Objective To explore an artificial intelligence (AI)-based method for automated hand hygiene monitoring and to compare the effectiveness of three algorithms (UniFormerV2, TDN, C3D) in recognizing hand hygiene steps in surgical settings, thereby aiding hospital infection control. Methods From April to October 2024, we non-invasively collected 641 video recordings of healthcare staff performing hand hygiene at four-bay scrub sinks in two tertiary hospitals using overhead HD cameras. The dataset was annotated by five trained experts for model training and validation. Results Following training on 385 samples, internal validation (n=119) showed the C3D model achieved 81% accuracy, 87% recall, and an 83% F1-score. The TDN model achieved 93%, 91%, and 92% for the same metrics. The UniFormerV2 model outperformed both, with an accuracy, recall, and F1-score of 93%—an improvement of over 10 percentage points compared to traditional CNNs (TDN, C3D). It also achieved an 84% accuracy in external validation, demonstrating strong generalization. Conclusion The UniFormerV2 model is more accurate than CNN-based models for hand hygiene step recognition and shows robust performance in external validation. It presents a viable tool for healthcare facilities to enhance hand hygiene management, ultimately improving medical quality and patient safety.

Objective To evaluate the short-term efficacy of transcatheter aortic valve replacement (TAVR) using Venus A-Plus valve delivery system in patients with severe aortic stenosis. Methods The clinical data of patients undergoing TAVR in our hospital from August 2018 to March 2022 were collected and they were divided into a Venus A-Plus and a Venus A group according to the type of valve delivery system used. The perioperative data of the two groups were compared. Results A total of 121 patients were included, including 70 patients in the Venus A-Plus group [45 males and 25 females with a mean age of (67.81±6.62) years], and 51 patients in the Venus A group [33 males and 18 females with a mean age of (68.25±7.01) years]. All patients underwent TAVR, and the postoperative hemodynamic features (left ventricular ejection fraction, mean cross-valve pressure difference, peak flow rate) were significantly improved (P<0.05). There was no statistical difference in surgical success rate, all-cause mortality, conversion to thorax opening, valve-in-valve placement, moderate or above perivalvular regurgitation, new left bundle branch block or new right bundle branch block between the two groups (P>0.05). Conclusion TAVR with Venus A-Plus valve delivery system in patients with severe aortic stenosis shows comparable efficacy to the first-generation Venus A system and is satisfactory, safe and reliable.

Objective To explore the effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods Patients scheduled to undergo on-pump cardiac valve surgery in the Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, between January and July 2022 were initially enrolled. Eligible patients were randomly assigned at a 1 : 1 ratio to either the RIPC group or the control group. Relevant indicators of heart rate variability [standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency (LF) component, high frequency (HF) component and LF/HF] at 8 hours in the morning on the surgical day between two groups were compared. Results A total of 118 patients were initially assessed. After screening, 58 patients were excluded, and 60 patients provided written informed consent and were enrolled in the trial, with 30 allocated to the RIPC group and 30 to the control group. Seven patients in the control group and 5 patients in the RIPC group were subsequently excluded due to missing heart rate variability data resulting from cancelled operations. Finally, 23 patients in the control group and 25 patients in the RIPC group were included in the analysis. There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.

Objective To investigate the post-discharge exercise behavior and factors influencing moderate to vigorous intensity physical activity (MVPA) in patients undergoing lung surgery. Methods A total of 2874 patients from the large prospective, observational perioperative lung symptom study cohort (CN-PRO-Lung 3) in the Department of Thoracic Surgery at Sichuan Cancer Hospital between April 7, 2021, and January 31, 2024, were selected as the survey subjects. A survey was conducted using the Investigation of Exercise Behavior after Lung Surgery questionnaire and the International Physical Activity Questionnaire-Short Form (IPAQ-SF) among patients who underwent lung surgery. Binary logistic regression was used to analyze the factors influencing patients’ engagement in MVPA. Results A total of 702 patients were surveyed, including 252 males and 450 females, with an average age of (52.4±10.2) years. Patients with lung cancer accounted for 85.9%. Only 36.0% of the patients had regular exercise habits, while 42.3% did not engage in any physical activity. The three main barriers for postoperative exercise were physical discomfort (pain, coughing, shortness of breath, etc, 54.7%), lack of professional guidance (41.7%), and concerns about the surgical wound (28.9%). The proportions of patients engaging in vigorous, moderate, and low-intensity physical activity were 5.7%, 28.2%, and 66.1%, respectively. Multivariate analysis showed that patients with a personal annual income ≥50000 yuan (OR=1.52, 95%CI 1.01-2.29, P=0.044), high school education or above (OR=1.92, 95%CI 1.33-2.76, P<0.001), and lobectomy (OR=1.44, 95%CI 1.02-2.03, P=0.037) engaged in more MVPA. Conclusion Patients undergoing lung surgery have inadequate physical activity after discharge, particularly lacking in MVPA. Patients with higher income, higher educational levels, and lobectomy are more frequently engaged in MVPA. Measures such as symptom control, providing exercise guidance, and enhancing education on wound care may potentially improve the inadequate physical activity in lung surgery patients after discharge.

Objective To investigate the clinical efficacy of minimally invasive mitral valvuloplasty (MVP) in the treatment of infective endocarditis (IE) with mitral regurgitation (MR). Methods A retrospective analysis was conducted on the clinical data of patients who underwent MVP for IE with MR at the Department of Cardiovascular Surgery in Zhongshan Hospital, Fudan University from 2016 to 2020. Patients were divided into two groups based on the surgical incision: those with a right mini-thoracotomy were classified as a minimally invasive surgery (MIS) group, and those with a median sternotomy (MS) were classified as an MS group. All patients had isolated mitral valve involvement. Perioperative data were analyzed, and mid- to long-term outcomes were compared between the two groups. Results A total of 86 patients were included, with 40 in the MIS group [22 males and 18 females, with a mean age of (39.78±15.36) years ranging from 14 to 75 years] and 46 in the MS group [27 males and 19 females, with a mean age of (49.94±16.13) years ranging from 14 to 71 years]. The patients in the MIS group were relatively younger (P=0.004) with better preoperative cardiac function (P=0.004). There was no statistical difference in preoperative fever, gender, or comorbidities between the two groups (P>0.05). The MIS group had shorter postoperative ventilation times, less postoperative 24-hour drainage, less blood transfusion, and shorter total hospital stays compared to the MS group (P＜0.05). There was no statistical difference in cardiopulmonary bypass times or ICU stays between the two groups (P>0.05). The perioperative complication rates and mortality rates were not significantly different between the two groups (P>0.05). Follow-up was conducted for 11-92 months, with a mean duration of (49±19) months and an overall follow-up rate of 91.9%. During the follow-up, 3 patients in each group required reoperation for mitral valve issues, with no statistical difference in incidence (7.5% vs. 6.5%, P=0.691). There were no warfarin-related complications, recurrences, or deaths in either group during follow-up. Multivariate regression analysis identified age, preoperative cardiac function, and surgeon experience as influencing factors for the choice of surgical approach. Conclusion Minimally invasive MVP for IE with MR is relatively safe in the perioperative period and shows significant efficacy, with clear mid- to long-term outcomes. It is recommended for younger patients with better preoperative cardiac function and when performed by surgeons with extensive experience in mitral valvuloplasty.

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.

Objective To fabricate a large-diameter vascular graft with a pore size gradient structure mimicking that of natural blood vessels, using poly-L-lactic acid (PLLA) and polycaprolactone (PCL) as base materials through electrospinning and ultrasonic pore-forming techniques, and to evaluate its application potential. Methods A three-layered tubular graft was fabricated from a PCL/PLLA blend (mass ratio 6 : 4) via electrospinning, followed by an ultrasonic pore-forming process to create a gradient porosity. The resulting graft (diameter: 2 cm, length: 4 cm) was implanted into the descending thoracic aorta of an experimental pig using an end-to-end anastomosis. Graft patency and anastomotic sites were monitored by computed tomography angiography (CTA) at 1 and 6 weeks post-surgery. After 2 months, the graft was explanted for systematic evaluation of vascular regeneration and repair through gross examination, histopathology (H&E and elastic fiber staining), immunohistochemistry [for ETS-related gene (ERG), Actin, and Vimentin], and scanning electron microscopy (SEM). Results Postoperative CTA confirmed excellent graft patency at both 1 and 6 weeks, with no evidence of thrombosis or anastomotic stenosis. Gross examination of the 2-month explant revealed a smooth luminal surface covered by neotissue. Histopathological analysis demonstrated that the graft successfully induced the formation of a three-layered structure resembling a native vessel wall, comprising endothelial cells, smooth muscle cells, and fibroblasts. Immunohistochemistry further verified coverage of the luminal surface by endothelial cells (ERG-positive), along with the presence of neosmooth muscle (Actin-positive) and fibroblasts (Vimentin-positive). Endothelial cells were observed adhering to the inner surface of the artificial vessel under SEM. Conclusion The biomimetic, three-layered PLLA/PCL large-diameter vascular graft, constructed via electrospinning and ultrasonic pore-forming, exhibits excellent short-term patency and biocompatibility in a large animal model. More importantly, it demonstrates a significant potential to promote host cell infiltration and achieve in situ regeneration of a three-layered vascular wall structure, providing a promising experimental basis for the development of next-generation functional vascular substitutes.

Objective To explore the accuracy of machine learning algorithms based on SHOX2 and RASSF1A methylation levels in predicting early-stage lung adenocarcinoma pathological types. Methods A retrospective analysis was conducted on formalin-fixed paraffin-embedded (FFPE) specimens from patients who underwent lung tumor resection surgery at Affiliated Hospital of Nantong University from January 2021 to January 2023. Based on the pathological classification of the tumors, patients were divided into three groups: a benign tumor/adenocarcinoma in situ (BT/AIS) group, a minimally invasive adenocarcinoma (MIA) group, and an invasive adenocarcinoma (IA) group. The methylation levels of SHOX2 and RASSF1A in FFPE specimens were measured using the LungMe kit through methylation-specific PCR (MS-PCR). Using the methylation levels of SHOX2 and RASSF1A as predictive variables, various machine learning algorithms (including logistic regression, XGBoost, random forest, and naive Bayes) were employed to predict different lung adenocarcinoma pathological types. Results A total of 272 patients were included. The average ages of patients in the BT/AIS, MIA, and IA groups were 57.97, 61.31, and 63.84 years, respectively. The proportions of female patients were 55.38%, 61.11%, and 61.36%, respectively. In the early-stage lung adenocarcinoma prediction model established based on SHOX2 and RASSF1A methylation levels, the random forest and XGBoost models performed well in predicting each pathological type. The C-statistics of the random forest model for the BT/AIS, MIA, and IA groups were 0.71, 0.72, and 0.78, respectively. The C-statistics of the XGBoost model for the BT/AIS, MIA, and IA groups were 0.70, 0.75, and 0.77, respectively. The naive Bayes model only showed robust performance in the IA group, with a C-statistic of 0.73, indicating some predictive ability. The logistic regression model performed the worst among all groups, showing no predictive ability for any group. Through decision curve analysis, the random forest model demonstrated higher net benefit in predicting BT/AIS and MIA pathological types, indicating its potential value in clinical application. Conclusion Machine learning algorithms based on SHOX2 and RASSF1A methylation levels have high accuracy in predicting early-stage lung adenocarcinoma pathological types.

Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

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.