Objective To analyze the influencing factors of chronic obstructive pulmonary disease(COPD)complicating pulmonary interstitial fibrosis,and to establish a nomogram prediction model.Methods The clinical data of 195 patients with COPD admitted and treated in this hospital from January 2019 to Feb-ruary 2021 were retrospectively analyzed.The factors possibly affecting the patients with COPD complicating pulmonary interstitial fibrosis were collected,and the patients were divided into 2 groups according to whether having pulmonary interstitial fibrosis.The independent risk factors were analyzed and screened by the multi-variate logistic regression.Then the nomogram prediction model was constructed.Results Among the includ-ed 195 cases of COPD in this study,there were 50 cases(25.64％)of complicating pulmonary interstitial fi-brosis.The univariate and multivariate logistic regression analysis results showed that the smoking history,duration of COPD,frequency of acute exacerbations onset,serum transforming growth factor β1(TGF-β1),basic fibroblast growth factor(bFGF)and angiotensin Ⅱ(AngⅡ)were the independent influencing factors of COPD complicating pulmonary interstitial fibrosis(P＜0.05).The nomogram model was constructed accord-ing to the results of multivariate analysis results,and the area under the receiver operating characteristic(ROC)curve was 0.956(95％CI:0.930-0.983),the average absolute error of internal verification by the Bootstrap method was 0.025,and the prediction model performance basically fitted the ideal model.Conclusion The nomogram model constructed by this study for predicting the pulmonary interstitial fibrosis in COPD patients has high accuracy and distinction degree.

BACKGROUND: The incidence and mortality of lung cancer have always been at the forefront of malignant tumors. With the development of lung cancer detection techniques, more peripheral pulmonary lesions (PPLs) have been detected. The diagnostic accuracy of procedures for PPLs keeps controversial. This study aims to systematically evaluate the diagnostic value and the safety of electromagnetic navigation bronchoscopy (ENB) in the diagnosis of PPLs. METHODS: The relevant literatures in the diagnostic yield of PPLs by ENB were systematically retrieved from Wanfang Data Knowledge Service Platform, China National Knowledge Infrastructure, Embase, PubMed, Cochrane Library and Web of Science. The software of Stata 16.0, RevMan 5.4 and Meta-disc 1.4 were used to conduct the meta-analysis. RESULTS: A total of 54 literatures with 55 studies were included in our meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio of ENB in the diagnosis of PPLs were 0.77 (95%CI: 0.73-0.81), 0.97 (95%CI: 0.93-0.99), 24.27 (95%CI: 10.21-57.67), 0.23 (95%CI: 0.19-0.28) and 104.19 (95%CI: 41.85-259.37), respectively. The area under curve (AUC) was 0.90 (95%CI: 0.87-0.92). Meta-regression and subgroup analyses indicated that the potential heterogeneity resulted from study type, additional localization techniques, sample size, lesion size and type of sedation. The use of additional localization techniques and general anesthesia have improved the diagnostic efficiency of ENB in PPLs. The incidence of adverse reactions and complications associated with ENB was very low. CONCLUSIONS ENB provides well diagnostic accuracy and safety.
Humans ; Bronchoscopy ; Lung Neoplasms ; Anesthesia ; China ; Electromagnetic Phenomena

This study established a method for rapid quantification of terpene lactone, bilobalide, ginkgolide C, ginkgolide A and ginkgolide B in the chromatographic process of Ginkgo Folium based on near infrared spectroscopy(NIRS). The effects of competitive adaptive reweighting sampling(CARS), random frog(RF), and synergy interval partial least squares(siPLS) on the performance of partial least squares regression(PLSR) model were compared to the reference values measured by HPLC. Among them, the correlation coefficients of prediction(Rp) of validation sets of terpene lactone, bilobalide, and ginkgolide C were all higher than 0.98, and the relative standard errors of prediction(RSEPs) were 5.87%, 6.90% and 6.63%, respectively. Aiming at ginkgolide A and ginkgolide B with relatively low content, the genetic algorithm joint extreme learning machine(GA-ELM) was used to establish the optimized quantitative analysis model. Compared with CARS-PLSR model, the CARS-GA-ELM models of ginkgolide A and ginkgolide B exhibited a reduction in RSEP from 15.65% to 8.52% and from 21.28% to 10.84%, respectively, which met the needs of quantitative ana-lysis. It has been proved that NIRS can be used for the rapid detection of various lactone components in the chromatographic process of Ginkgo Folium.
Chromatography, High Pressure Liquid ; Ginkgo biloba ; Lactones/analysis* ; Least-Squares Analysis ; Spectroscopy, Near-Infrared/methods*

An analytical method was developed for determination of ginkgolic acids in Yinxing Tongzhi Dropping Pills by ultra high performance liquid chromatography-triple quadrupole mass spectrometry. The samples were purified by mix-mode anion exchange and reversed-phase SPE. A chromatographic column, Waters Cortecs T3 (50 mm×2.1 mm, 2.7 μm), was used with acetonitrile-methanol-1% acetic acid (44:44:12) as the mobile phase. The ginkgolic acids were detected by electrospray ionization mass spectrometry in negative mode with multiple reaction monitoring (MRM) mode. Ginkgolic acid C13:0, C15:1 and C17:1 possessed good linear correlation in the mass concentration range from 0.2 to 200 μg·L^-1, 2 to 200 μg·L^-1, 4 to 200 μg·L^-1, respectively, with the correlation coefficients more than 0.999. The mean recoveries at spiked levels of 50, 250 and 600 μg·kg^-1 were in the range of 70.8%-95.1%, and the RSDs were 0.7%-8.6%. The limits of quantification were 1, 10, 20 μg·kg^-1, respectively. The method could be applied to the analysis of ginkgolic acids in complex matrix samples.