Effect of rock salt aerosol therapy on quality of life of patients with pneumoconiosis: A multicenter, randomized, double-blind clinical trial.

This study aimed to explore the impact of rock salt aerosol therapy on the quality of life in pneumoconiosis patients. It may provide new treatment method for the comprehensive control of pneumoconiosis. A total of 452 subjects from 6 hospitals were divided based on the multi-level hierarchical random design. The patients in the treatment group received conventional comprehensive treatment + rock salt aerosol therapy. The baseline data were collected, including gender, age, age of dust exposure, stage and COPD combination. Cough, expectoration and dyspnea levels were valuated. Both of the two methods exhibited good curative effect following time extension. Rock salt aerosol therapy showed more significant effect compared with routine method. The clinical symptom tends to be stable after two weeks treatment of rock salt aerosol therapy. The curative effect increases with the extension of treatment time. 2-4 weeks for one course of treatment can improve the curative effect. Rock salt aerosol therapy can effectively improve the quality of life of pneumoconiosis patients. It is a good treatment and rehabilitation method for the prevention and treatment of pneumoconiosis, thus is worthy of clinical application.

lncRNAPCAT29 inhibits pulmonary fibrosis via the TGF­ß1­regulated RASAL1/ERK1/2 signal pathway.

Pulmonary fibrosis is a severe respiratory disease characterized by the aggregation of extracellular matrix components and inflammation­associated injury. Studies have suggested that long non­coding RNAs (lncRNA) may serve a role in the pathophysiological processes of pulmonary fibrosis. However, the potential molecular mechanisms involving the lncRNA, prostate cancer­associated transcript 29 (lncRNAPCAT29) in the progression of pulmonary fibrosis are yet to be determined. In the present study, the role of lncRNAPCAT29 and the potential signaling mechanism in pulmonary fibrosis progression was investigated. Reverse transcription­quantitative polymerase chain reaction and immunohistochemistry revealed that the expression levels of lncRNAPCAT29 were downregulated within interstitial lung cells from mice with silica­induced pulmonary fibrosis. Transfection with lncRNAPCAT29 was associated with upregulated expression of microRNA (miRNA)­221 and downregulated expression of transforming growth factor­ß1 (TGF­ß1); reduced inflammation and fibrotic progression was also associated with lncRNAPCAT29 transfection. TGF­ß1 expression levels were inhibited within pulmonary fibroblasts due to lncRNAPCAT29 expression; NEDD4 binding protein 2 and Plexin­A4 expression levels were also suppressed. Analysis of the potential mechanism underlying silica­induced pulmonary fibrosis revealed that the expression levels of RAS protein activator like 1 (RASAL1) and extracellular signal­regulated kinases 1/2 (ERK1/2) were suppressed due to lncRNAPCAT29 expression. The results of the present study demonstrated that lncRNAPCAT29 induced miRNA­221 upregulation and TGF­ß1 downregulation. These observations were associated with reduced inflammation and progression of silica­induced pulmonary fibrosis via the TGF­ß1­regulated RASAL1/ERK1/2 signaling pathway, which may serve as a potential target for the treatment of pulmonary fibrosis.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.