Diagnostic efficacy of two-dimensional echocardiography combined with coronary angiogram in patients with acute myocardial infarction, and the effectiveness of atorvastatin

Purpose: To investigate the diagnostic significance of two-dimensional echocardiography (2DE) combined with coronary angiogram in patients with acute myocardial infarction, and to determine the effectiveness of atorvastatin.Methods: Patients (n = 100) with acute myocardial infarction admitted in Affiliated Hospital of Jinggangshan University, Ji’an, China, were divided into control group (CG) treated with conventional therapy, and study group (EG) treated with atorvastatin, in addition to conventional therapy. The diseased vessels examined by echocardiography and coronary angiogramv were recorded and compared. The effectiveness of atorvastatin treatment was assessed by evaluating myocardial injury, oxidative stress, vascular injury and cardiac function indices, viz, left ventricular ejection fraction (LVEF), left ventricular end-diastolic internal diameter (LVEDD), left ventricular end-systolic internal diameter (LVESD), left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV).Results: The number of double and multiple lesions shown on coronary angiogram were significantly higher than those shown in echocardiography. Phosphocreatine kinase (CK), creatine kinase isoenzyme (CK-MB) and cardiac troponin T (cTnT) improved significantly in both groups after surgery, with CK-MB significantly lower in EG than those in CG at 1 and 3 days post-operation. Interleukin-6 (IL- 6) was significantly lower in EG than in CG at 1 and 7 days after the surgery (p < 0.05). N-terminal Btype natriuretic peptidogen (NT-proBNP) was lower in EG than in CG on the 3rd day after surgery (p < 0.05). Superoxide dismutase (SOD) was significantly higher in EG than in the CG at 1, 3 and 7 days after surgery. The QRS scores significantly improved in both groups after surgery (p < 0.05).Conclusion: Echocardiography, when used in combination with coronary angiogram, accurately assesses the coronary lesions in acute myocardial infarction, and atorvastatin treatment after PCI reduces myocardial injury, relieves inflammation, and promotes the recovery of cardiac function in patients

Changing Patterns of Spatial Clustering of Schistosomiasis in Southwest China between 1999–2001 and 2007–2008: Assessing Progress toward Eradication after the World Bank Loan Project

We compared changes in the spatial clustering of schistosomiasis in Southwest China at the conclusion of and six years following the end of the World Bank Loan Project (WBLP), the control strategy of which was focused on the large-scale use of chemotherapy. Parasitological data were obtained through standardized surveys conducted in 1999–2001 and again in 2007–2008. Two alternate spatial cluster methods were used to identify spatial clusters of cases: Anselin’s Local Moran’s I test and Kulldorff’s spatial scan statistic. Substantial reductions in the burden of schistosomiasis were found after the end of the WBLP, but the spatial extent of schistosomiasis was not reduced across the study area. Spatial clusters continued to occur in three regions: Chengdu Plain, Yangtze River Valley, and Lancang River Valley during the two periods, and regularly involved five counties. These findings suggest that despite impressive reductions in burden, the hilly and mountainous regions of Southwest China remain at risk of schistosome re-emergence. Our results help to highlight specific locations where integrated control programs can focus to speed the elimination of schistosomiasis in China

A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring

Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.publishedVersio

A-GSTCN: An Augmented Graph Structural–Temporal Convolution Network for Medication Recommendation Based on Electronic Health Records

Medication recommendation based on electronic health records (EHRs) is a significant research direction in the biomedical field, which aims to provide a reasonable prescription for patients according to their historical and current health conditions. However, the existing recommended methods have many limitations in dealing with the structural and temporal characteristics of EHRs. These methods either only consider the current state while ignoring the historical situation, or fail to adequately assess the structural correlations among various medical events. These factors result in poor recommendation quality. To solve this problem, we propose an augmented graph structural–temporal convolutional network (A-GSTCN). Firstly, an augmented graph attention network is used to model the structural features among medical events of patients’ EHRs. Next, the dilated convolution combined with residual connection is applied in the proposed model, which can improve the temporal prediction capability and further reduce the complexity. Moreover, the cache memory module further enhances the model’s learning of the history of EHRs. Finally, the A-GSTCN model is compared with the baselines through experiments, and the efficiency of the A-GSTCN model is verified by Jaccard, F1 and PRAUC. Not only that, the proposed model also reduces the training parameters by an order of magnitude.publishedVersio

Defect identification in adhesive structures using multi-Feature fusion convolutional neural network

The interface-debonding defects of adhesive bonding structures may cause a reduction in bonding strength, which in turn affects the bonding quality of adhesive bonding samples. Hence, defect recognition in adhesive bonding structures is particularly important. In this study, a terahertz (THz) wave was used to analyze bonded structure samples, and a multi-feature fusion convolutional neural network (CNN) was used to identify the defect waveforms. The pooling method of the squeeze-and-excitation (SE) attention mechanism was optimized, defect feature weights were adaptively assigned, and feature fusion was conducted using automatic label net-works to segment the THz waveforms in the adhesive bonding area with fine granularity waveforms as an input to the multi-channel CNN. The results revealed that the speed of the THz waveform labeling with the automatic labeling network was 10 times higher than that with traditional methods, and the defect-recognition accuracy of the defect-recognition network constructed in this study was up to 99.28%. The F1-score was 99.73%, and the lowest pre-embedded defect recognition error rate of the generalization experiment samples was 0.27%

The Role of High Mobility Group Box 1 in Ischemic Stroke

High-mobility group box 1 protein (HMGB1) is a novel, cytokine-like, and ubiquitous, highly conserved, nuclear protein that can be actively secreted by microglia or passively released by necrotic neurons. Ischemic stroke is a leading cause of death and disability worldwide, and the outcome is dependent on the amount of hypoxia-related neuronal death in the cerebral ischemic region. Acting as an endogenous danger-associated molecular pattern (DAMP) protein, HMGB1 mediates cerebral inflammation and brain injury and participates in the pathogenesis of ischemic stroke. It is thought that HMGB1 signals via its presumed receptors, such as toll-like receptors (TLRs), matrix metalloproteinase (MMP) enzymes, and receptor for advanced glycation end products (RAGEs) during ischemic stroke. In addition, the release of HMGB1 from the brain into the bloodstream influences peripheral immune cells. However, the role of HMGB1 in ischemic stroke may be more complex than this and has not yet been clarified. Here, we summarize and review the research into HMGB1 in ischemic stroke

Pleiotropic effects of the twin-arginine translocation system on biofilm formation, colonization, and virulence in Vibrio cholerae

<p>Abstract</p> <p>Background</p> <p>The Twin-arginine translocation (Tat) system serves to translocate folded proteins, including periplasmic enzymes that bind redox cofactors in bacteria. The Tat system is also a determinant of virulence in some pathogenic bacteria, related to pleiotropic effects including growth, motility, and the secretion of some virulent factors. The contribution of the Tat pathway to <it>Vibrio cholerae </it>has not been explored. Here we investigated the functionality of the Tat system in <it>V. cholerae</it>, the etiologic agent of cholera.</p> <p>Results</p> <p>In <it>V. cholerae</it>, the <it>tatABC </it>genes function in the translocation of TMAO reductase. Deletion of the <it>tatABC </it>genes led to a significant decrease in biofilm formation, the ability to attach to HT-29 cells, and the ability to colonize suckling mouse intestines. In addition, we observed a reduction in the output of cholera toxin, which may be due to the decreased transcription level of the toxin gene in <it>tatABC </it>mutants, suggesting an indirect effect of the mutation on toxin production. No obvious differences in flagellum biosynthesis and motility were found between the <it>tatABC </it>mutant and the parental strain, showing a variable effect of Tat in different bacteria.</p> <p>Conclusion</p> <p>The Tat system contributes to the survival of <it>V. cholerae </it>in the environment and <it>in vivo</it>, and it may be associated with its virulence.</p

Predictive value of PIMREG in the prognosis and response to immune checkpoint blockade of glioma patients

Glioma is the most common primary brain tumor in the human brain. The present study was designed to explore the expression of PIMREG in glioma and its relevance to the clinicopathological features and prognosis of glioma patients. The correlations of PIMREG with the infiltrating levels of immune cells and its relevance to the response to immunotherapy were also investigated. PIMREG expression in glioma was analyzed based on the GEO, TCGA, and HPA databases. Kaplan–Meier survival analysis was used to examine the predictive value of PIMREG for the prognosis of patients with glioma. The correlation between the infiltrating levels of immune cells in glioma and PIMREG was analyzed using the CIBERSORT algorithm and TIMRE database. The correlation between PIMREG and immune checkpoints and its correlation with the patients’ responses to immunotherapy were analyzed using R software and the GEPIA dataset. Cell experiments were conducted to verify the action of PIMREG in glioma cell migration and invasion. We found that PIMREG expression was upregulated in gliomas and positively associated with WHO grade. High PIMREG expression was correlated with poor prognosis of LGG, prognosis of all WHO grade gliomas, and prognosis of recurrent gliomas. PIMREG was related to the infiltration of several immune cell types, such as M1 and M2 macrophages, monocytes and CD8+ T cells. Moreover, PIMREG was correlated with immune checkpoints in glioma and correlated with patients’ responses to immunotherapy. KEGG pathway enrichment and GO functional analysis illustrated that PIMREG was related to multiple tumor- and immune-related pathways. In conclusion, PIMREG overexpression in gliomas is associated with poor prognosis of patients with glioma and is related to immune cell infiltrates and the responses to immunotherapy

Genome-wide comparison of DNA hydroxymethylation in mouse embryonic stem cells and neural progenitor cells by a new comparative hMeDIP-seq method

The genome-wide distribution patterns of the ‘6th base’ 5-hydroxymethylcytosine (5hmC) in many tissues and cells have recently been revealed by hydroxymethylated DNA immunoprecipitation (hMeDIP) followed by high throughput sequencing or tiling arrays. However, it has been challenging to directly compare different data sets and samples using data generated by this method. Here, we report a new comparative hMeDIP-seq method, which involves barcoding different input DNA samples at the start and then performing hMeDIP-seq for multiple samples in one hMeDIP reaction. This approach extends the barcode technology from simply multiplexing the DNA deep sequencing outcome and provides significant advantages for quantitative control of all experimental steps, from unbiased hMeDIP to deep sequencing data analysis. Using this improved method, we profiled and compared the DNA hydroxymethylomes of mouse ES cells (ESCs) and mouse ESC-derived neural progenitor cells (NPCs). We identified differentially hydroxymethylated regions (DHMRs) between ESCs and NPCs and uncovered an intricate relationship between the alteration of DNA hydroxymethylation and changes in gene expression during neural lineage commitment of ESCs. Presumably, the DHMRs between ESCs and NPCs uncovered by this approach may provide new insight into the function of 5hmC in gene regulation and neural differentiation. Thus, this newly developed comparative hMeDIP-seq method provides a cost-effective and user-friendly strategy for direct genome-wide comparison of DNA hydroxymethylation across multiple samples, lending significant biological, physiological and clinical implications