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.

OBJECTIVE To analyze the pathogens of severe pneumonia induced by different underlying diseases in(hospital) and to evaluate the difference of the pathogens to antimicrobial susceptibility test.METHODS The(bacteria) through sputum culture with VITEK-AMS,and the antimicrobial susceptibility against(bacteria) by K-B method were tested in hospital from 2002 to 2005.RESULTS There were 106 patients with 173 strains of isolated(pathogenic) bacteria,including Klebsiella pneumoniae(KPA) 20.9%,and Escherichia coli(ECO)(16.3%) from the aspiration severe pneumonia;Pseudomonas alcaligenes(PAL) 18.9%,P.aeruginosa(PAL)(16.2%) and Staphylococcus aureus(SAU) 16.2% from the obstructive severe pneumonia;KPN 22.6%,SAU(18.9%),and PAE 17.0% from the COPD complicated with severe pneumonia and PAE 25.0% and SAU 20% from the(hospital-)acquired pneumonia(HAP).CONCLUSIONS The constructed ratio of pathogens is different between(severe) pneumonia infected by different underlying diseases and community-acquired pneumonia without any(underlying) disease.The resistant pathogens are increasing significantly in cases with HAP.

The study on the functions of the diaphragm has been carried on for 5 years.It consisted of the studies on the relationship of the tension and endurance of inspiratory muscles with pulmonary functions,the changes of diaphragmatic electromyogram in maximal ventilation volume test in normal subjects and patients with chronic obstructive pulmonary disease(COPD),the functional changes of the diaphragm in dogs with RDS and the therapeutic effects of aminophylline.the changes of diaphragmatic electromyogram in dogs with hypoxemia and hy-percapnia,determination of transdiaphragmatic pressure,the relationship of the stimulation frequency on the phrenic nerve with diaphragmtic fatigue,etc.It was found that there existed di-aphragmtic fatigue in dogs with RDS,in dogs with hypoxemia and hypercapnia,and in patients with COPD,and aminophylline,digoxin,salbutomal ?-receptor excitory agents can minimize or prevent diaphragmatic fatigue.

Objective To detect the expression of inositol 1,4,5-triphosphate receptor (IP 3R) subtypes in normal rat airway smooth muscle cells (ASMCs) and changes during chronic asthma formation. Methods ASMCs were cultured by collagen enzyme digestion method. The expressions of subtypes of IP 3R were detected by RT-PCR and the purified PCR products were linked with pGEM-T vector for DNA sequencing. Chronic asthma model was established with egg albumin. The changes of IP 3Rs were detected by RT-PCR method. Results All subtypes of IP 3R were expressed in airway smooth muscle cells of normal rats. The expression of IP 3R1 in asthma groups increased obviously as compared with that in the control group (P