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Objective. To observe the effect of adenosine A1 receptor in the hippocampus of mice on GSK-3β phosphorylation level and elucidate the underlying mechanisms of electroacupuncture pretreatment by activating Α1 receptor mediating cerebral ischemia-reperfusion injury. Method. The model of middle cerebral artery occlusion (MCAO) was established and grouped into electroacupuncture pretreatment group (EA group), MCAO group, and sham-operated group (Sham group). The neurobehavioral manifestation, the volume of cerebral infarction, and its related protein changes in mice in each group were observed. Then, adenosine Α1 receptor antagonist and agonist were injected intraperitoneally to observe the effects of A1 receptor on the phosphorylation level of GSK-3β, neurobehavioral changes, and infarction volume. Results. (1) Compared with the MCAO group (24 hours after reperfusion), the infarct size in the EA group decreased significantly, and the Garcia neurological score and phosphorylation level of GSK-3β are increased. (2) Compared with the EA group, the infarct size in the A1 receptor antagonist 8-cyclopentyl-1,3-dipropylxanthine (DPCPX) group increased significantly, and the Garcia neurological score and phosphorylation level of GSK-3β are decreased. (3) Compared with the MCAO group, the infarct size in the A1 receptor agonist 2-Chloro-N6-cyclopentyladenosine (CCPA) group decreased significantly, and the Garcia neurological score and phosphorylation level of GSK-3β are increased. There was no significant difference between the EA group and CCPA group. Conclusions. Electroacupuncture pretreatment can increase GSK-3β phosphorylation level via activating A1 receptor, to protect neurons in ischemia-reperfusion injury. [ABSTRACT FROM AUTHOR]

Introduction. The global incidence of brain tumors, the most common of which is lower grade glioma (LGG), remains high. Pleckstrin homology domain-containing family A member 4 (PLEKHA4) has been reported to be related to tumor invasion and growth. However, its role and correlation with immunity in LGG remain elusive. Methods. We evaluated the expression pattern, prognostic value, biological functions, and immune effects of PLEKHA4 in LGG. We also analyzed the association between PLEKHA4 levels in different tumors, patient prognosis, and its role in tumor immunity. Depending on the type of research data, we used statistical methods such as Student's t -tests, Mann–Whitney U tests one-way ANOVA tests Kruskal–Wallis tests Pearson's or Spearman's correlation analysis Chi-square and Fisher's exact tests in this paper. Results and Conclusions. The results revealed that PLEKHA4 levels were markedly elevated in most tumors (such as LGG). High PLEKHA4 levels are associated with poor overall survival (OS), progression-free interval (PFI) rates, and disease-specific survival (DSS) in LGG patients. Cox regression analysis and nomograms showed that PLEKHA4 levels are independent prognostic factors for LGG patients. According to functional enrichment analysis, PLEKHA4 levels in LGG are associated with immune infiltration and immunotherapy. In conclusion, PLEKHA4 is a potential prognostic marker and immunotherapy target for LGG. [ABSTRACT FROM AUTHOR]

Background. Myopathies related to Ryanodine receptor 1 (RYR1) mutation are the most common nondystrophy muscle disorder in humans. Early detection and diagnosis of RYR1 mutation-associated myopathies may lead to more timely treatment of patients, which contributes to the management and preparation for malignant hyperthermia. However, diagnosis of RYR1 mutation-associated myopathies is delayed and challenging. The absence of diagnostic morphological features in muscle biopsy does not rule out the possibility of pathogenic variations in RYR1. Accordingly, it is helpful to seek biomarkers to diagnose RYR1 mutation-associated myopathies. Methods. Skeletal muscle tissue microarray datasets of RYR1 mutation-associated myopathies or healthy persons were built in accordance with the gene expression synthesis (GEO) database. Differentially expressed genes (DEGs) were identified on the basis of R software. Genes specific to tissue/organ were identified through BioGPS. An enrichment analysis of DEGs was conducted in accordance with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We also built protein-protein interaction (PPI) networks to explore the function and enrichment pathway of DEGs and the identification of hub genes. Lastly, the ROC curve was drawn for hub genes achieving specific expressions within skeletal muscle. Moreover, the area under the curve (AUC) was obtained to calculate the predictive value of key genes. The transcription factors of hub genes achieving specific expressions within skeletal muscle were predicted with the use of the iRegulon plugin. Results. We identified 170 DEGs among 11 muscle biopsy samples of healthy subjects and 17 muscle biopsy samples of RYR1 mutation-associated myopathy patients in the dataset. Among the above DEGs, 30 genes achieving specific expressions within tissues/organs were found. GO and KEGG enrichment analysis of DEGs mainly focused on muscle contraction, actin-mediated cell contraction, actin filament-based movement, and muscular sliding. 12 hub genes were identified with the use of Cytoscape. Four hub genes were specifically expressed in skeletal muscle tissue, including MYH1 (AUC: 0.856), TNNT3 (AUC: 0.840), MYLPF (AUC: 0.786), and ATP2A1 (AUC: 0.765). The iRegulon predicted results suggested that the transcription factor MYF6 was found with the highest reliability. Conclusions. Four skeletal muscle tissue-specific genes were identified, including MYH1, TNNT3, MYLPF, and ATP2A1, as the potential biomarkers for diagnosing and treating RYR1 mutation-associated myopathies, which provided insights into the transcriptome-level development mechanism. The transcription factor MYF6 may be a vital upstream regulator of the above biomarkers. [ABSTRACT FROM AUTHOR]

Background. Myopathies related to Ryanodine receptor 1 (RYR1) mutation are the most common nondystrophy muscle disorder in humans. Early detection and diagnosis of RYR1 mutation-associated myopathies may lead to more timely treatment of patients, which contributes to the management and preparation for malignant hyperthermia. However, diagnosis of RYR1 mutation-associated myopathies is delayed and challenging. The absence of diagnostic morphological features in muscle biopsy does not rule out the possibility of pathogenic variations in RYR1. Accordingly, it is helpful to seek biomarkers to diagnose RYR1 mutation-associated myopathies. Methods. Skeletal muscle tissue microarray datasets of RYR1 mutation-associated myopathies or healthy persons were built in accordance with the gene expression synthesis (GEO) database. Differentially expressed genes (DEGs) were identified on the basis of R software. Genes specific to tissue/organ were identified through BioGPS. An enrichment analysis of DEGs was conducted in accordance with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We also built protein-protein interaction (PPI) networks to explore the function and enrichment pathway of DEGs and the identification of hub genes. Lastly, the ROC curve was drawn for hub genes achieving specific expressions within skeletal muscle. Moreover, the area under the curve (AUC) was obtained to calculate the predictive value of key genes. The transcription factors of hub genes achieving specific expressions within skeletal muscle were predicted with the use of the iRegulon plugin. Results. We identified 170 DEGs among 11 healthy subject samples and 18 RYR1 mutation-associated myopathy samples in the dataset. Among the above DEGs, 31 genes achieving specific expressions within tissues/organs were found. GO and KEGG enrichment analysis of DEGs mainly focused on muscle contraction, actin-mediated cell contraction, actin filament-based movement, and muscular sliding. 12 hub genes were identified with the use of Cytoscape. Four hub genes were specifically expressed in skeletal muscle tissue, including MYH1 (AUC: 0.856), TNNT3 (AUC: 0.840), MYLPF (AUC: 0.786), and ATP2A1 (AUC: 0.765). The iRegulon predicted results suggested that the transcription factor MYF6 was found with the highest reliability. Conclusions. Four skeletal muscle tissue-specific genes were identified, including MYH1, TNNT3, MYLPF, and ATP2A1, as the potential biomarkers for diagnosing and treating RYR1 mutation-associated myopathies, which provided insights into the transcriptome-level development mechanism. The transcription factor MYF6 may be a vital upstream regulator of the above biomarkers. [ABSTRACT FROM AUTHOR]

Background. Myopathies related to Ryanodine receptor 1 (RYR1) mutation are the most common nondystrophy muscle disorder in humans. Early detection and diagnosis of RYR1 mutation-associated myopathies may lead to more timely treatment of patients, which contributes to the management and preparation for malignant hyperthermia. However, diagnosis of RYR1 mutation-associated myopathies is delayed and challenging. The absence of diagnostic morphological features in muscle biopsy does not rule out the possibility of pathogenic variations in RYR1. Accordingly, it is helpful to seek biomarkers to diagnose RYR1 mutation-associated myopathies. Methods. Skeletal muscle tissue microarray datasets of RYR1 mutation-associated myopathies or healthy persons were built in accordance with the gene expression synthesis (GEO) database. Differentially expressed genes (DEGs) were identified on the basis of R software. Genes specific to tissue/organ were identified through BioGPS. An enrichment analysis of DEGs was conducted in accordance with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We also built protein-protein interaction (PPI) networks to explore the function and enrichment pathway of DEGs and the identification of hub genes. Lastly, the ROC curve was drawn for hub genes achieving specific expressions within skeletal muscle. Moreover, the area under the curve (AUC) was obtained to calculate the predictive value of key genes. The transcription factors of hub genes achieving specific expressions within skeletal muscle were predicted with the use of the iRegulon plugin. Results. We identified 170 DEGs among 11 healthy subject samples and 18 RYR1 mutation-associated myopathy samples in the dataset. Among the above DEGs, 31 genes achieving specific expressions within tissues/organs were found. GO and KEGG enrichment analysis of DEGs mainly focused on muscle contraction, actin-mediated cell contraction, actin filament-based movement, and muscular sliding. 12 hub genes were identified with the use of Cytoscape. Four hub genes were specifically expressed in skeletal muscle tissue, including MYH1 (AUC: 0.856), TNNT3 (AUC: 0.840), MYLPF (AUC: 0.786), and ATP2A1 (AUC: 0.765). The iRegulon predicted results suggested that the transcription factor MYF6 was found with the highest reliability. Conclusions. Four skeletal muscle tissue-specific genes were identified, including MYH1, TNNT3, MYLPF, and ATP2A1, as the potential biomarkers for diagnosing and treating RYR1 mutation-associated myopathies, which provided insights into the transcriptome-level development mechanism. The transcription factor MYF6 may be a vital upstream regulator of the above biomarkers. [ABSTRACT FROM AUTHOR]

Background. Myopathies related to Ryanodine receptor 1 (RYR1) mutation are the most common nondystrophy muscle disorder in humans. Early detection and diagnosis of RYR1 mutation-associated myopathies may lead to more timely treatment of patients, which contributes to the management and preparation for malignant hyperthermia. However, diagnosis of RYR1 mutation-associated myopathies is delayed and challenging. The absence of diagnostic morphological features in muscle biopsy does not rule out the possibility of pathogenic variations in RYR1. Accordingly, it is helpful to seek biomarkers to diagnose RYR1 mutation-associated myopathies. Methods. Skeletal muscle tissue microarray datasets of RYR1 mutation-associated myopathies or healthy persons were built in accordance with the gene expression synthesis (GEO) database. Differentially expressed genes (DEGs) were identified on the basis of R software. Genes specific to tissue/organ were identified through BioGPS. An enrichment analysis of DEGs was conducted in accordance with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We also built protein-protein interaction (PPI) networks to explore the function and enrichment pathway of DEGs and the identification of hub genes. Lastly, the ROC curve was drawn for hub genes achieving specific expressions within skeletal muscle. Moreover, the area under the curve (AUC) was obtained to calculate the predictive value of key genes. The transcription factors of hub genes achieving specific expressions within skeletal muscle were predicted with the use of the iRegulon plugin. Results. We identified 170 DEGs among 11 healthy subject samples and 18 RYR1 mutation-associated myopathy samples in the dataset. Among the above DEGs, 31 genes achieving specific expressions within tissues/organs were found. GO and KEGG enrichment analysis of DEGs mainly focused on muscle contraction, actin-mediated cell contraction, actin filament-based movement, and muscular sliding. 12 hub genes were identified with the use of Cytoscape. Four hub genes were specifically expressed in skeletal muscle tissue, including MYH1 (AUC: 0.856), TNNT3 (AUC: 0.840), MYLPF (AUC: 0.786), and ATP2A1 (AUC: 0.765). The iRegulon predicted results suggested that the transcription factor MYF6 was found with the highest reliability. Conclusions. Four skeletal muscle tissue-specific genes were identified, including MYH1, TNNT3, MYLPF, and ATP2A1, as the potential biomarkers for diagnosing and treating RYR1 mutation-associated myopathies, which provided insights into the transcriptome-level development mechanism. The transcription factor MYF6 may be a vital upstream regulator of the above biomarkers. [ABSTRACT FROM AUTHOR]

Background. Acute spinal cord injury (SCI) is one of the most common and devastating causes of sensory or motor dysfunction. Nuclear factor-kappa B(NF-κB)-mediated neuroinflammatory responses, in addition to nitric oxide (NO), are key regulatory pathways in SCI. Paeoniflorin (PF), a major active component extracted from Paeonia roots, has been suggested to exert neuroprotective effects in the central nervous system. However, whether PF could improve the motor function after SCI in vivo is still unclear. Method. Immunohistochemical analysis, western blot, real-time quantitative PCR, immunofluorescence staining, and histopathological and behavioral evaluation were used to explore the effects of paeoniflorin after SCI for 14 days. Results. In this study, PF treatment significantly inhibited NF-κB activation and downregulated the expression of inducible nitric oxide synthase (iNOS), cyclooxygenase-2(COX-2), and Nogo-A. Comparing behavioral and histological changes in SCI and PF treatment groups, we found that PF treatment improved motor function recovery, attenuated the histopathological damage, and increased neuronal survival in the SCI model. PF treatment also reduced expression levels of Bax and c-caspase-3 and increased the expression level of Bcl-2 and cell viabilities. Upregulation of TNF-α, IL-6, and IL-1β after injury was also prevented by PF. Conclusion. These results suggest that the neuroprotective effects of PF are related to the inhibition of the NF-κB signaling pathway. And PF may be a therapeutic strategy in spinal cord injury. [ABSTRACT FROM AUTHOR]

Background and Aims. Early prediction of disease severity of acute pancreatitis (AP) would be helpful for triaging patients to the appropriate level of care and intervention. The aim of the study was to develop a model able to predict Severe Acute Pancreatitis (SAP). Methods. A total of 647 patients with AP were enrolled. The demographic data, hematocrit, High-Density Lipoprotein Cholesterol (HDL-C) determinant at time of admission, Blood Urea Nitrogen (BUN), and serum creatinine (Scr) determinant at time of admission and 24 hrs after hospitalization were collected and analyzed statistically. Results. Multivariate logistic regression indicated that HDL-C at admission and BUN and Scr at 24 hours (hrs) were independently associated with SAP. A logistic regression function (LR model) was developed to predict SAP as follows: −2.25–0.06 HDL-C (mg/dl) at admission + 0.06 BUN (mg/dl) at 24 hours + 0.66 Scr (mg/dl) at 24 hours. The optimism-corrected c-index for LR model was 0.832 after bootstrap validation. The area under the receiver operating characteristic curve for LR model for the prediction of SAP was 0.84. Conclusions. The LR model consists of HDL-C at admission and BUN and Scr at 24 hours, representing an additional tool to stratify patients at risk of SAP. [ABSTRACT FROM AUTHOR]