An efficient ECG denoising method by fusing ECA-Net and CycleGAN.

For wearable electrocardiogram (ECG) acquisition, it was easy to infer motion artifices and other noises. In this paper, a novel end-to-end ECG denoising method was proposed, which was implemented by fusing the Efficient Channel Attention (ECA-Net) and the cycle consistent generative adversarial network (CycleGAN) method. The proposed denoising model was optimized by using the ECA-Net method to highlight the key features and introducing a new loss function to further extract the global and local ECG features. The original ECG signal came from the MIT-BIH Arrhythmia Database. Additionally, the noise signals used in this method consist of a combination of Gaussian white noise and noises sourced from the MIT-BIH Noise Stress Test Database, including EM (Electrode Motion Artifact), BW (Baseline Wander) and MA (Muscle Artifact), as well as mixed noises composed of EM+BW, EM+MA, BW+MA and EM+BW+MA. Moreover, corrupted ECG signals were generated by adding different levels of single and mixed noises to clean ECG signals. The experimental results show that the proposed method has better denoising performance and generalization ability with higher signal-to-noise ratio improvement (SNRimp), as well as lower root-mean-square error (RMSE) and percentage-root-mean-square difference (PRD).

Effect of Pramipexole on Inflammatory Response in Central Nervous System of Parkinson's Disease Rat Model.

BACKGROUND: Pramipexole is the dopamine receptor agonist commonly used for treatment of PD, the effect of which on immunity played an important role in pathological process is also deserved to be further studied. AIM OF STUDY: We observed the effect of pramipexole on behavior and central nervous system (CNS) inflammatory cytokines of Parkinson's Disease (PD) model rats. METHODS: We injected 3.0 µL lipopolysaccharide into the right substantia nigra compact (SNc) and ventral tegmental area (VTA) of sprague-dawley (SD) rats to establish PD models which were divided as treated group feeded with pramipexole for 14 d and untreated group feeded with saline. And SD rats were selected as control group feeded with saline. We conducted rotation test on PD model rats before and after treatment. We also performed euthanasia on all rats to obtain the striatum area and nearby tissues after treatment, measuring mRNA expression and concentration of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) by reverse transcription polymerase chain reaction test and elisa method, respectively. RESULTS: It was observed that the degree of behavior improvement in treated group was greater than that in untreated group. In addition, mRNA expression and concentrations of IL-6 and TNF-α in treated group were lower than those in untreated group and higher than those in control group. CONCLUSIONS: Pramipexole improved behavior of PD model rat, and down regulated the mRNA expression and concentrations of IL-6 and TNF-α in their CNS.

Bioinformatics and Network Pharmacology Identify the Therapeutic Role and Potential Mechanism of Melatonin in AD and Rosacea.

Rosacea is significantly associated with dementia, particularly Alzheimer's disease (AD). However, the common underlying molecular mechanism connecting these two diseases remains limited. This study aimed to reveal the common molecular regulatory networks and identify the potential therapeutic drugs for rosacea and AD. There were 747 overlapped DEGs (ol-DEGs) that were detected in AD and rosacea, enriched in inflammation-, metabolism-, and apoptosis-related pathways. Using the TF regulatory network analysis, 37 common TFs and target genes were identified as hub genes. They were used to predict the therapeutic drugs for rosacea and AD using the DGIdb/CMap database. Among the 113 predicted drugs, melatonin (MLT) was co-associated with both RORA and IFN-γ in AD and rosacea. Subsequently, network pharmacology analysis identified 19 pharmacological targets of MLT and demonstrated that MLT could help in treating AD/rosacea partly by modulating inflammatory and vascular signaling pathways. Finally, we verified the therapeutic role and mechanism of MLT on rosacea in vivo and in vitro. We found that MLT treatment significantly improved rosacea-like skin lesion by reducing keratinocyte-mediated inflammatory cytokine secretion and repressing the migration of HUVEC cells. In conclusion, this study contributes to common pathologies shared by rosacea and AD and identified MLT as an effective treatment strategy for rosacea and AD via regulating inflammation and angiogenesis.

A hybrid model of maximum margin clustering method and support vector regression for noninvasive electrocardiographic imaging.

Noninvasive electrocardiographic imaging, such as the reconstruction of myocardial transmembrane potentials (TMPs) distribution, can provide more detailed and complicated electrophysiological information than the body surface potentials (BSPs). However, the noninvasive reconstruction of the TMPs from BSPs is a typical inverse problem. In this study, this inverse ECG problem is treated as a regression problem with multi-inputs (BSPs) and multioutputs (TMPs), which will be solved by the Maximum Margin Clustering- (MMC-) Support Vector Regression (SVR) method. First, the MMC approach is adopted to cluster the training samples (a series of time instant BSPs), and the individual SVR model for each cluster is then constructed. For each testing sample, we find its matched cluster and then use the corresponding SVR model to reconstruct the TMPs. Using testing samples, it is found that the reconstructed TMPs results with the MMC-SVR method are more accurate than those of the single SVR method. In addition to the improved accuracy in solving the inverse ECG problem, the MMC-SVR method divides the training samples into clusters of small sample sizes, which can enhance the computation efficiency of training the SVR model.