Objective To introduce an innovative technique, the "balance-shaped sternal elevation device" and its application in the subxiphoid uniportal video-assisted thoracoscopic surgery (VATS) for anterior mediastinal masses resection. Methods Patients who underwent single-port thoracoscopic assisted anterior mediastinal tumor resection through the xiphoid process at the Department of Thoracic Surgery, West China Hospital, Sichuan University from May to June 2024 were included, and their clinical data were analyzed. Results A total of 7 patients were included, with 3 males and 4 females, aged 28-72 years. The diameter of the tumor was 1.9-17.0 cm. The operation time was 62-308 min, intraoperative blood loss was 5-100 mL, postoperative chest drainage tube retention time was 0-9 days, pain score on the 7th day after surgery was 0-2 points, and postoperative hospital stay was 3-12 days. All patients underwent successful and complete resection of the masses and thymus, with favorable postoperative recovery. Conclusion The "balance-shaped sternal elevation device" effectively expands the retrosternal space, providing surgeons with satisfactory surgical views and operating space. This technique significantly enhances the efficacy and safety of minimally invasive surgery for anterior mediastinal masses, reduces trauma and postoperative pain, and accelerates patient recovery, demonstrating important clinical significance and application value.

Objective To analyze the application effects of artificial intelligence (AI) software and Mimics software in preoperative three-dimensional (3D) reconstruction for thoracoscopic anatomical pulmonary segmentectomy. Methods A retrospective analysis was conducted on patients who underwent thoracoscopic pulmonary segmentectomy at the Second People's Hospital of Huai'an from October 2019 to March 2024. Patients who underwent AI 3D reconstruction were included in the AI group, those who underwent Mimics 3D reconstruction were included in the Mimics group, and those who did not undergo 3D reconstruction were included in the control group. Perioperative related indicators of each group were compared. Results A total of 168 patients were included, including 73 males and 95 females, aged 25-81 (61.61±10.55) years. There were 79 patients in the AI group, 53 patients in the Mimics group, and 36 patients in the control group. There were no statistical differences in gender, age, smoking history, nodule size, number of lymph node dissection groups, postoperative pathological results, or postoperative complications among the three groups (P>0.05). There were statistical differences in operation time (P<0.001), extubation time (P<0.001), drainage volume (P<0.001), bleeding volume (P<0.001), and postoperative hospital stay (P=0.001) among the three groups. There were no statistical differences in operation time, extubation time, bleeding volume, or postoperative hospital stay between the AI group and the Mimics group (P>0.05). There was no statistical difference in drainage volume between the AI group and the control group (P=0.494), while there were statistical differences in operation time, drainage tube retention time, bleeding volume, and postoperative hospital stay (P<0.05). Conclusion For patients requiring thoracoscopic anatomical pulmonary segmentectomy, preoperative 3D reconstruction and preoperative planning based on 3D images can shorten the operation time, postoperative extubation time and hospital stay, and reduce intraoperative bleeding and postoperative drainage volume compared with reading CT images only. The use of AI software for 3D reconstruction is not inferior to Mimics manual 3D reconstruction in terms of surgical guidance and postoperative recovery, which can reduce the workload of clinicians and is worth promoting.

Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. Methods Patients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. Results A total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.

Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

Objective To establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. Methods Patients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patients' CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. Results A total of 299 patients were included, with 103 males and 196 females, with a median age of 57.00 (51.00, 67.25) years. There were 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average density [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.982)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.750, and specificity of 0.936. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. Conclusion For patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.

Objective To explore the diagnosis and treatment strategies for elderly patients with ground-glass opacity (GGO). Methods The imaging features and postoperative pathological findings of the elderly patients with pulmonary GGO receiving surgery in our hospital from 2017 to 2019 were retrospectively analyzed. The patients were divided into an elderly patient group and a non-elderly patient group based on their age. Results Finally 575 patients were included in the study. There were 281 elderly patients, including 83 males and 198 females, with an average age of (67.0±5.3) years. There were 294 non-elderly patients, including 88 males and 206 females, with an average age of (49.1±7.3) years. Compared with the non-elderly patients, elderly GGO patients showed the following distinct clinical features: long observation time for lesions (P=0.001), high proportion of rough edges of GGO (P<0.001), significant pleural signs (P＜0.001) and bronchial signs (P＜0.001), and high proportion of type Ⅱ-Ⅳ GGO (P<0.001), lobectomy type (P=0.013), and invasive lesions reported in postoperative pathology (P<0.001). There was no statistical difference in the average hospital stay between the two groups (P=0.106). Multivariate logistic regression analysis showed that GGO diameter and GGO type were the main factors affecting the operation. Observation time, GGO diameter, GGO type and pleural signs were the main influencing factors for postoperative pathological infiltrative lesions. The cut-off value of GGO diameter in predicting infiltrating lesions was 10.5 mm in the elderly patients group. Conclusion The size and type of GGO are important factors in predicting invasive lesions and selecting surgical methods. Elderly patients with radiographic manifestations of type Ⅱ-Ⅳ GGO lesions with a diameter greater than 10.5 mm should be closely followed up.

Objective To investigate the correlation between spread through air space (STAS) of sub-centimeter non-small cell lung cancer and clinical characteristics and radiological features, constructing a nomogram risk prediction model for STAS to provide a reference for the preoperative planning of sub-centimeter non-small cell lung cancer patients. Methods The data of patients with sub-centimeter non-small cell lung cancer who underwent surgical treatment in Nanjing Drum Tower Hospital from January 2022 to October 2023 were retrospectively collected. According to the pathological diagnosis of whether the tumor was accompanied with STAS, they were divided into a STAS positive group and a STAS negative group. The clinical and radiological data of the two groups were collected for univariate logistic regression analysis, and the variables with statistical differences were included in the multivariate analysis. Finally, independent risk factors for STAS were screened out and a nomogram model was constructed. The sensitivity and specificity were calculated based on the Youden index, and area under the curve (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the model. Results A total of 112 patients were collected, which included 17 patients in the STAS positive group, consisting of 11 males and 6 females, with a mean age of (59.0±10.3) years. The STAS negative group included 95 patients, with 30 males and 65 females, and a mean age of (56.8±10.3) years. Univariate logistic regression analysis showed that male, anti-GAGE7 antibody positive, mean CT value and spiculation were associated with the occurrence of STAS (P＜0.05). Multivariate regression analysis showed that associations between STAS and male (OR=5.974, 95%CI 1.495 to 23.872), anti-GAGE7 antibody positive (OR=11.760, 95%CI 1.619 to 85.408) and mean CT value (OR=1.008, 95%CI 1.004 to 1.013) were still significant (P＜0.05), while the association between STAS and spiculation was not significant anymore (P=0.438). Based on the above three independent predictors, a nomogram model of STAS in sub-centimeter non-small cell lung cancer was constructed. The AUC value of the model was 0.890, the sensitivity was 76.5%, and the specificity was 91.6%. The calibration curve was well fitted, suggesting that the model had a good prediction efficiency for STAS. The DCA plot showed that the model had a good clinically utility. Conclusion Male, anti-GAGE7 antibody positive and mean CT value are independent predictors of STAS positivity of sub-centimeter non-small cell lung cancer, and the nomogram model established in this study has a good predictive value and provides reference for preoperative planning of patients.

Objective To investigate the new-onset conduction block after transcatheter aortic valve replacement (TAVR) and summarize the relevant experience. Methods The perioperative data of TAVR patients in the Second Hospital of Hebei Medical University from January 2016 to February 2023 were collected, and the new-onset incidence of conduction block after TAVR was analyzed retrospectively. Results Finally 352 patients were included, including 225 males and 127 females, with an average age of (67.2±5.1) years, among whom 256 patients were treated with Venus-A valves, 69 patients with Vita-Flow valves, and 27 patients with J-Valve valves. There were 38 (10.8%) patients of new-onset postoperative block. There were 6 (1.7%) patients of new-onset postoperative grade Ⅲ atrioventricular block, including 5 (2.0%) patients of Venus-A and 1 (1.4%) patient of Vita-Flow. Conduction function was restored in 2 patients within 14 days after surgery, and failed to be restored in 4 patients, who then received permanent pacemaker implantation in the Department of Cardiology. There were 27 (7.7%) patients of new left bundle branch block after surgery, including 22 (8.6%) patients of Venus-A, 4 (5.8%) patients of Vita-Flow and 1 (3.7%) patient of J-Valve; and conduction function was restored within 7 days after surgery in 23 patients, and 5 (1.4%) patients developed new right bundle branch blocks after surgery including 4 (1.5%) patients of Venus-A and 1 (1.4%) patient of Vita-Flow. Conclusion New-onset conduction block is a common complication after TAVR, and the new-onset rate of left bundle branch block is the highest, followed by the grade Ⅲ atrioventricular block. Mastering reasonable methods and applying appropriate strategies can effectively reduce the new-onset rate of postoperative conduction block and improve the overall success rate of TAVR surgery.

Objective To investigate the mastery of the management knowledge of patients with atrial fibrillation after coronary artery bypass grafting by cardiac surgeons in Beijing tertiary hospitals, and the practice status and obstacles of following the guidelines for postoperative atrial fibrillation. Methods A convenient sampling method was used to select cardiac surgeons from four tertiary hospitals in Beijing, and a self-designed questionnaire on the management of postoperative atrial fibrillation patients was used. Results A total of 227 valid questionnaires were collected. Only 47.9% of doctors and 12.8% of nurses passed in knowledge, and 31.3% of doctors and 28.5% of nurses passed in behavior. Among them, risk factor assessment, preventive medication, stroke and bleeding risk assessment were the weakest. "Lack of departmental requirements" was identified as a common barrier to healthcare workers' adherence to guidelines. Job title and participation in training were common influencing factors that affected the knowledge and behavior of healthcare workers, and knowledge level was an important factor affecting healthcare worker behavior. Conclusion In order to improve the effect of CABG surgery and improve the quality of postoperative patient management, hospitals should further strengthen the knowledge and skills training of medical staff on the management guidelines of postoperative atrial fibrillation with CABG, formulate relevant systems to ensure the clinical implementation of guidelines.

Liver fibrosis is a chronic liver injury resulting from factors like viral hepatitis, autoimmune hepatitis, non-alcoholic steatohepatitis, fatty liver disease, and cholestatic liver disease. Liver transplantation is currently the gold standard for treating severe liver diseases. However, it is limited by a shortage of donor organs and the necessity for lifelong immunosuppressive therapy. Mesenchymal stem cells (MSCs) can differentiate into various liver cells and enhance liver function when transplanted into patients due to their differentiation and proliferation capabilities. Therefore, it can be used as an alternative therapy for treating liver diseases, especially for liver cirrhosis, liver failure, and liver transplant complications. However, due to the potential tumorigenic effects of MSCs, researchers are exploring a new approach to treating liver fibrosis using extracellular vesicles (exosomes) secreted by stem cells. Many studies show that exosomes released by stem cells can promote liver injury repair through various pathways, contributing to the treatment of liver fibrosis. In this review, we focus on the molecular mechanisms by which stem cell exosomes affect liver fibrosis through different pathways and their potential therapeutic targets. Additionally, we discuss the advantages of exosome therapy over stem cell therapy and the possible future directions of exosome research, including the prospects for clinical applications and the challenges to be overcome.