With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

Albendazole-glucan particles(ABZ-GPS)and abendazole(ABZ)were used to treat secondary alveolar echinococ-cosis in mice.The therapeutic effects of ABZ-GPS on alveolar echinococcosis in vivo were evaluated,and the feasibility of using glucan particles as anti-hydatid drug carriers was further verified.Mice with echinococcosis were randomly divided into an ABZ group,glucan nanoparticle(GP)group,ABZ-GPS group,and control group.After four courses of administration with a final concentration of 50 mg/mL,the therapeutic effects of ABZ-GPS were evaluated on the basis of imaging,histopathological changes,ultrastructure,and immunology.After ABZ-GPS and ABZ administration,clear liver lesion tissue necrosis and large numbers of infiltrating lymphocytes were observed.Significant differences in the average cyst wet weight(t=7.83,P＜0.05),were observed between the ABZ-GPS group and ABZ group.Imaging revealed that ABZ-GPs were targeted to liver tissue.Pa-thology and ultrastructure analyses demonstrated that the alveolar hydatid cells in the liver in the control group and GP group grew well,and the vesicles were large,filled with cystic fluid,and translucent or transparent;the cyst wall tension was high;no calcification was observed;the stratum corneum and germinal layer were clear;and more fertile capsules and different num-bers of protocephalospora were present in the liver.In the ABZ group,the capsular cavity collapsed,and showed partial necro-sis and lymphocyte infiltration.In the ABZ-GP group,the corneum and germinal layer of echinococcus vesicles were difficult to identify,and we observed bulbous necrosis,central calcification,fibrous tissue hyperplasia,inflammatory cell infiltration,coarser,shorter or absent microvilli of the germinal layer,nuclear shrinkage,dissolution or disappearance,clear expansion of cytoplasmic microtubules,and myelin-like or vacuole-like changes.Therefore,ABZ-GPs showed good targeting and killing ac-tivity in vivo in mice with secondary alveolar coccosis.

Albendazole-glucan particles(ABZ-GPS)and abendazole(ABZ)were used to treat secondary alveolar echinococ-cosis in mice.The therapeutic effects of ABZ-GPS on alveolar echinococcosis in vivo were evaluated,and the feasibility of using glucan particles as anti-hydatid drug carriers was further verified.Mice with echinococcosis were randomly divided into an ABZ group,glucan nanoparticle(GP)group,ABZ-GPS group,and control group.After four courses of administration with a final concentration of 50 mg/mL,the therapeutic effects of ABZ-GPS were evaluated on the basis of imaging,histopathological changes,ultrastructure,and immunology.After ABZ-GPS and ABZ administration,clear liver lesion tissue necrosis and large numbers of infiltrating lymphocytes were observed.Significant differences in the average cyst wet weight(t=7.83,P＜0.05),were observed between the ABZ-GPS group and ABZ group.Imaging revealed that ABZ-GPs were targeted to liver tissue.Pa-thology and ultrastructure analyses demonstrated that the alveolar hydatid cells in the liver in the control group and GP group grew well,and the vesicles were large,filled with cystic fluid,and translucent or transparent;the cyst wall tension was high;no calcification was observed;the stratum corneum and germinal layer were clear;and more fertile capsules and different num-bers of protocephalospora were present in the liver.In the ABZ group,the capsular cavity collapsed,and showed partial necro-sis and lymphocyte infiltration.In the ABZ-GP group,the corneum and germinal layer of echinococcus vesicles were difficult to identify,and we observed bulbous necrosis,central calcification,fibrous tissue hyperplasia,inflammatory cell infiltration,coarser,shorter or absent microvilli of the germinal layer,nuclear shrinkage,dissolution or disappearance,clear expansion of cytoplasmic microtubules,and myelin-like or vacuole-like changes.Therefore,ABZ-GPs showed good targeting and killing ac-tivity in vivo in mice with secondary alveolar coccosis.

Objective:To explore the application effect of a student-faculty collaborative dynamic question bank based on outcome based education (OBE) in the teaching of maternal and infant nursing.Methods:A total of 204 junior nursing undergraduates (Grade 2021 and Grade 2022) enrolled at Shantou University Medical College between September 2023 and January 2025 were selected by convenience sampling. Ninety-two students from Grade 2021 were assigned to the control group, and 112 students from Grade 2022 were assigned to the intervention group. The control group received the traditional teaching syllabus before the course, used the People's Medical Publishing House question bank for self-testing, and the original static self-constructed exam question bank in the Kaoyi system for assessment. The intervention group practiced and was assessed using a student-faculty collaborative dynamic question bank based on OBE. The intervention lasted for one semester. Course performance and satisfaction were compared between the two groups.Results:The number of questions in the bank increased from 347 to 2 058. After course completion, the intervention group had higher stage test scores, final exam scores, and overall course scores than the control group ( P<0.05). The intervention group also reported higher levels of overall course satisfaction, perceived personal ability improvement, and satisfaction with course content and instructors than the control group ( P<0.05) . Conclusions:The student-faculty collaborative dynamic question bank based on OBE can effectively improve students' academic performance and satisfaction, and is worthy of wider application.

PURPOSE: This study aims to identify the prevalence and risk factors of military training-related abdominal injuries and help plan and conduct training properly. METHODS: This questionnaire survey study was conducted from October 2021 to May 2022 among military personnel from 6 military units and 8 military medical centers and participants' medical records were consulted to identify the training-related abdominal injuries. All the military personnel who ever participated in military training were included. Those who refused to participate in this study or provided an incomplete questionnaire were excluded. The questionnaire collected demographic information, type of abdominal injury, frequency, training subjects, triggers, treatment, and training disturbance. Chi-square test and t-test were used to compare baseline information. Univariate and multivariate regression analyses were used to explore the risk factors associated with military training-related abdominal injuries. RESULTS: A total of 3058 participants were involved in this study, among which 1797 (58.8%) had suffered training-related abdominal injuries (the mean age was 24.3 years and the service time was 5.6 years), while 1261 (41.2%) had no training-related abdominal injuries (the mean age was 23.1 years and the service time was 4.3 years). There were 546 injured patients (30.4%) suspended the training and 84 (4.6%) needed to be referred to higher-level hospitals. The most common triggers included inadequate warm-up, fatigue, and intense training. The training subjects with the most abdominal injuries were long-distance running (589, 32.8%). Civil servants had the highest rate of abdominal trauma (17.1%). Age ≥ 25 years, military service ≥ 3 years, poor sleep status, and previous abdominal history were independent risk factors for training-related abdominal injury. CONCLUSION More than half of the military personnel have suffered military training-related abdominal injuries. Inadequate warm-up, fatigue, and high training intensity are the most common inducing factors. Scientific and proper training should be conducted according to the factors causing abdominal injuries.
Humans ; Military Personnel ; Risk Factors ; Prevalence ; Male ; Abdominal Injuries/etiology* ; Female ; Adult ; Surveys and Questionnaires ; Young Adult

Objective:To explore the application effect of a student-faculty collaborative dynamic question bank based on outcome based education (OBE) in the teaching of maternal and infant nursing.Methods:A total of 204 junior nursing undergraduates (Grade 2021 and Grade 2022) enrolled at Shantou University Medical College between September 2023 and January 2025 were selected by convenience sampling. Ninety-two students from Grade 2021 were assigned to the control group, and 112 students from Grade 2022 were assigned to the intervention group. The control group received the traditional teaching syllabus before the course, used the People's Medical Publishing House question bank for self-testing, and the original static self-constructed exam question bank in the Kaoyi system for assessment. The intervention group practiced and was assessed using a student-faculty collaborative dynamic question bank based on OBE. The intervention lasted for one semester. Course performance and satisfaction were compared between the two groups.Results:The number of questions in the bank increased from 347 to 2 058. After course completion, the intervention group had higher stage test scores, final exam scores, and overall course scores than the control group ( P<0.05). The intervention group also reported higher levels of overall course satisfaction, perceived personal ability improvement, and satisfaction with course content and instructors than the control group ( P<0.05) . Conclusions:The student-faculty collaborative dynamic question bank based on OBE can effectively improve students' academic performance and satisfaction, and is worthy of wider application.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.