ObjectiveTo establish a more accurately method to detect the residue of 1,4-butanediol diglycidyl-ether (BDDE) in the cross-linked sodium hyaluronic gel so as to provide a scientific testing method for the quality control. MethodsThe gas chromatography was used to explore the thermal stability of BDDE, and the residues of BDDE in sodium hyaluronic gel was detected by fluorescence spectrophotometry. The hyaluronidase was added to the BDDE standard solution and the improved fluorescence spectrophotometer was used to detect the BDDE residues in the cross-linked hyaluronic sodium gel. ResultsA good linearity was obtained as y=14.102x+1.103 (R2=0.999 8) for BDDE. BDDE was unstable under high temperature and long storage time. The relevant fluorescence intensity was detected with hyaluronidase solution. After adding hyaluronidase into the BDDE standard solutions, the advanced linearity was obtained as y=14.027x+7.062 (R2=0.999 9). ConclusionFluorescent spectrophotometry is a simple, rapid, and accurate method to analyze BDDE residue of cross-linked sodium hyaluronic gel. Due to the poor thermal stability, all the factors related to temperature must be excluded during the process, including the temperature control of every step. Furthermore, the adding of hyaluronidase in the pre-preparation of cross-linked sodium hyaluronic gel can bring interference. So when using fluorescent spectrophotometry, adjustment must be taken before the calibration curve is preparation.
ObjectiveTo systematically review the value of intra-operative ultrasound in diagnosis of tumor residue after resection of intracranial gliomas. MethodsSuch databases as PubMed, EMbase, The Cochrane Library, CBM, CNKI, WanFang Data and VIP were electronically searched for the diagnostic test about intra-operative ultrasound in diagnosis of tumor residue after resection of intracranial gliomas by March 31st, 2013. Meanwhile, search engines such as Google, Baidu were also used for relevant search. According to the inclusion and exclusion criteria, the literature was screened and the data were extracted. The methodological quality was evaluated in accordance with the quality assessment tool for diagnostic accuracy studies (QUADAS) and then meta-analysis was conducted using Meta-DiSc 1.4 software. ResultsA total of 10 studies involving 423 patients were included. The results of meta-analysis showed that the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio (DOR) were 0.78 (95%CI 0.74 to 0.82), 0.90 (95%CI 0.88 to 0.90), 5.12 (95%CI 2.86 to 9.16), 0.29 (95%CI 0.21 to 0.39) and 25.00 95%CI (13.27 to 47.10), respectively; and the AUC was 0.89. In the subgroup analysis, for low grade intracranial gliomas, the results of meta-analysis showed that the sensitivity, specificity and DOR were 0.87 (95%CI 0.77 to 0.94), 0.88 (95%CI 0.78 to 0.94) and 28.93 (95%CI 7.46 to 112.14), respectively, and the AUC was 0.92. For high grade gliomas, the results of meta-analysis showed that the sensitivity, specificity and DOR were 0.80 (95%CI 0.72 to 0.87), 0.67 (95%CI 0.53 to 0.79) and 7.20 (95%CI 3.04 to 17.09), respectively, and the AUC was 0.80. ConclusionIntra-operative ultrasound is useful for the diagnosis of tumor residue after resection of intracranial gliomas, especially for low grade gliomas.
Protein structure determines function, and structural information is critical for predicting protein thermostability. This study proposes a novel method for protein thermostability prediction by integrating graph embedding features and network topological features. By constructing residue interaction networks (RINs) to characterize protein structures, we calculated network topological features and utilize deep neural networks (DNN) to mine inherent characteristics. Using DeepWalk and Node2vec algorithms, we obtained node embeddings and extracted graph embedding features through a TopN strategy combined with bidirectional long short-term memory (BiLSTM) networks. Additionally, we introduced the Doc2vec algorithm to replace the Word2vec module in graph embedding algorithms, generating graph embedding feature vector encodings. By employing an attention mechanism to fuse graph embedding features with network topological features, we constructed a high-precision prediction model, achieving 87.85% prediction accuracy on a bacterial protein dataset. Furthermore, we analyzed the differences in the contributions of network topological features in the model and the differences among various graph embedding methods, and found that the combination of DeepWalk features with Doc2vec and all topological features was crucial for the identification of thermostable proteins. This study provides a practical and effective new method for protein thermostability prediction, and at the same time offers theoretical guidance for exploring protein diversity, discovering new thermostable proteins, and the intelligent modification of mesophilic proteins.