A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection.

Introduction: Arrhythmia is an important indication of underlying cardiovascular diseases (CVD) and is prevalent worldwide. Accurate diagnosis of arrhythmia is crucial for timely and effective treatment. Electrocardiogram (ECG) plays a key role in the diagnosis of arrhythmia. With the continuous development of deep learning and machine learning processes in the clinical field, ECG processing algorithms have significantly advanced the field with timely and accurate diagnosis of arrhythmia. Methods: In this study, we combined the wavelet time-frequency maps with the novel Swin Transformer deep learning model for the automatic detection of cardiac arrhythmias. In specific practice, we used the MIT-BIH arrhythmia dataset, and to improve the signal quality, we removed the high-frequency noise, artifacts, electromyographic noise and respiratory motion effects in the ECG signals by the wavelet thresholding method; we used the complex Morlet wavelet for the feature extraction, and plotted wavelet time-frequency maps to visualise the time-frequency information of the ECG; we introduced the Swin Transformer model for classification and achieve high classification accuracy of ECG signals through hierarchical construction and self attention mechanism, and combines windowed multi-head self-attention (W-MSA) and shifted window-based multi-head self-attention (SW-MSA) to comprehensively utilise the local and global information. Results: To enhance the confidence of the experimental results, we evaluated the performance using intra-patient and inter-patient paradigm analyses, and the model classification accuracies reached 99.34% and 98.37%, respectively, which are better than the currently available detection methods. Discussion: The results reveal that our proposed method is superior to currently available methods for detecting arrhythmia ECG. This provides a new idea for ECG based arrhythmia diagnosis.

Magnetic resonance imaging on brain structure and function changes in diabetic peripheral neuropathy.

With the significant increase in the global prevalence of diabetes mellitus (DM), the occurrence of diabetic peripheral neuropathy (DPN) has become increasingly common complication associated with DM. It is particularly in the peripheral nerves of the hands, legs, and feet. DPN can lead to various adverse consequences that greatly affect the quality of life for individuals with DM. Despite the profound impact of DPN, the specific mechanisms underlying its development and progression are still not well understood. Advancements in magnetic resonance imaging (MRI) technology have provided valuable tools for investigating the central mechanisms involved in DPN. Structural and functional MRI techniques have emerged as important methods for studying the brain structures and functions associated with DPN. Voxel-based morphometry allows researchers to assess changes in the volume and density of different brain regions, providing insights into potential structural alterations related to DPN. Functional MRI investigates brain activity patterns, helping elucidate the neural networks engaged during sensory processing and pain perception in DPN patients. Lastly, magnetic resonance spectroscopy provides information about the neurochemical composition of specific brain regions, shedding light on potential metabolic changes associated with DPN. By synthesizing available literature employing these MRI techniques, this study aims to enhance our understanding of the neural mechanisms underlying DPN and contribute to the improvement of clinical diagnosis.

Structural and functional activities of brain in patients with vascular cognitive impairment: A case-controlled magnetic resonance imaging study.

This study aimed to identify abnormal brain regions and imaging indices of vascular cognitive impairment (VCI) and explore specific imaging diagnostic markers of VCI. In this study, 24 patients with VCI were allocated to the VCI group and 25 healthy subjects were assigned to the healthy control (HC) group. Demographic data and neuropsychological test scores were compared using SPSS 25.0. The structural and functional imaging data were post-processed and statistically analyzed using CAT12, DPARSF and SPM12 software, based on the MATLAB platform. The structural and functional indices of gray matter volume (GMV) and regional homogeneity (ReHo) were obtained, and inter-group data were analyzed using an independent-sample t test. Sex, age, years of education, and total brain volume were used as covariates. Compared to the HC group, the GMV of VCI in the VCI group decreased significantly in the rectus muscles of the bilateral gyrus, left superior temporal gyrus, left supplementary motor area (SMA), right insula, right superior temporal gyrus, right anterior cuneiform lobe, and right anterior central gyrus (PRECG) (P < .05, FWE correction), without GMV enlargement in the brain area. ReHo decreased in the right inferior temporal gyrus (ITG), right parahippocampal gyrus, and left temporal pole (middle temporal gyrus, right lingual gyrus, left posterior central gyrus, and right middle temporal gyrus), the areas of increased ReHo were the left caudate nucleus, left rectus gyrus, right anterior cingulate gyrus and lateral cingulate gyrus (P < .05, FWE correction). Correlation analysis showed that the GMV of the left superior temporal gyrus was positively correlated with the Montreal Cognitive Assessment (MoCA) score (P < .05), and the GMV of the right insula was positively correlated with the MESE and long delayed memory scores (P < .05). There was a significant positive correlation between the ReHo and short-term delayed memory scores in the middle temporal gyrus of the left temporal pole (P < .05). The volume of GMV and ReHo decreased in VCI patients, suggesting that impairment of brain structure and function in specific regions is the central mechanism of cognitive impairment in these patients. Meanwhile, the functional indices of some brain regions were increased, which may be a compensatory mechanism for the cognitive impairment associated with VCI.

Metformin relieves H/R-induced cardiomyocyte injury through miR-19a/ACSL axis - possible therapeutic target for myocardial I/R injury.

This study proposed to investigate the function of miR-19a/ACSL axis in hypoxia/reoxygenation (H/R)-induced myocardial injury and determine whether metformin exerts its protective effect via miR-19a/ACSL axis. Firstly, bioinformatics analysis of data from Gene Expression Omnibus (GEO) database indicated that miR-19a was downregulated in patients with myocardial infarction (MI) compared to that in control group. H/R model was constructed with AC16 cells in vitro. qRT-PCR assay revealed that miR-19a was downregulated in H/R-treated AC16 cells. Then, CCK-8 assay demonstrated that upregulation of miR-19a significantly alleviated H/R-induced decline of cell viability. Moreover, bioinformatics prediction, western blotting and dual-luciferase reporter assays were performed to check the target genes of miR-19a, and ACSL1 was determined as a downstream target gene of miR-19a. Besides, the analysis based on Comparative Toxicogenomics Database (CTD) suggested that metformin targeting ACSL1 can be used as a potential drug for further research. Biological function experiments in vitro revealed that H/R markedly declined the viability and elevated the apoptosis of AC16 cells, while metformin can significantly mitigate these effects. Furthermore, overexpression of miR-19a significantly strengthened the beneficial effect of metformin on H/R-induced AC16 cells injury, which can be reversed by upregulation of ACSL1. In conclusion, metformin can alleviate H/R-induced cells injury via regulating miR-19a/ACSL axis, which lays a foundation for identifying novel targets for myocardial I/R injury therapy.

Sodium Hydroxide Pinpoint Pressing Permeation Method for the Animal Modeling of Sick Sinus Syndrome.

Sodium hydroxide pinpoint pressing permeation (SHPPP) was investigated in order to build a rat model of sick sinus syndrome (SSS), which is easy to operate and control the degree of damage, with fewer complications and applicable for large and small animals.Thirty healthy Wistar rats (15 males and 15 females, weighing 250-350 g) were randomly divided into 3 groups, namely a formaldehyde thoracotomy wet compressing group (FTWC), formaldehyde pinpoint pressing permeation group (FPPP) group, and SHPPP group. The number of surviving rats, heart rate (HR), sinoatrial node recovery time (SNRT), corrected SNRT (CSNRT), and sinoatrial conduction time (SACT) were recorded 3 days, one week, and two weeks after modeling.The achievement ratio of modeling was 10% in the FTWC group, 40% in the FPPP group, and 70% in the SHPPP group, and the differences were statistically significant (χ(2) = 7.250, P = 0.007). Meanwhile, the HR was reduced by about 37% in these 3 groups 3 days after modeling, while the reduction was maintained only in SHPPP (P > 0.05) and the HR was re-elevated in the FTWC and FPPP groups 2 weeks after modeling (P < 0.05). Additionally, the SNRT, cS-NRT, and SACT were significantly prolonged compared with pre-modeling in all 3 groups (P < 0.01).SHPPP was the best method with which to build an SSS model with stable and lasting low HR and high success rate of modeling, which might be helpful for further studies on the SSS mechanisms and drugs.