背景:研究表明骨髓间充质干细胞源外泌体在多种呼吸系统炎症及疾病损伤模型中表现出强大的修复和再生能力,但在慢性阻塞性肺疾病中的研究较少,且尚未有研究将外泌体雾化吸入应用于慢性阻塞性肺疾病的模型实验中.目的:探讨大鼠骨髓间充质干细胞源外泌体通过雾化吸入途径对慢性阻塞性肺疾病大鼠炎症和肺部损伤的治疗作用,并且明确最适治疗剂量.方法:体外分离培养大鼠骨髓间充质干细胞,并提取鉴定其外泌体.脂多糖联合烟熏28 d建立慢性阻塞性肺疾病大鼠模型,然后给予低剂量(0.5×108 particles/kg)、中剂量(1.0×108 particles/kg)、高剂量(1.5×108 particles/kg)外泌体雾化吸入治疗以及外泌体(1.5×108 particles/kg)尾静脉注射治疗,模型组雾化1 mL PBS,对照组不造模、雾化1 mL PBS.连续雾化或注射治疗5 d,最后一次雾化或注射治疗后的第2天开始检测,使用小动物肺功能仪测试各组肺功能指标,ELISA检测支气管肺泡灌洗液及血清中的白细胞介素1β和肿瘤坏死因子α水平,苏木精-伊红染色和Masson染色从组织学评估肺组织改变.结果 与结论:①骨髓间充质干细胞来源外泌体在透射电镜下显示为椭圆形的双层膜囊泡结构,呈典型的杯口状,粒径分析提示外泌体的峰直径为91.7 nm,占比为97.3％,颗粒浓度为3.3×109 L-1,并且外泌体表面蛋白CD9和CD63高表达;②与尾静脉注射外泌体相比,雾化吸入外泌体显著改善了慢性阻塞性肺疾病大鼠的肺功能、肺组织切片胶原沉积和肺组织病理变化,并且明显降低了支气管肺泡灌洗液和血清中白细胞介素1β和肿瘤坏死因子α水平,且低剂量外泌体治疗效果最为显著;③以上结果表明,雾化吸入骨髓间充质干细胞来源外泌体可以减轻慢性阻塞性肺疾病的炎性损伤,并且最适剂量可能为0.5×108 particles/kg.

Chronic obstructive pulmonary disease (COPD) has a high morbidity and mortality worldwide and is characterized by chronic inflammation and progressive airflow obstruction. At present, no drug treatment has been proved to improve the survival rate of COPD patients. In recent years, exosomes have become a research hotspot due to their involvement in inflammatory response, immune regulation and injury repair. At the same time, studies have found that exosomes are closely related to the occurrence and development of COPD. This review first introduced the biological characteristics components and functions of exosomes, and then lists the relevant studies on exosomes in COPD biomarkers and pathogenesis, and finally clarifies the latest progress of exosomes in clinical diagnosis and treatment of respiratory diseases.

Purpose: Although cigarette smoke exposure is the major risk factor for chronic obstructive pulmonary disease (COPD), the mechanism is not completely understood.The aim of the present study was to investigate whether ACSL4-mediated ferroptosis in lung epithelial cells plays a part in the COPD development process and its association.Patients and Methods: In this study, animal and cell models of COPD were modelled using cigarette smoke extracts (CSEs), and cell viability, lipid ROS, iron ion deposition, and ferroptosis-related markers were measured in lung tissue and lung epithelial cells following CSE exposure.Morphological changes in mitochondria were observed in lung tissue and epithelial cells of the lung by transmission electron microscope.The expression levels of ACSL4 mRNA and protein in lung tissue and epithelial cells were measured by real-time PCR and Western blotting.In addition, animal-interfering lentivirus and cell-interfering RNA against ACSL4 were constructed in this study, ferroptosis in lung tissue and lung epithelial cells after ACSL4 interference was detected, and ACSL4 mRNA and protein expression levels were detected.Results: CSE induced ferroptosis in lung tissues and lung epithelial cells, and the expression levels of ACSL4 were elevated in CSEtreated lung tissues and lung epithelial cells.After ACSL4 interference, the expression of ACSL4 decreased, mitochondrial morphology was restored, and ferroptosis in lung tissues and lung epithelial cells was alleviated.Both respiratory frequency and enhanced pause of COPD mice models decreased after ACSL4 interference.Conclusion: ACSL4-mediated ferroptosis in lung epithelial cells is associated with COPD and positively correlated with ferroptosis in epithelial cells.

Objective:To propose a model using the maximum intensity projection (MIP) of lung field computed tomography (CT) images and deep convolution neural network (CNN) and explore its value in identifying chronic obstructive pulmonary disease (COPD).Methods:A total of 201 subjects were selected from the Second Hospital of Dalian Medical University from January 2010 to May 2021. All subjects were included according to the inclusion criteria and were divided into COPD group (101 cases) and healthy controls group (100 cases). Each patient underwent a high-resolution CT scan of the chest and pulmonary function test. First, the lung field was extracted from CT images and the intrapulmonary MIP images were acquired. Second, with these MIP images as input, the model for identifying COPD was constructed based on a modified residual network (ResNet). Finally, the influence of the number of residual blocks on the performance of the models was investigated. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the identification efficiency.Results:The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of ResNet26 was 76.1%, 76.2%, 76.0%, 76.2%, and 76.0%, respectively; and the AUC of the test was 0.855 (95% CI: 0.799-0.901). The accuracy, sensitivity, specificity, PPV, NPV of ResNet50 was 77.6%, 76.2%, 79.0%, 78.6%, and 76.7%, respectively; and the AUC of the test was 0.854 (95% CI: 0.797-0.900). The accuracy, sensitivity, specificity, PPV, NPV of ResNet26d was 82.1%, 83.2%, 81.0%, 81.6%, and 82.7%, respectively; and the AUC of the test was 0.885 (95% CI: 0.830-0.926). Conclusions:The COPD identification model via MIP images from CT images within the lung and deep CNN is successfully constructed and achieves accurate COPD identification. And it can provide an effective tool for COPD screening.