This paper systematically summarizes the practical experience of the 2025 Dingri earthquake emergency medical rescue in Tibet. It analyzes the requirements for earthquake medical rescue under conditions of high-altitude hypoxia, low temperature, and low air pressure. The paper provides a detailed discussion on the strategic layout of earthquake medical rescue at the national level, local government level, and through social participation. It covers the construction of rescue organizational systems, technical systems, material support systems, and information systems. The importance of building rescue teams is emphasized. In high-altitude and cold conditions, rapid response, scientific decision-making, and multi-party collaboration are identified as key elements to enhance rescue efficiency. By optimizing rescue organizational structures, strengthening the development of new equipment, and promoting telemedicine technologies, the precision and effectiveness of medical rescue can be significantly improved, providing important references for future similar disaster rescues.

According to the "Regulations on clinical application management of medical technologies", physicians intending to carry out restricted technologies must undergo standardized training and pass assessments in accordance with the clinical application management standards for the respective technology. As ventricular assist technology is classified as a nationally restricted technology, standardized training is one of the essential conditions for its application. This paper primarily explores the standardized training for the clinical application of ventricular assist technology in Shanghai, in light of its background, clinical application, and current training status. It proposes the training requirements for ventricular assist technology, animal training assessment standards, and clinical practice assessment standards in Shanghai, aiming to promote the standardized development and high-quality advancement of ventricular assist technology in Shanghai.

Objective To explore the application of artificial intelligence (AI) in the standardized training of thoracic surgery residents, specifically in enhancing clinical skills and anatomical understanding through AI-assisted lung nodule identification and lung segment anatomy teaching. Methods Thoracic surgery residents undergoing standardized training at Peking Union Medical College Hospital from September 2023 to September 2024 were selected. They were randomly assigned to a trial group and a control group using a random number table. The trial group used AI-assisted three-dimensional reconstruction technology for lung nodule identification, while the control group used conventional chest CT images. After basic teaching and self-practice, the ability to identify lung nodules on the same patient CT images was evaluated, and feedback was collected through questionnaires. Results A total of 72 residents participated in the study, including 30 (41.7%) males and 42 (58.3%) females, with an average age of (24.0±3.0) years. The trial group showed significantly better overall diagnostic accuracy for lung nodules (91.9% vs. 73.3%) and lung segment identification (100.0% vs. 83.70%) compared to the control group, and the reading time was significantly shorter [ (118.5±10.5) s vs. (332.1±20.2) s, P<0.01]. Questionnaire results indicated that 94.4% of the residents had a positive attitude toward AI technology, and 91.7% believed that it improved diagnostic accuracy. Conclusion AI-assisted teaching significantly improves thoracic surgery residents’ ability to read images and clinical thinking, providing a new direction for the reform of standardized training.

Objective To investigate the incidence, severity and longitudinal trajectories of symptoms at various time points in the perioperative period of lung cancer patients, and to provide scientific basis for clinical staff to implement predictive nursing and dynamic management of symptom clusters. Methods A prospective longitudinal investigation was conducted. The patients with lung cancer who underwent thoracoscopic lung surgery in four wards of the Department of Thoracic Surgery in our hospital were investigated by face-to-face and telephone follow-up before surgery, 1-2 days after surgery, on the day of discharge and 2 weeks after discharge. The investigation tool was the revised Chinese version of MD Anderson Symptom Inventory lung cancer specific module. Results A total of 192 patients with lung cancer were included in this study, including 59 males and 133 females, with an average age of (55.68±11.01) years. There were two symptom clusters (respiratory-gastrointestinal and emotional/psychological-disturbed sleep symptom clusters) before surgery, three symptom clusters (respiratory, gastrointestinal, and emotional/psychological-disturbed sleep symptom clusters) 1-2 days after surgery, three symptom clusters (pain-fatigue-emotional/psychological, respiratory, and gastrointestinal symptom clusters) on the day of discharge, and two symptom clusters (pain-fatigue-respiratory and respiratory symptom clusters) 2 weeks after discharge. The composition of symptoms was different in each time point during perioperative period. Conclusion There are four symptom clusters in patients with lung cancer during perioperative period, which are pain-fatigue-disturbed sleep symptoms, gastrointestinal symptoms, respiratory symptoms and emotional/psychological symptoms. The symptom clusters of lung cancer patients at different time points are relatively stable, but the symptoms within the symptom clusters show dynamic changes. Medical staff should attach great importance to and continuously monitor the dynamic changes of perioperative symptom groups of lung cancer patients, do relevant education and nursing in advance, and timely adjust the management plan according to the symptom group evaluation results.

Objective To analyze the early prognosis of repairing adult aortic insufficiency with the Florida sleeve procedure. Methods The patients with aortic insufficiency who underwent Florida sleeve repair in the Nanjing First Hospital, Nanjing Medical University between August 2020 and May 2024 were selected. Their general data, perioperative conditions, and echocardiographic data before, during, and after the procedure and at follow-up were analyzed. Results Fifteen patients were included, consisting of 12 males and 3 females, aged 33-71 (53.5±12.4) years. Preoperative echocardiography indicated that there was 1 patient of rheumatic disease, 7 patients of degenerative disease, 4 patients secondary to aortic aneurysm, and 3 patients of bicuspid aortic valve. The severity distribution included 2 patients of severe insufficiency, 4 patients of moderate-to-severe insufficiency, 5 patients of moderate insufficiency, and 4 patients of mild-to-moderate insufficiency. The mean cardiopulmonary bypass time was (135.0±40.0) minutes, the aortic cross-clamp time was (109.9±38.6) minutes, and the median ICU stay was 1.0 day. No mortality was recorded within 30 days postoperatively. Follow-up echocardiography showed that the valve regurgitation, left ventricular end-diastolic diameter, left ventricular end-systolic diameter, and sinus diameter all achieved the desired outcomes. Conclusion Florida sleeve repair for aortic valve in patients with a sinus diameter less than 50 mm not only effectively improves hemodynamics in adults with aortic insufficiency, but also has the advantages of low surgical risk and rapid postoperative recovery, making it a promising procedure for clinical application.

The widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth, such as baseline morphological parameters, dynamic indicators, and clinical characteristics, traditional prediction models (exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

Objective To compare the long-term survival of elderly patients with esophageal squamous cell carcinoma (ESCC) treated with surgical versus non-surgical treatment. Methods A retrospective analysis was conducted on the clinical data of elderly patients aged ≥70 years with ESCC who underwent esophagectomy or radiotherapy/chemotherapy at Sichuan Cancer Hospital from January 2009 to September 2017. Patients were divided into a surgical group (S group) and a non-surgical group (NS group) according to the treatment method. The propensity score matching method was used to match the two groups of patients at a ratio of 1∶1, and the survival of the two groups before and after matching was analyzed. Results A total of 726 elderly patients with ESCC were included, including 552 males and 174 females, with 651 patients aged ≥70-80 years and 75 patients aged ≥80-90 years. There were 515 patients in the S group and 211 patients in the NS group. The median follow-up time was 60.8 months, and the median overall survival of the S group was 41.9 months [95%CI (35.2, 48.5)], while that of the NS group was only 24.0 months [95%CI (19.8, 28.3)]. The 1-, 3-, and 5-year overall survival rates of the S group were 84%, 54%, and 40%, respectively, while those of the NS group were 72%, 40%, and 30%, respectively [HR=0.689, 95%CI (0.559, 0.849), P<0.001]. After matching, 138 patients were included in each group, and there was no statistical difference in the overall survival between the two groups [HR=0.871, 95%CI (0.649, 1.167), P=0.352]. Conclusion Compared with conservative treatment, there is no significant difference in the long-term survival of elderly patients aged ≥70 years who undergo esophagectomy for ESCC. Neoadjuvant therapy combined with surgery is still an important choice to potentially improve the survival of elderly patients with ESCC.

Objective To investigate the impact of COVID-19 infection on the early clinical outcomes of patients undergoing valve replacement. Methods Perioperative data of patients who underwent single valve replacement at the Second Affiliated Hospital of Chinese People's Liberation Army Medical University from January to February 2023 were consecutively collected. Based on COVID-19 infection status, patients were divided into a COVID-19 group and a non-COVID-19 group. The perioperative data were compared between the two groups. Results A total of 136 patients were included, comprising 53 males and 83 females, with a mean age of (53.4±10.2) years. There were 32 patients receiving aortic valve replacements, 102 mitral valve replacements, and 2 tricuspid valve replacements. The COVID-19 group comprised 70 patients, and the non-COVID-19 group included 66 patients. No statistical difference was observed in the incidence of postoperative complications between the two groups [9.09% (6/66) vs. 11.43% (8/70), P=0.654]. However, the COVID-19 group had longer postoperative mechanical ventilation duration [1 201.00 (1 003.75, 1 347.75) min vs. 913.50 (465.50, 1 251.00) min, P=0.001] and ICU stay [3 (2, 3) days vs. 2 (2, 3) days, P<0.001] compared to the non-COVID-19 group. Additionally, troponin I [4.76 (2.55, 7.93) ng/mL vs. 2.66 (1.19, 5.65) ng/mL, P=0.001] and brain natriuretic peptide [608.50 (249.75, 1 150.00) pg/mL vs. 192.00 (100.93, 314.75) pg/mL, P<0.001] levels were significantly higher in the COVID-19 group. Conclusion For patients with single valve disease undergoing elective surgery, short-term outcomes after recovery from COVID-19 infection are favorable, with no significant increase in in-hospital mortality or postoperative complication rates.