ABSTRACT
@#Objective To systematically evaluate the clinical efficacy and adverse reactions of paclitaxel and carboplatin with or without bevacizumab in the treatment of non-small cell lung cancer (NSCLC). Methods The databases including PubMed, The Cochrane Library, EMbase, CNKI, Wanfang Data, VIP and CBM were searched from inception to October 2022 to collect randomized controlled trials of the clinical efficacy of paclitaxel and carboplatin with or without bevacizumab for the treatment of NSCLC. RevMan 5.4 software was used for meta-analysis. Results Eight randomized controlled trials were enrolled, involving a total of 1 724 patients. Meta-analysis showed that for the treatment of NSCLC, the disease control rate, overall response rate, 1-year survival rate, and 2-year survival rate were higher in the trial group (paclitaxel and carboplatin combined with bevacizumab) than those in the control group (paclitaxel and carboplatin) (P<0.05); however, the incidences of the adverse reactions, such as leukopenia, hemorrhage, proteinuria and hypertension, etc, were higher in the trial group than those in the control group (P<0.05). There were no statistical differences between the trial group and the control group in the incidences of fatigue, thrombocytopenia, neutropenia or hyponatremia, etc (P>0.05). In addition, the median progression-free survival and overall survival were longer in the trial group than those in the control group. Conclusion For the treatment of NSCLC, paclitaxel and carboplatin combined with bevacizumab is superior in terms of disease control, overall response and prolonging patient survival, etc, but will be associated with more adverse reactions.
ABSTRACT
Protein nitration and nitrosylation are essential post-translational modifications (PTMs) involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases. Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65 for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively, demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%-42% improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http://deepnitro.renlab.org.