Prevention of leakage by sealing colon anastomosis: experimental study in a mouse model

Background: In colorectal surgery, anastomotic leakage (AL) is the most significant complication. Sealants applied around the colon anastomosis may help prevent AL by giving the anastomosis time to heal by mechanically supporting the anastomosis and preventing bacteria leaking into the peritoneal cavity. The aim of this study is to compare commercially available sealants on their efficacy of preventing leakage in a validated mouse model for AL. Methods: Six sealants (Evicel, Omnex, VascuSeal, PleuraSeal, BioGlue, and Colle Chirurgicale Cardial) were applied around an anastomosis constructed with five interrupted sutures in mice, and compared with a control group without sealant. Outcome measures were AL, anastomotic bursting pressure, and death. Results: In the control group there was a 40% death rate with a 50% rate of AL. None of the sealants were able to diminish the rate of AL. Furthermore, use of the majority of sealants resulted in failure to thrive, increased rates of ileus, and higher mortality rates. Conclusions: If sealing of a colorectal anastomosis could achieve a reduction of incidence of clinical AL, this would be a promising tool for prevention of leakage in colorectal surgery. In this study, we found no evidence that sealants reduce leakage rates in a mouse model for AL. However, the negative results of this study make us emphasize the need of systemic research, investigating histologic tissue reaction of the bowel to different sealants, the capacity of sealants to form a watertight

The Role of Energy Innovation and Corruption in Carbon Emissions: Evidence Based on the EKC Hypothesis

This study investigates how energy innovations and corruption affect carbon emissions. To this end, a panel data model of 16 selected OECD countries is employed, spanning the period of 1995–2016. The empirical framework falls within the hypothesis of the environmental Kuznets curve (EKC), which explores the relationship between the economic growth and carbon emissions. The empirical results show that when economic systems interact with corruption, positive effects that energy innovations have on environmental quality are reduced. Furthermore, the amount of economic growth needed to limit environmental pollution levels is also distorted. Corruption seems to be pernicious for the environment in the long term, as it limits the stage at which decontamination occurs; i.e., corruption reduces the positive effect generated by measures focused on energy innovation in terms of reducing environmental pollution. These findings are expected to be significant in terms of implementing anti-corruption measures and effective environmental policies, and they call for appropriate policy measures that might limit the effects of corruption on environmental quality. © 2019, Springer Nature Switzerland AG

Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score

To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients