The sensitivity and specificity of microRNAs (miRNAs) for diagnosing glioma are controversial. We therefore performed a meta-analysis to systematically identify glioma-associated miRNAs. We initially screened five miRNA microarray datasets to evaluate the differential expression of miRNAs between glioma and normal tissues. We next compared the expression of the miRNAs in different organs and tissues to assess the sensitivity and specificity of the differentially expressed miRNAs in the diagnosis of glioma. Finally, pathway analysis was performed using GeneGO. We identified 27 candidate miRNAs associated with glioma initiation, progression, and patient prognosis. Sensitivity and specificity analysis indicated miR-15a, miR-16, miR-21, miR-23a, and miR-9 were up-regulated, while miR-124 was down-regulated in glioma. Ten signaling pathways showed the strongest association with glioma development and progression: the p53 pathway feedback loops 2, Interleukin signaling pathway, Toll receptor signaling pathway, Parkinson's disease, Notch signaling pathway, Cadherin signaling pathway, Apoptosis signaling pathway, VEGF signaling pathway, Alzheimer disease-amyloid secretase pathway, and the FGF signaling pathway. Our results indicate that the integration of miRNA, gene, and protein expression data can yield valuable biomarkers for glioma diagnosis and treatment. Indeed, six of the miRNAs identified in this study may be useful diagnostic and prognostic biomarkers in glioma.

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Objective: Decompressive craniectomy (DC) plays an important role in the treatment of patients with severe traumatic brain injury (sTBI) with mass lesions and intractably elevated intracranial hypertension (ICP). However, whether DC should be performed in patients with bilateral dilated pupils and a low Glasgow Coma Scale (GCS) score is still controversial. This retrospective study explored the clinical outcomes and risk factors for an unfavorable prognosis in sTBI patients undergoing emergency DC with bilateral dilated pupils and a GCS score <5.Methods: The authors reviewed the data from patients who underwent emergency DC from January 2012 to March 2019 in a medical center in China. All data, such as patient demographics, radiological findings, clinical parameters, and preoperative laboratory variables, were extracted. Multivariate logistic regression analysis was performed to determine the factors associated with 30-day mortality and 6-month negative neurological outcome {defined as death or vegetative state [Glasgow Outcome Scale (GOS) score 1–2]}.Results: A total of 94 sTBI patients with bilateral dilated pupils and a GCS score lower than five who underwent emergency DC were enrolled. In total, 74 patients (78.7%) died within 30 days, and 84 (89.4%) had a poor 6-month outcome (GOS 1–2). In multivariate analysis, advanced age (OR: 7.741, CI: 2.288–26.189), prolonged preoperative activated partial thromboplastin time (aPTT) (OR: 7.263, CI: 1.323–39.890), and low GCS (OR: 6.162, CI: 1.478–25.684) were associated with a higher risk of 30-day mortality, while advanced age (OR: 8.812, CI: 1.817–42.729) was the only independent predictor of a poor 6-month prognosis in patients undergoing DC with preoperative bilateral dilated pupils and a GCS score <5.Conclusions: The mortality and disability rates are extremely high in severe TBI patients undergoing emergency DC with bilateral fixed pupils and a GCS score <5. DC is more valuable for younger patients.

Patent documents are a special long text format, and traditional deep learning methods have insufficient feature extraction ability, which results in a weaker classification effect than ordinary text. Based on this, this paper constructs a text feature extraction method based on the lexical network, according to the inner relation between words and classification. Firstly, the inner relationship between words and classification was obtained from linear and probability dimensions and the lexical network were constructed. Secondly, the lexical network is fused with the features extracted from the deep learning model. Finally, the fusion features are trained in the original model to get the final classification result. T This method is a classification enhancement method that can classify patent text alone or enhance the accuracy of various types of neural networks in patent text classification. Experimental results demonstrate that the accuracy of BERT combined with lexical network method is as high as 82.73%, and the accuracy of lexical network method combined with CNN and LSTM is increased by 2.19% and 2.25% respectively. In addition, it was demonstrated that the lexical network feature extraction method accelerated the convergence speed of the model during training and improved the classification ability of the model in Chinese patent texts.