiving VR-based intensive care unit(ICU)emergency tracheal intubation training,to share their experiences with the VR technology-based ICU emergency tracheal intubation training and the shortcomings encountered during the process.Results ① General information:a total of 25 resident physician questionnaires were distributed in this study and received 25 valid responses(response rate 100%).Among the 25 residents,there were 15 males(60%)and 10 females(40%),with an average age of(25.3±0.8)years.Clinical experience was categorised as≤1 year 4 residents(16%),>1-3 years 8 residents(32%),>3-<5 years 5 residents(20%)and≥5 years 8 residents(32%).Among them,3 residents(12%)had no prior tracheal intubation experience,while 11(44%)had performed>10 intubations.Prior to this training,3 residents(12%)had received other forms of virtual tracheal intubation training,whereas 22(88%)had not.During traditional tracheal intubation training,18 residents(72%)reported that they monitored heart rate and blood pressure,whereas 7(28%)did not.In real-world emergency tracheal intubation scenarios,21 residents(84%)experienced role confusion.Additionally,23 residents(92%)believed that opportunities for tracheal intubation practice were too limited,17(68%)thought traditional training provided more guidance from instructors,and 15(60%)valued practical operation opportunities more in tracheal intubation training.②VR-based ICU emergency tracheal intubation training experience:23 residents(92%)considered the VR-based ICU emergency tracheal intubation training effective,with 13(52%)believing it to be more effective than traditional training.Furthermore,23 residents(92%)felt that the VR-based training created a more relaxed learning atmosphere,heightened their interest in learning tracheal intubation,and had better future prospects;22 residents(88%)believed that VR technology facilitated a better understanding of the laryngeal structure;24 residents(96%)thought that VR-based training reduced practical operation risks and better simulated real-world conditions;16 residents(64%)were highly satisfied with the VR-based ICU emergency tracheal intubation operating system;24 residents(96%)considered the case scenarios in the VR-based training reasonable.17 residents(68%)believed that VR-based training offered more learning opportunities,and 19 residents(76%)thought it reduced anxiety during the intubation process.③ Disadvantages of VR-based ICU emergency tracheal intubation:9 residents(36%)tended to overlook obtaining family consent before emergency tracheal intubation prior to the training.Regarding interactivity,5 residents(20%)rated it as excellent,9(36%)as average,and 11 residents(44%)believe that the interactivity was poor;22 residents(88%)felt a lack of tactile feedback during practical operations;20 residents(80%)recommended adding more simulated scenarios;11 residents(44%)believed that,compared to traditional training,VR training lacked practical operation opportunities.Additionally,17 residents(68%)experienced discomfort such as dizziness during the operation.Conclusion VR-based intubation training effectively enhances technical proficiency and psychological preparedness in ICU clinicians,particularly in anatomical visualization and risk-controlled rehearsal.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.