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Objective:To systematically evaluate the efficacy of different kinds of smoking cessation drugs by network Meta-analysis.Methods:Literature was retrieved from PubMed, Web of Science, Embase, Cochrane Library, CBM, CNKI, VIP, Wan fang database, from the establishment of the database to November 2022, and randomized controlled trials (RCT) about bupropion, varenicline, nicotine replacement therapy (NRT) versus placebo in the treatment of smoking patients were collected. After data extraction from included literature which met inclusion criteria, and quality evaluation with Cochrane 5.1 risk bias evaluation tool, network Meta-analysis was performed by Stata15.1 software.Results:A total of 19 RCTs, involving 6106 patients and three interventions measures (bupropion, varenicline, NRT) and one control measure (placebo) were included. The results of network Meta-analysis showed that in terms of short-term abstinence rate, varenicline [ OR=4.21, 95% CI (2.32, 7.63)], bupropion [ OR=2.81, 95% CI(1.05, 7.54)] were better than placebo ( P<0.05). The surface under the cumulative ranking area (SUCRA): varenicline (90.2%)>bupropion (64.8%)>NRT (41.7%)>placebo (3.2%). In terms of the long-term abstinence rate, varenicline [ OR=3.06, 95% CI (1.59, 5.90)], NRT [ OR=3.39, 95% CI (2.20, 5.21)] were better than placebo ( P<0.05). SUCRA: varenicline (83.8%)>NRT (73.9%)>bupropion (37.2%)>placebo (5.2%). Conclusion:The existing evidence shows that compared with bupropion, NRT, varenicline has the best effect on quitting smoking, but more high-quality randomized trial evidence is needed for verification.
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Lung cancer is one of the most harmful global diseases with high morbidity and mortality rate. As a unique kind of driver gene, the pathogenic fusion gene is a common mechanism of lung cancer. Most fusion genes are produced by chromosome rearrangement and encoding receptor tyrosine kinases, which could be potential lung cancer therapeutic targets. Since ALK was first identified in 2007, methods like FISH, IHC, RT-PCR and NGS have been intensively applied, leading to identification of multiple other lung cancer fusion genes including ROS1, RET, FGFR, NTRK1, NRG1, DNAH5 and LTK. These works broaden the spectrum of lung cancer related gene mutations, and support the customized treatment for clinical patients. For some fusion genes, corresponding kinase inhibitors have been developed with good efficacy, however, the treatment is still being challenged by several problems like drug resistance. Based on recent studies, the research development of lung cancer fusion genes will be discussed.
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The rapid development of high-throughput sequencing technologies has led to a dramatic decrease in the money and time required for de novo genome sequencing or genome resequencing projects, with new genome sequences constantly released every week. Among such projects, the plethora of updated genome assemblies induces the requirement of version-dependent annotation files and other compatible public dataset for downstream analysis. To handle these tasks in an efficient manner, we developed the reference-based genome assembly and annotation tool (RGAAT), a flexible toolkit for resequencing-based consensus building and annotation update. RGAAT can detect sequence variants with comparable precision, specificity, and sensitivity to GATK and with higher precision and specificity than Freebayes and SAMtools on four DNA-seq datasets tested in this study. RGAAT can also identify sequence variants based on cross-cultivar or cross-version genomic alignments. Unlike GATK and SAMtools/BCFtools, RGAAT builds the consensus sequence by taking into account the true allele frequency. Finally, RGAAT generates a coordinate conversion file between the reference and query genomes using sequence variants and supports annotation file transfer. Compared to the rapid annotation transfer tool (RATT), RGAAT displays better performance characteristics for annotation transfer between different genome assemblies, strains, and species. In addition, RGAAT can be used for genome modification, genome comparison, and coordinate conversion. RGAAT is available at https://sourceforge.net/projects/rgaat/ and https://github.com/wushyer/RGAAT_v2 at no cost.