Randomized controlled trial to treat migraine with acupuncture: design and protocol

<p>Abstract</p> <p>Background and motivation</p> <p>The effectiveness of using acupuncture to treat migraine is rarely and even suspectedly reported in the literature. In this article, we report the design and the protocol of a randomized controlled large-scale trial to treat migraine using acupuncture, aiming at testifying it is effective to use acupuncture to treat migraine. We demonstrate also that the effectiveness of the treatment may vary due to using acupoints of different meridians or different acupoints of one meridian.</p> <p>Methods and design</p> <p>A multi-center randomized controlled trial is currently undergoing, with three acupoints treatment groups and one non-acupoints control group. The acupuncture treatment consists of 20 sessions per patient with a observation period of 20 weeks. In total, 480 patients with Migraine are registered in this study within 8 hospitals in China from March 2008 to June 2009. These patients are randomly assigned to receive one of the following four acupoints treatment groups, i.e. 1) specific acupoints of Shaoyang meridians (120 patients), 2) non-specific acupoints of Shaoyang meridians (120 patients), 3) acupoints of other meridians (120 patients); or 4) non-acupoints control group (120 patients). The main outcome measurement in this trial is the effect comparison achieved among these four groups in terms of number of days with migraine and intensity of migraine during and after the baseline phase, i.e. the first 4 weeks before randomization and 4, 8 and 16 weeks after randomization. The intensity of headache including Headache intensity grade (0–3) and visual analogue scale (VAS) score will also be used in this study. In addition, the differences of Migraine-Specific Quality-of-Life Questionnaire (MSQ) and Transcranial Doppler Sonography (TCD) before and after randomization are also used as the secondary outcome measurement.</p> <p>Discussion</p> <p>The result of this trial (which will be available in 2009) will demonstrate the efficacy of using acupuncture to treat migraine, and verify whether the specific effect of acupoints exists and whether this specific effect of acupoints is related to meridian and a collection of meridian Qi.</p> <p>Trials registration</p> <p>Clinical Trials.gov NCT00599586</p

High-Level PM2.5/PM10 Exposure Is Associated With Alterations in the Human Pharyngeal Microbiota Composition

Previous studies showed that high concentration of particulate matter (PM) 2.5 and PM10 carried a large number of bacterial and archaeal species, including pathogens and opportunistic pathogens. In this study, pharyngeal swabs from 83 subjects working in an open air farmer’s market were sampled before and after exposure to smog with PM2.5 and PM10 levels up to 200 and 300 μg/m3, respectively. Their microbiota were investigated using high-throughput sequencing targeting the V3–V4 regions of the 16S rRNA gene. The genus level phylotypes was increased from 649 to 767 in the post-smog pharyngeal microbiota, of which 142 were new and detected only in the post-smog microbiota. The 142 new genera were traced to sources such as soil, marine, feces, sewage sludge, freshwater, hot springs, and saline lakes. The abundance of the genera Streptococcus, Haemophilus, Moraxella, and Staphylococcus increased in the post-smog pharyngeal microbiota. All six alpha diversity indices and principal component analysis showed that the taxonomic composition of the post-smog pharyngeal microbiota was significantly different to that of the pre-smog pharyngeal microbiota. Redundancy analysis showed that the influences of PM2.5/PM10 exposure and smoking on the taxonomic composition of the pharyngeal microbiota were statistically significant (p &lt; 0.001). Two days of exposure to high concentrations of PM2.5/PM10 changed the pharyngeal microbiota profiles, which may lead to an increase in respiratory diseases. Wearing masks could reduce the effect of high-level PM2.5/PM10 exposure on the pharyngeal microbiota

A multiresolution mixture generative adversarial network for video super-resolution.

Generative adversarial networks (GANs) have been used to obtain super-resolution (SR) videos that have improved visual perception quality and more coherent details. However, the latest methods perform poorly in areas with dense textures. To better recover the areas with dense textures in video frames and improve the visual perception quality and coherence in videos, this paper proposes a multiresolution mixture generative adversarial network for video super-resolution (MRMVSR). We propose a multiresolution mixture network (MRMNet) as the generative network that can simultaneously generate multiresolution feature maps. In MRMNet, the high-resolution (HR) feature maps can continuously extract information from low-resolution (LR) feature maps to supplement information. In addition, we propose a residual fluctuation loss function for video super-resolution. The residual fluctuation loss function is used to reduce the overall residual fluctuation on SR and HR video frames to avoid a scenario where local differences are too large. Experimental results on the public benchmark dataset show that our method outperforms the state-of-the-art methods for the majority of the test sets

Spatio-Temporal Saliency Perception via Hypercomplex Frequency Spectral Contrast

Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC). Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value) color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms

Surface Defect Recognition of Solar Panel Based on Percolation-Based Image Processing and Serre Standard Model

During the production process of solar panels, it is inevitable to have some defects, such as cracks on the surface of solar panels due to extrusion or damage due to quality issues. This article improves the Serre standard model, which can simulate the ventral visual pathway with object recognition ability, based on the latest research progress and results of simulating biological visual mechanism models in computer vision, to improve the recognition effect of surface defects on solar panels. At the same time, a pre-processing scheme combining Gaussian Laplace operator operator and adaptive Wiener filter to remove noise spots is studied, and the local Gabor Binary Pattern Histogram Sequence (LGBPHS) features are obtained through pre-processing. The Percolation-Based image processing method for detecting obvious cracks was used to determine the location of the algorithm and the calculation results based on the improved standard model method. It mainly refers to the MAX value output by the C2 layer and the classification and identification results of whether there are cracks, and the crack location function is completed. The experimental results show that the proposed method has an accuracy rate of 98.86&#x0025; in training and 98.64&#x0025; in testing, and both the false detection rate and the missed detection rate do not exceed 1&#x0025;. Therefore, the method proposed in the study has a high accuracy and can effectively identify the surface defects of solar panels

Finger Citron Extract Ameliorates Glycolipid Metabolism and Inflammation by Regulating GLP-1 Secretion via TGR5 Receptors in Obese Rats

Finger citron (FC) is one of many traditional Chinese herbs that have been used to treat obesity. The aim of this study was to elucidate the pharmacological effects and mechanisms of FC on obese rats. Rats were fed with a high-fat diet as a model of obesity and treated with FC at three different dosages for 6 weeks. Pathology in liver tissue was observed. Glucose levels, lipids levels, and inflammatory indicators in serum were evaluated by enzyme‐linked immunosorbent assay. Furthermore, the expression of G protein-coupled receptor 5 (TGR5) pathway genes in rat colon tissue was detected by reverse transcription-polymerase chain reaction analysis (RT-PCR). Our result revealed that FC alleviates obesity by reducing body weight (BW) and waist circumference, managing inflammation and improving glycolipid metabolism, liver function, and liver lipid peroxidation in vivo. In addition, the mechanism of FC on obesity is possibly the stimulation of glucagon-like peptide-1 (GLP-1) secretion by activating the TGR5 pathway in intestinal endocrine cells. Our studies highlight the obesity reduction effects of FC and one of the mechanisms may be the activation of the TGR5 pathway in intestinal endocrine cells