The aim of this meta-analysis was to evaluate whether adoption of DaVinci Xi
The Pubmed, CINAHL, Cochrane Library and MEDLINE (Ovid) databases were systematically searched. Operating time as well as docking and surgeon console times were the primary endpoints. Conversion and postoperative complication rates were the secondary endpoints.
Six studies totaling 610 patients (320 Si and 290 Xi) were included. Total operating time [MD (95% CI) = 30.553 (15.071, 46.035); p < 0.001], docking time [MD (95% CI) = 4.178 (2.120, 6.235); p < 0.001] and surgeon console time [MD (95% CI) = 17.246 (-0.479, 34.971); p = 0.056] were longer in DaVinci Si
This meta-analysis found that the adoption of DaVinci Xi

A meta-analysis of DaVinci Si versus Xi in colorectal surgery.

Aggregation across multiple networks highlights robust co-expression interactions and improves the functional connectivity of grapevine gene co-expression networks. In recent years, the rapid accumulation of transcriptome datasets from diverse experimental conditions has enabled the widespread use of gene co-expression network (GCN) analysis in plants. In grapevine, GCN analysis has shown great promise for gene function prediction, however, measurable progress is currently lacking. Using accumulated microarray datasets from the grapevine whole-genome array (33 experiments, 1359 samples), we explored how meta-analysis through aggregation influences the functional connectivity (performance) of derived networks using guilt-by-association neighbor voting. Two annotation schemes, i.e. MapMan BIN and Pfam, at two sparsity thresholds, i.e. top 100 (stringent) and 300 (relaxed) ranked genes were evaluated. We observed that aggregating across multiple networks improves performance dramatically, with the aggregate outperforming the majority of functional terms across individual networks. Network sparsity and size (i.e. the number of samples and aggregates) were key factors influencing performance while the choice of annotation scheme had little. Systematic comparison with various state-of-the-art microarray and RNA-seq networks was also performed, however, none outperformed the aggregate microarray network despite having good predictive performance. Repeating these series of tests using a functional enrichment-based performance metric also showed remarkably consistent findings with guilt-by-association neighbor voting. To demonstrate its functionality, we explore the function and transcriptional regulation of grapevine EXPANSIN genes. We envisage that network aggregation will offer new and unique opportunities for gene function prediction in future grapevine functional genomics studies. To this end, we make the aggregate networks and associated metadata publicly available at VTC-Agg (https://sites.google.com/view/vtc-agg).

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