128 research outputs found

    Specific synbiotics in early life protect against diet-induced obesity in adult mice

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    Aims: The metabolic state of human adults is associated with their gut microbiome. The symbiosis between host and microbiome is initiated at birth, and early life microbiome perturbation can disturb health throughout life. Here, we determined how beneficial microbiome interventions in early life affect metabolic health in adulthood. Methods: Postnatal diets were supplemented with either prebiotics (scGOS/lcFOS) or synbiotics (scGOS/lcFOS with Bifidobacterium breve M-16V) until post-natal (PN) day 42 in a well-established rodent model for nutritional programming. Mice were subsequently challenged with a high-fat Western-style diet (WSD) for 8 weeks. Body weight and composition were monitored, as was gut microbiota composition at PN21, 42 and 98. Markers of glucose homeostasis, lipid metabolism and host transcriptomics of 6 target tissues were determined in adulthood (PN98). Results: Early life synbiotics protected mice against WSD-induced excessive fat accumulation throughout life, replicable in 2 independent European animal facilities. Adult insulin sensitivity and dyslipidaemia were improved and most pronounced changes in gene expression were observed in the ileum. We observed subtle changes in faecal microbiota composition, both in early life and in adulthood, including increased abundance of Bifidobacterium. Microbiota transplantation using samples collected from synbiotics-supplemented adolescent mice at PN42 to age-matched germ-free recipients did not transfer the beneficial phenotype, indicating that synbiotics-modified microbiota at PN42 is not sufficient to transfer long-lasting protection of metabolic health status. Conclusion: Together, these findings show the potential and importance of timing of synbiotic interventions in early life during crucial microbiota development as a preventive measure to lower the risk of obesity and improve metabolic health throughout life

    Semi-automated computed tomography Volumetry can predict hemihepatectomy specimens’ volumes in patients with hepatic malignancy

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    Background: One of the major causes of perioperative mortality of patients undergoing major hepatic resections is post-hepatectomy liver failure (PHLF). For preoperative appraisal of the risk of PHLF it is important to accurately predict resectate volume and future liver remnant volume (FLRV). The objective of our study is to prospectively evaluate the accuracy of hemihepatectomy resectate volumes that are determined by computed tomography volumetry (CTV) when compared with intraoperatively measured volumes and weights as gold standard in patients undergoing hemihepatectomy. Methods: Twenty four patients (13 women, 11 men) scheduled for hemihepatectomy due to histologically proven primary or secondary hepatic malignancies were included in our study. CTV was performed using a semi-automated module (S, hereinafter) (syngo.CT Liver Analysis VA30, Siemens Healthcare, Germany). Conversion factors between CT volumes on the one side and intraoperative volumes and weights on the other side were calculated using the method of least squares. Absolute and relative disagreements between CT volumes and intraoperative volumes were determined. Results: A conversion factor of c = 0.906 most precisely predicted intraoperative volumes of exsanguinated hemihepatectomy specimens from CT volumes in all patients with mean absolute and relative disagreements between CT volumes and intraoperative volumes of 57 ml and 6.3%. The use of operation-specific conversion factors yielded even better results. Conclusions: CTV performed with S accurately predicts intraoperative volumes of hemihepatectomy specimens when applying conversion factors which compensate for exsanguination. This allows to precisely estimate the FLRV and thus minimize the risk of PHLF in patients undergoing major hepatic resections

    Agents intervening against delirium in the intensive care unit trial-Protocol for a secondary Bayesian analysis

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    Background Delirium is highly prevalent in the intensive care unit (ICU) and is associated with high morbidity and mortality. The antipsychotic haloperidol is the most frequently used agent to treat delirium although this is not supported by solid evidence. The agents intervening against delirium in the intensive care unit (AID-ICU) trial investigates the effects of haloperidol versus placebo for the treatment of delirium in adult ICU patients. Methods This protocol describes the secondary, pre-planned Bayesian analyses of the primary and secondary outcomes up to day 90 of the AID-ICU trial. We will use Bayesian linear regression models for all count outcomes and Bayesian logistic regression models for all dichotomous outcomes. We will adjust for stratification variables (site and delirium subtype) and use weakly informative priors supplemented with sensitivity analyses using sceptical priors. We will present results as absolute differences (mean differences and risk differences) and relative differences (ratios of means and relative risks). Posteriors will be summarised using median values as point estimates and percentile-based 95% credibility intervals. Probabilities of any benefit/harm, clinically important benefit/harm and clinically unimportant differences will be presented for all outcomes. Discussion The results of this secondary, pre-planned Bayesian analysis will complement the primary frequentist analysis of the AID-ICU trial and facilitate a nuanced and probabilistic interpretation of the trial results.Peer reviewe
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