Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

Robot-assisted surgery with its advantages such as three-dimensional high-definition vision, dexterous robotic arms, and tremor filtration, is increasingly being applied to complex radical gastrectomy. However, the role of the surgical assistant remains crucial during the procedure. The assistant is responsible for tasks outside the console, including adjusting robotic arms, changing instruments, exposing the surgical field, and addressing unexpected situations. The technical proficiency of the assistant and their collaboration efficiency with the primary surgeon directly impact the smoothness of surgery and patients' outcomes. With the expansion of robot-assisted surgical indications, the establishment of a standardized training system and the optimization of team collaboration models have become urgent challenges to address. This article draws on the author's practical experience as an assistant in robot-assisted gastric cancer surgeries, conducting an in-depth analysis of the responsibilities and operational skills of surgical assistants in robot-assisted procedures. The aim is to develop a relatively comprehensive set of operational guidelines for surgical assistants in robot-assisted radical gastrectomy, providing valuable references for enhancing the overall efficiency of surgical teams and improving surgical outcomes.

Acute lung injury （ALI） is one of the most common and critical diseases in clinical practice， with extremely high morbidity and mortality， seriously threatening human life and health. The pathogenesis of ALI is complex， in which the inflammatory response is a key factor. Studies have shown that NOD-like receptor protein 3 （NLRP3） inflammasomes are involved in ALI through mechanisms such as inflammation induction， increased microvascular permeability， recruitment of neutrophils， oxidative stress， and pyroptosis， playing a key role in the occurrence and progression of ALI. Therefore， regulating NLRP3 inflammasomes and inhibiting the release of inflammatory factors can alleviate the damage in ALI. At present， ALI is mainly treated by mechanical ventilation and oxygen therapy， which have problems such as high costs and poor prognosis. In recent years， studies have shown that traditional Chinese medicine （TCM） can reduce the inflammatory response and the occurrence of oxidative stress and pyroptosis by regulating the NLRP3 inflammasome， thus alleviating the damage and decreasing the mortality of ALI. Based on the relevant literature in recent years， this article reviews the research progress in TCM treatment of ALI by regulating NLRP3 inflammasomes， discusses how NLRP3 inflammasomes participate in ALI， and summarizes the active ingredients， extracts， and compound prescriptions of TCM that regulate NLRP3 inflammasomes， aiming to provide new ideas for the clinical treatment of ALI and the development of relevant drugs.

Multimorbidity has emerged as a critical global public health challenge, necessitating effective management strategies. In Heilongjiang Province, a cold north region in China with a high prevalence of multimorbidity, healthcare disparities, uneven resource distribution, and inadequate chronic disease control remain pressing issues. Driven by national policies on family physician contracting and regional demands for chronic disease management, this study established a "hospital-community general practitioner co-management" model utilizing a mobile-based digital platform. This model integrates hospital-based general practitioners into primary care teams to optimize resource allocation and enhance multimorbidity management. The initiative aims to explore pathways for improving healthcare system reform and strengthening the chronic disease prevention framework in the region.

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

Objective:To compare the effects of robotic and laparoscopic radical gastrectomy on short-term clinical outcomes and long-term prognosis in obese patients with gastric cancer.Methods:Clinical data from 231 obese gastric cancer patients undergoing laparoscopic or robotic radical gastrectomy at the Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University between Jan 2015 and Dec 2022 was analyzed. After propensity score matching, the patients were divided into robotic surgery group ( n=79) and laparoscopic surgery group ( n=79). The short-term clinical outcomes and long-term prognosis were compared. Results:Compared with the laparoscopic group, the robotic group had a significantly greater number of dissected lymph nodes [(32±13) vs. (26±11), t=2.797, P=0.006], shorter operation time [(245±65) min vs. (272±62) min, t=-2.669, P=0.008], less intraoperative blood loss[(84±69) vs. (119±56) ml, t=-3.502, P=0.001], shorter postoperative hospital stay [(8.2±3.5) vs. (9.6±4.2) d, t=-2.363, P=0.019], and higher hospitalization cost [(102,139±18,303) vs. (77,857±18,325) yuan, t=8.333, P<0.001]. The 5-year overall survival and disease-free survival rates were comparable between the robotic and laparoscopic groups (77.2% vs. 74.7%, P=0.684; and 73.4% vs. 68.4%, P=0.491, respectively). Conclusions:Robotic radical gastrectomy is a safe and feasible alternative for obese gastric cancer patients in experienced hands. It offers advantages in short-term clinical outcomes, however, it fails to provide a significant long-term survival benefit.

Objective:To compare the short- and long-term outcomes of robotic versus laparoscopic gastrectomy in patients with locally advanced gastric cancer after neoadjuvant chemotherapy.Methods:Data from 321 patients with locally advanced gastric cancer undergoing neoadjuvant chemotherapy followed by robotic ( n=109) and laparoscopic ( n=212) radical gastrectomy at our center between May 2017 and Sep 2022 was collected. After 1∶1 propensity score matching, 106 patients from each group were included in the final analysis to compare short-term clinical outcomes and long-term prognostic indicators. Results:The robotic group had a significantly lower overall complication rate (13.2% vs. 28.3%, χ2=6.453, P=0.007) and surgery-related complication rate (8.5% vs. 17.9%, χ2=3.333, P=0.043) than the laparoscopic group. The robotic group also retrieved more total lymph nodes (35.3±4.9 vs. 31.4±6.3, t=4.863, P<0.001) and supra-pancreatic lymph nodes (13.1±3.4 vs. 10.1±2.1, t=5.258, P<0.001). Additionally, the robotic group had a shorter operative time [(218±47) min vs. (267±71) min, t=-6.001, P<0.001], less intraoperative blood loss [(47±12) ml vs. (71±17) ml, t=-5.424, P<0.001], and faster postoperative recovery. The 3-year recurrence-free survival rate was significantly higher in the robotic group compared to the laparoscopic group (75.5% vs. 62.3%, P=0.017). Conclusion:Compared with laparoscopic gastrectomy, robotic gastrectomy allows for a more lymph nodes harvest, significantly reduces intraoperative blood loss and complication rates and significantly improves recurrence-free survival.

Robot-assisted surgery with its advantages such as three-dimensional high-definition vision, dexterous robotic arms, and tremor filtration, is increasingly being applied to complex radical gastrectomy. However, the role of the surgical assistant remains crucial during the procedure. The assistant is responsible for tasks outside the console, including adjusting robotic arms, changing instruments, exposing the surgical field, and addressing unexpected situations. The technical proficiency of the assistant and their collaboration efficiency with the primary surgeon directly impact the smoothness of surgery and patients' outcomes. With the expansion of robot-assisted surgical indications, the establishment of a standardized training system and the optimization of team collaboration models have become urgent challenges to address. This article draws on the author's practical experience as an assistant in robot-assisted gastric cancer surgeries, conducting an in-depth analysis of the responsibilities and operational skills of surgical assistants in robot-assisted procedures. The aim is to develop a relatively comprehensive set of operational guidelines for surgical assistants in robot-assisted radical gastrectomy, providing valuable references for enhancing the overall efficiency of surgical teams and improving surgical outcomes.

Multimorbidity has emerged as a critical global public health challenge, necessitating effective management strategies. In Heilongjiang Province, a cold north region in China with a high prevalence of multimorbidity, healthcare disparities, uneven resource distribution, and inadequate chronic disease control remain pressing issues. Driven by national policies on family physician contracting and regional demands for chronic disease management, this study established a "hospital-community general practitioner co-management" model utilizing a mobile-based digital platform. This model integrates hospital-based general practitioners into primary care teams to optimize resource allocation and enhance multimorbidity management. The initiative aims to explore pathways for improving healthcare system reform and strengthening the chronic disease prevention framework in the region.

Objective:To compare the effects of robotic and laparoscopic radical gastrectomy on short-term clinical outcomes and long-term prognosis in obese patients with gastric cancer.Methods:Clinical data from 231 obese gastric cancer patients undergoing laparoscopic or robotic radical gastrectomy at the Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University between Jan 2015 and Dec 2022 was analyzed. After propensity score matching, the patients were divided into robotic surgery group ( n=79) and laparoscopic surgery group ( n=79). The short-term clinical outcomes and long-term prognosis were compared. Results:Compared with the laparoscopic group, the robotic group had a significantly greater number of dissected lymph nodes [(32±13) vs. (26±11), t=2.797, P=0.006], shorter operation time [(245±65) min vs. (272±62) min, t=-2.669, P=0.008], less intraoperative blood loss[(84±69) vs. (119±56) ml, t=-3.502, P=0.001], shorter postoperative hospital stay [(8.2±3.5) vs. (9.6±4.2) d, t=-2.363, P=0.019], and higher hospitalization cost [(102,139±18,303) vs. (77,857±18,325) yuan, t=8.333, P<0.001]. The 5-year overall survival and disease-free survival rates were comparable between the robotic and laparoscopic groups (77.2% vs. 74.7%, P=0.684; and 73.4% vs. 68.4%, P=0.491, respectively). Conclusions:Robotic radical gastrectomy is a safe and feasible alternative for obese gastric cancer patients in experienced hands. It offers advantages in short-term clinical outcomes, however, it fails to provide a significant long-term survival benefit.