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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-1β 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-1β 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.

Biomedical image fusion is the process of combining the information from different imaging modalities to get a synthetic image. Fusion of phase contrast and green fluorescent protein (GFP) images is significant to predict the role of unknown proteins, analyze the function of proteins, locate the subcellular structure, and so forth. Generally, the fusion performance largely depends on the registration of GFP and phase contrast images. However, accurate registration of multi-modal images is a very challenging task. Hence, we propose a novel fusion method based on convolutional sparse representation (CSR) to fuse the mis-registered GFP and phase contrast images. At first, the GFP and phase contrast images are decomposed by CSR to get the coefficients of base layers and detail layers. Secondly, the coefficients of detail layers are fused by the sum modified Laplacian (SML) rule while the coefficients of base layers are fused by the proposed adaptive region energy (ARE) rule. ARE rule is calculated by discussion mechanism based brain storm optimization (DMBSO) algorithm. Finally, the fused image is achieved by carrying out the inverse CSR. The proposed fusion method is tested on 100 pairs of mis-registered GFP and phase contrast images. The experimental results reveal that our proposed fusion method exhibits better fusion results and superior robustness than several existing fusion methods.

In this paper, the cold launch test platform is taken as the research object, theoptimization model of this test platform is established by using topology optimization method. First,the topology optimization goal of the structure is determined, and then the constraints are carriedout. Finally, optimized and non-optimized areas are identified, and the result is processed. Afteroptimization, the quality of the test platform was reduced by 2.9 tons under the conditions of use.

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.

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.

Image fusion techniques can integrate the information from different imaging modalities to get a composite image which is more suitable for human visual perception and further image processing tasks. Fusing green fluorescent protein (GFP) and phase contrast images is very important for subcellular localization, functional analysis of protein and genome expression. The fusion method of GFP and phase contrast images based on complex shearlet transform (CST) is proposed in this paper. Firstly the GFP image is converted to IHS model and its intensity component is obtained. Secondly the CST is performed on the intensity component and the phase contrast image to acquire the low-frequency subbands and the high-frequency subbands. Then the high-frequency subbands are merged by the absolute-maximum rule while the low-frequency subbands are merged by the proposed Haar wavelet-based energy (HWE) rule. Finally the fused image is obtained by performing the inverse CST on the merged subbands and conducting IHS-to-RGB conversion. The proposed fusion method is tested on a number of GFP and phase contrast images and compared with several popular image fusion methods. The experimental results demonstrate that the proposed fusion method can provide better fusion results in terms of subjective quality and objective evaluation.

Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors. Many people may be unaware of their risk for MetS. A cross-sectional, descriptive study was conducted among hospitalized patients with at least one cardiometabolic risk factor in Mainland China. This study assessed the MetS knowledgelevel(through MetS Knowledge Scale, MSKS) and examined the potential predictors by regression analysis. A total of 204 patients aged 58.5 &plusmn, 10.1 years (55% males) participated in this study. The majority of participants had no history of hypertension (54%), dyslipidemia (79%), or diabetes (85%). However, 56% of these participants had at least three cardiometabolic risk factors, indicating the presence of MetS. The average MSKS was very low (mean = 36.7&plusmn, 18.8, possible range = 0&ndash, 100), indicating the urgent needs of MetS education in current practice. Predictors of better MetS knowledge included higher educational level, history of dyslipidemia, and normal high-density lipoprotein cholesterol (F (8, 195) = 9.39, adjusted R2 = 0.192, p&lt, 0.001). In conclusion, adults with cardiometabolic risk factors are at risk of developing MetS, but with a low level of knowledge. Specific health education on MetS should be provided, particularly for those with limited formal education or inadequate lipid management.

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.