[Effect and mechanism of microRNA-155 in chronic obstructive pulmonary disease by targeting PIK3R1].

This study aimed to investigate the effect of microRNA-155 (miR-155) in chronic obstructive pulmonary disease (COPD) and cigarette smoke extract (CSE)-treated airway smooth muscle cells (ASMCs) by targeting phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1) to regulate the PTEN/PI3K/Akt signaling pathway. The COPD mouse model was induced by lipopolysaccharide (LPS) combined with passive smoking. After modeling, miR-155 mimics and miR-155 inhibitor were used for intervention treatment. The pulmonary function of each group was detected by an EMKA detector. Hematoxylin-eosin (HE) staining was used to observe the pathological changes and scores of lung tissues. The expression of TNF-α, IL-6, and IL-1ß in bronchial alveolar lavage fluid (BALF) was detected by ELISA. Primary ASMCs were isolated and cultured in adherent tissue culture. The proliferation activity was detected by CCK-8 and EdU assays. Transwell and wound healing assays were used to measure the migration of ASMCs. The targeting relationship between miR-155 and PIK3R1 was validated by a double luciferase reporter gene assay. The expression levels of miR-155 and PIK3R1 mRNA in lung tissues of mice in each group were detected by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Western blot was used to detect the expression levels of Ki67, PNCA, PTEN, p-PI3K, PI3K, p85α, p-Akt, and Akt in lung tissues and ASMCs. The results showed that lung function was significantly reduced in the miR-155 mimic group, and the levels of PIK3R1 were significantly increased; while lung function in the miR-155 inhibitor group was significantly improved. The results of HE staining showed that there was obvious inflammatory cell infiltration in the miR-155 mimics group compared to that of the model group. Lung histopathological injury was significantly reduced in the miR-155 inhibitor group, accompanied by decreased expression of Ki67, PNCA, PI3K, p-Akt, increased PTEN and p85α protein levels, and reduced levels of TNF-α, IL-6, and IL-1ß in BALF. The results of the double luciferase reporter gene assay showed that miRNA-155 could target bind to PIK3R1. Compared with those in the CSE+miR-155 NC group, the proliferation and migration of ASMCs were significantly increased in the CSE+miR-155 group. The proliferation and migration of ASMCs were significantly attenuated in the CSE+miR-155+pcDNA PIK3R1 group compared with those in the CSE+miR-155 group, accompanied by decreased expression of Ki67, PNCA, p-Akt and increased PTEN and p85α protein levels. These results suggest that miR-155 activates the PTEN/PI3K/Akt signaling pathway by targeting PIK3R1 to promote the occurrence and development of COPD, which provides new evidence for the use of miR-155 in the treatment of COPD.

Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images.

Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.

Detecting the Media-adventitia Border in Intravascular Ultrasound Images through a Classification-based Approach.

The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images. First, a novel morphologic feature describing the relative position of each structure relative to the MA border, called RPES for short, is proposed. Then, the RPES feature and other features are employed in a multiclass extreme learning machine (ELM) to classify IVUS images into nine classes including the MA border and other structures. At last, a modified snake model is employed to effectively detect the MA border in the rectangular domain, in which a modified external force field is constructed on the basis of local border appearances and classification results. The proposed method is evaluated on a public dataset with 77 IVUS images by three indicators in eight situations, such as calcification and a guide wire artifact. With the proposed RPES feature, detection performances are improved by more than 39 percent, which shows an apparent advantage in comparative experiments. Furthermore, compared with two other existing methods used on the same dataset, the proposed method achieves 18 of the best indicators among 24, demonstrating its higher capability in detecting the MA border.

A Novel Feature Selection Method Based on Extreme Learning Machine and Fractional-Order Darwinian PSO.

The paper presents a novel approach for feature selection based on extreme learning machine (ELM) and Fractional-order Darwinian particle swarm optimization (FODPSO) for regression problems. The proposed method constructs a fitness function by calculating mean square error (MSE) acquired from ELM. And the optimal solution of the fitness function is searched by an improved particle swarm optimization, FODPSO. In order to evaluate the performance of the proposed method, comparative experiments with other relative methods are conducted in seven public datasets. The proposed method obtains six lowest MSE values among all the comparative methods. Experimental results demonstrate that the proposed method has the superiority of getting lower MSE with the same scale of feature subset or requiring smaller scale of feature subset for similar MSE.