Lipopolysaccharide (LPS),

Aims: This study systematically reviews the literature regarding preoperative stoma site marking and discusses the effectiveness of the procedure on complication rates, self-care deficits and health-related quality of life (HRQOL).Design: Systematic review and meta-analysis.Data source: Our review was conducted following the PRISMA guidelines. PubMed, EMBASE, Cochrane and CINAHL databases were searched to obtain articles published in English. Articles were also retrieved from Korean databases as well. Our last search was conducted on 2 June 2019.Review methods: Two reviewers independently selected relevant studies, evaluated their methodological quality and extracted data. Experimental and observational studies were included. Our main focus was on complication rates, self-care deficits and HRQOL. We conducted meta-analysis using the statistical software spss 25.0 and Stata 13.0. Results:Of the 1,039 articles reviewed, 20 were included for review, and 19 were used for quantitative synthesis. Preoperative stoma site marking reduced complication rates (odds ratio [OR]: 0.47; 95% confidence interval [CI]: 0.36-0.62; I 2 : 70.6%), lowered self-care deficits (OR: 0.34; 95% CI: 0.18-0.64; I 2 : 0%), and increased HRQOL (standardized mean difference, 1.05; 95% CI: 0.70-1.40; I 2 : 0%). Quality appraisal results for both the individual studies and the studies overall were excellent. The possibility of publication bias was low. Conclusions:Our findings indicate that preoperative stoma site marking improves patient outcomes: stoma-related complication rates and self-care deficits decrease and HRQOL rises. For this reason, preoperative stoma site marking should be a mandatory procedure in clinical settings. The practice should also be supported by policymakers and healthcare expert associations. Impact: Preoperative stoma site marking reduces overall complication rates by 53% and skin problems by 59%. Preoperative stoma site marking also improves self-care and health-related quality of life. We recommend that preoperative stoma site marking should be a mandatory procedure in clinical settings.

The effects of antibiotics on environment-originated nonpathogenic Acinetobacter species have been poorly explored. To understand the antibiotic-resistance mechanisms that function in nonpathogenic Acinetobacter species, we used an RNA-sequencing (RNA-seq) technique to perform global gene-expression profiling of soil-borne Acinetobacter oleivorans DR1 after exposing the bacteria to 4 classes of antibiotics (ampicillin, Amp; kanamycin, Km; tetracycline, Tc; norfloxacin, Nor). Interestingly, the well-known two global regulators, the soxR and the rpoE genes are present among 41 commonly upregulated genes under all 4 antibiotic-treatment conditions. We speculate that these common genes are essential for antibiotic resistance in DR1. Treatment with the 4 antibiotics produced diverse physiological and phenotypic changes. Km treatment induced the most dramatic phenotypic changes. Examination of mutation frequency and DNA-repair capability demonstrated the induction of the SOS response in Acinetobacter especially under Nor treatment. Based on the RNA-seq analysis, the glyoxylate-bypass genes of the citrate cycle were specifically upregulated under Amp treatment. We also identified newly recognized non-coding small RNAs of the DR1 strain, which were also confirmed by Northern blot analysis. These results reveal that treatment with antibiotics of distinct classes differentially affected the gene expression and physiology of DR1 cells. This study expands our understanding of the molecular mechanisms of antibiotic-stress response of environment-originated bacteria and provides a basis for future investigations.

BackgroundIdentifying gene regulatory networks is an important task for understanding biological systems. Time-course measurement data became a valuable resource for inferring gene regulatory networks. Various methods have been presented for reconstructing the networks from time-course measurement data. However, existing methods have been validated on only a limited number of benchmark datasets, and rarely verified on real biological systems.ResultsWe first integrated benchmark time-course gene expression datasets from previous studies and reassessed the baseline methods. We observed that GENIE3-time, a tree-based ensemble method, achieved the best performance among the baselines. In this study, we introduce BTNET, a boosted tree based gene regulatory network inference algorithm which improves the state-of-the-art. We quantitatively validated BTNET on the integrated benchmark dataset. The AUROC and AUPR scores of BTNET were higher than those of the baselines. We also qualitatively validated the results of BTNET through an experiment on neuroblastoma cells treated with an antidepressant. The inferred regulatory network from BTNET showed that brachyury, a transcription factor, was regulated by fluoxetine, an antidepressant, which was verified by the expression of its downstream genes.ConclusionsWe present BTENT that infers a GRN from time-course measurement data using boosting algorithms. Our model achieved the highest AUROC and AUPR scores on the integrated benchmark dataset. We further validated BTNET qualitatively through a wet-lab experiment and showed that BTNET can produce biologically meaningful results.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0547-0) contains supplementary material, which is available to authorized users.