Presentation and management of keloid scarring following median sternotomy: a case study

<p>Abstract</p> <p>Introduction</p> <p>Keloid scars following median sternotomy are rare and occur more frequently in pigmented skin. Different management strategies have been described with variable success. We present a case of keloid scar formation following cardiac surgery including our management and the final aesthetic result.</p> <p>Case description</p> <p>A 64 year old female of fair complexion underwent mitral valve replacement. The procedure and postoperative recovery were uncomplicated, however, during the following year, thick keloid scars formed over the incision sites. Initial non surgical measures failed to relieve pain and did not offer any tangible aesthetic benefit. Eventually surgical excision was attempted. She presented to our clinic for nine months follow up with significant improvement in pain and aesthetic result.</p> <p>Discussion and Evaluation</p> <p>Several theories have attempted to explore the pathophysiology of keloid scar formation. A number of predisposing factors have been documented however none existed in this case. A variety of invasive and non invasive approaches have been described but significant differences in success rates and methodology of investigations still precludes a standardized management protocol.</p> <p>Conclusions</p> <p>In this case study a rare presentation of keloid scar has been presented. The variety of methods used to improve pain and aesthetic result demonstrates the propensity of keloid scars to recur and the therapeutic challenges that surgeons have to face in their quest for a satisfactory patient outcome.</p

Protective intraoperative ventilation with higher versus lower levels of positive end-expiratory pressure in obese patients (PROBESE): study protocol for a randomized controlled trial

Background: Postoperative pulmonary complications (PPCs) increase the morbidity and mortality of surgery in obese patients. High levels of positive end-expiratory pressure (PEEP) with lung recruitment maneuvers may improve intraoperative respiratory function, but they can also compromise hemodynamics, and the effects on PPCs are uncertain. We hypothesized that intraoperative mechanical ventilation using high PEEP with periodic recruitment maneuvers, as compared with low PEEP without recruitment maneuvers, prevents PPCs in obese patients. Methods/design The PRotective Ventilation with Higher versus Lower PEEP during General Anesthesia for Surgery in OBESE Patients (PROBESE) study is a multicenter, two-arm, international randomized controlled trial. In total, 2013 obese patients with body mass index ≥35 kg/m2 scheduled for at least 2 h of surgery under general anesthesia and at intermediate to high risk for PPCs will be included. Patients are ventilated intraoperatively with a low tidal volume of 7 ml/kg (predicted body weight) and randomly assigned to PEEP of 12 cmH2O with lung recruitment maneuvers (high PEEP) or PEEP of 4 cmH2O without recruitment maneuvers (low PEEP). The occurrence of PPCs will be recorded as collapsed composite of single adverse pulmonary events and represents the primary endpoint. Discussion To our knowledge, the PROBESE trial is the first multicenter, international randomized controlled trial to compare the effects of two different levels of intraoperative PEEP during protective low tidal volume ventilation on PPCs in obese patients. The results of the PROBESE trial will support anesthesiologists in their decision to choose a certain PEEP level during general anesthesia for surgery in obese patients in an attempt to prevent PPCs. Trial registration ClinicalTrials.gov identifier: NCT02148692. Registered on 23 May 2014; last updated 7 June 2016. Electronic supplementary material The online version of this article (doi:10.1186/s13063-017-1929-0) contains supplementary material, which is available to authorized users

Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization

Limbo (LIbrary for Model-Based Optimization) is an open-source C++11 library for Gaussian Processes and data-efficient optimization (e.g., Bayesian optimization) that is designed to be both highly flexible and very fast. It can be used as a state-of-the-art optimization library or to experiment with novel algorithms with “plugin” components. Limbo is currently mostly used for data-efficient policy search in robot learning and online adaptation because computation time matters when using the low-power embedded computers of robots. For example, Limbo was the key library to develop a new algorithm that allows a legged robot to learn a new gait after a mechanical damage in about 10-15 trials (2 minutes), and a 4-DOF manipulator to learn neural networks policies for goal reaching in about 5 trials. The implementation of Limbo follows a policy-based design that leverages C++ templates: this allows it to be highly flexible without the cost induced by classic object-oriented designs (cost of virtual functions). The regression benchmarks show that the query time of Limbo’s Gaussian processes is several orders of magnitude better than the one of GPy (a state-of-the-art Python library for Gaussian processes) for a similar accuracy (the learning time highly depends on the optimization algorithm chosen to optimize the hyper-parameters). The black-box optimization benchmarks demonstrate that Limbo is about 2 times faster than BayesOpt (a C++ library for data-efficient optimization) for a similar accuracy and data-efficiency. In practice, changing one of the components of the algorithms in Limbo (e.g., changing the acquisition function) usually requires changing only a template definition in the source code. This design allows users to rapidly experiment and test new ideas while keeping the software as fast as specialized code. Limbo takes advantage of multi-core architectures to parallelize the internal optimization processes (optimization of the acquisition function, optimization of the hyper-parameters of a Gaussian process) and it vectorizes many of the linear algebra operations (via the Eigen 3 library and optional bindings to Intel’s MKL). The library is distributed under the CeCILL-C license via a Github repository. The code is standard-compliant but it is currently mostly developed for GNU/Linux and Mac OS X with both the GCC and Clang compilers. New contributors can rely on a full API reference, while their developments are checked via a continuous integration platform (automatic unit-testing routines). Limbo is currently used in the ERC project ResiBots, which is focused on data-efficient trial-and-error learning for robot damage recovery, and in the H2020 projet PAL, which uses social robots to help coping with diabetes. It has been instrumental in many scientific publications since 2015Limbo (LIbrary for Model-Based Optimization) is an open-source C++11 library for Gaussian Processes and data-efficient optimization (e.g., Bayesian optimization) that is designed to be both highly flexible and very fast. It can be used as a state-of-the-art optimization library or to experiment with novel algorithms with “plugin” components. Limbo is currently mostly used for data-efficient policy search in robot learning and online adaptation because computation time matters when using the low-power embedded computers of robots. For example, Limbo was the key library to develop a new algorithm that allows a legged robot to learn a new gait after a mechanical damage in about 10-15 trials (2 minutes), and a 4-DOF manipulator to learn neural networks policies for goal reaching in about 5 trials. The implementation of Limbo follows a policy-based design that leverages C++ templates: this allows it to be highly flexible without the cost induced by classic object-oriented designs (cost of virtual functions). The regression benchmarks show that the query time of Limbo’s Gaussian processes is several orders of magnitude better than the one of GPy (a state-of-the-art Python library for Gaussian processes) for a similar accuracy (the learning time highly depends on the optimization algorithm chosen to optimize the hyper-parameters). The black-box optimization benchmarks demonstrate that Limbo is about 2 times faster than BayesOpt (a C++ library for data-efficient optimization) for a similar accuracy and data-efficiency. In practice, changing one of the components of the algorithms in Limbo (e.g., changing the acquisition function) usually requires changing only a template definition in the source code. This design allows users to rapidly experiment and test new ideas while keeping the software as fast as specialized code. Limbo takes advantage of multi-core architectures to parallelize the internal optimization processes (optimization of the acquisition function, optimization of the hyper-parameters of a Gaussian process) and it vectorizes many of the linear algebra operations (via the Eigen 3 library and optional bindings to Intel’s MKL). The library is distributed under the CeCILL-C license via a Github repository. The code is standard-compliant but it is currently mostly developed for GNU/Linux and Mac OS X with both the GCC and Clang compilers. New contributors can rely on a full API reference, while their developments are checked via a continuous integration platform (automatic unit-testing routines). Limbo is currently used in the ERC project ResiBots, which is focused on data-efficient trial-and-error learning for robot damage recovery, and in the H2020 projet PAL, which uses social robots to help coping with diabetes. It has been instrumental in many scientific publications since 201

A data-driven method for reconstructing and modelling social interactions in moving animal groups

International audienceGroup-living organisms that collectively migrate range from cells and bacteria to human crowds, and include swarms of insects, schools of fish and flocks of birds or ungulates. Unveiling the behavioural and cognitive mechanisms by which these groups coordinatetheir movements is a challenging task. These mechanisms take place at the individual scale and they can be described as a combination ofpairwise interactions between individuals and interactions between these individuals and the physical obstacles in the environment.Thanks to the development of novel tracking techniques that provide large and accurate data sets, the main characteristics of indivi\-dual and collective behavioural patterns can be quantified with an unprecedented level of precision. However, in a large number of works, social interactions are usually described by force map methods that only have a limited capacity of explanation and prediction, being rarely suitable for a direct implementation in a concise and explicit mathematical model. Here, we present a general method to extract the interactions between individuals that are involved in the coordination of collective movements in groups of organisms. We then apply this method to characterize social interactions in two species of shoaling fish, the rummy-nose tetra (Hemigrammus rhodostomus) and the zebrafish (Danio rerio),which both present a burst-and-coast motion. The detailed quantitative description of microscopic individual-level interactions thus provides predictive models of the emergent dynamics observed at the macroscopic group-level. This method can be applied to a wide range of biological and social systems

Protective intraoperative ventilation with higher versus lower levels of positive end-expiratory pressure in obese patients (PROBESE): Study protocol for a randomized controlled trial

Background: Postoperative pulmonary complications (PPCs) increase the morbidity and mortality of surgery in obese patients. High levels of positive end-expiratory pressure (PEEP) with lung recruitment maneuvers may improve intraoperative respiratory function, but they can also compromise hemodynamics, and the effects on PPCs are uncertain. We hypothesized that intraoperative mechanical ventilation using high PEEP with periodic recruitment maneuvers, as compared with low PEEP without recruitment maneuvers, prevents PPCs in obese patients. Methods/design: The PRotective Ventilation with Higher versus Lower PEEP during General Anesthesia for Surgery in OBESE Patients (PROBESE) study is a multicenter, two-arm, international randomized controlled trial. In total, 2013 obese patients with body mass index ≥35 kg/m2 scheduled for at least 2 h of surgery under general anesthesia and at intermediate to high risk for PPCs will be included. Patients are ventilated intraoperatively with a low tidal volume of 7 ml/kg (predicted body weight) and randomly assigned to PEEP of 12 cmH2O with lung recruitment maneuvers (high PEEP) or PEEP of 4 cmH2O without recruitment maneuvers (low PEEP). The occurrence of PPCs will be recorded as collapsed composite of single adverse pulmonary events and represents the primary endpoint. Discussion: To our knowledge, the PROBESE trial is the first multicenter, international randomized controlled trial to compare the effects of two different levels of intraoperative PEEP during protective low tidal volume ventilation on PPCs in obese patients. The results of the PROBESE trial will support anesthesiologists in their decision to choose a certain PEEP level during general anesthesia for surgery in obese patients in an attempt to prevent PPCs. Trial registration: ClinicalTrials.gov identifier: NCT02148692. Registered on 23 May 2014; last updated 7 June 2016

Intraoperative transfusion practices in Europe

BACKGROUND: Transfusion of allogeneic blood influences outcome after surgery. Despite widespread availability of transfusion guidelines, transfusion practices might vary among physicians, departments, hospitals and countries. Our aim was to determine the amount of packed red blood cells (pRBC) and blood products transfused intraoperatively, and to describe factors determining transfusion throughout Europe. METHODS: We did a prospective observational cohort study enrolling 5803 patients in 126 European centres that received at least one pRBC unit intraoperatively, during a continuous three month period in 2013. RESULTS: The overall intraoperative transfusion rate was 1.8%; 59% of transfusions were at least partially initiated as a result of a physiological transfusion trigger- mostly because of hypotension (55.4%) and/or tachycardia (30.7%). Haemoglobin (Hb)- based transfusion trigger alone initiated only 8.5% of transfusions. The Hb concentration [mean (sd)] just before transfusion was 8.1 (1.7) g dl(-1) and increased to 9.8 (1.8) g dl(-1) after transfusion. The mean number of intraoperatively transfused pRBC units was 2.5 (2.7) units (median 2). CONCLUSION: Although European Society of Anaesthesiology transfusion guidelines are moderately implemented in Europe with respect to Hb threshold for transfusion (7-9 g dl(-1)), there is still an urgent need for further educational efforts that focus on the number of pRBC units to be transfused at this threshold. CLINICAL TRIAL REGISTRATION: NCT 01604083