Roux-en-Y biliary reconstruction as a definitive treatment for serious complications of portal biliopathy. Case series.

INTRODUCTION AND IMPORTANCE: Portal biliopathy (PB) is an abnormality of the biliary tree wall due to extrahepatic portal hypertension. Among the complications of portal biliopathy are digestive bleeding, jaundice, and cholangitis. Surgical treatment is an exception when medical management is not possible. CASE PRESENTATION: This is a case series study of four patients with severe PB complications requiring surgical management in our center from 2005 to 2016. Two of them had previous surgical procedures related to portal hypertension. All presented with severe biliary stenosis and recurrent cholangitis, and two also had massive upper gastrointestinal bleeding. Because of endoscopic management failure, a Roux-en-Y hepaticojejunostomy was performed in all cases. Two patients presented morbidity Clavien-Dindo>IIIA, requiring reoperation. During follow-up, no one developed other complications related to PB. DISCUSSION: Surgical treatment for PB complications is a challenge and mainly implies a portosystemic shunt as a first step. When it fails, an alternative is perform a biliodigestive anastomoses, with high risk of bleeding given the prominent collaterals present in the hepatoduodenal pedicle secondary to portal cavernomatosis. CONCLUSION: Our patients after YRGB didn't present new complications due to PB. The surgery could be a definite solution for PB complications. It has only been made for selective cases because it implies high complexity and risk.

The Selective LAT1 Inhibitor JPH203 Enhances Mitochondrial Metabolism and Content in Insulin-Sensitive and Insulin-Resistant C2C12 Myotubes.

Population data have shown an association between higher circulating branched-chain amino acids (BCAA) and the severity of insulin resistance in people with diabetes. While several studies have assessed BCAA metabolism as a potential target for regulation, less attention has been paid to the role of L-type amino acid transporter 1 (LAT1), the primary transporter of BCAA in skeletal muscle. The aim of this study was to assess the impact of JPH203 (JPH), a LAT1 inhibitor, on myotube metabolism in both insulin-sensitive and insulin-resistant myotubes. C2C12 myotubes were treated with or without 1 µM or 2 µM JPH for 24 h with or without insulin resistance. Western blot and qRT-PCR were used to assess protein content and gene expression, respectively. Mitochondrial and glycolytic metabolism were measured via Seahorse Assay, and fluorescent staining was used to measure mitochondrial content. BCAA media content was quantified using liquid chromatography-mass spectrometry. JPH at 1 µM (but not 2 µM) increased mitochondrial metabolism and content without inducing changes in mRNA expression of transcripts associated with mitochondrial biogenesis or mitochondrial dynamics. Along with increased mitochondrial function, 1µM treatment also reduced extracellular leucine and valine. JPH at 2 µM reduced pAkt signaling and increased extracellular accumulation of isoleucine without inducing changes in BCAA metabolic genes. Collectively, JPH may increase mitochondrial function independent of the mitochondrial biogenic transcription pathway; however, high doses may reduce insulin signaling.

Customized normothermic machine perfusion decreases ischemia-reperfusion injury compared with static cold storage in a porcine model of liver transplantation.

BACKGROUND: Liver transplantation has been demonstrated to be the best treatment for several liver diseases, while grafts are limited. This has caused an increase in waiting lists, making it necessary to find ways to expand the number of organs available for transplantation. Normothermic perfusion (NMP) of liver grafts has been established as an alternative to static cold storage (SCS), but only a small number of perfusion machines are commercially available. METHODS: Using a customized ex situ machine perfusion, we compared the results between ex situ NMP and SCS preservation in a porcine liver transplant model. RESULTS: During NMP, lactate concentrations were 80% lower after the 3-h perfusion period, compared with SCS. Bile production had a 2.5-fold increase during the NMP period. After transplantation, aspartate transaminase (AST) and alanine transaminase (ALT) levels were 35% less in the NMP group, compared to the SCS group. In pathologic analyses of grafts after transplant, tissue oxidation did not change between groups, but the ischemia-reperfusion injury score was lower in the NMP group. CONCLUSION: NMP reduced hepatocellular damage and ischemia-reperfusion injury when compared to SCS using a customized perfusion machine. This could be an alternative for low-income countries to include machine perfusion in their therapeutic options.

Evolutionary optimisation of antibiotic dosing regimens for bacteria with different levels of resistance.

Antimicrobial resistance is one of the biggest threats to global health, food security, and development. Antibiotic overuse and misuse are the main drivers for the emergence of resistance. It is crucial to optimise the use of existing antibiotics in order to improve medical outcomes, decrease toxicity and reduce the emergence of resistance. We formulate the design of antibiotic dosing regimens as an optimisation problem, and use an evolutionary algorithm suited to continuous optimisation (differential evolution) to solve it. Regimens are represented as vectors of real numbers encoding daily doses, which can vary across the treatment duration. A stochastic mathematical model of bacterial infections with tuneable resistance levels is used to evaluate the effectiveness of evolved regimens. The objective is to minimise the treatment failure rate, subject to a constraint on the maximum total antibiotic used. We consider simulations with different levels of bacterial resistance, two ways of administering the drug (orally and intravenously), as well as coinfections with two strains of bacteria. Our approach produced effective dosing regimens, with an average improvement in lowering the failure rate 30%, when compared with standard fixed-daily-dose regimens with the same total amount of antibiotic.

Right Lobe Liver Donation After Bariatric Surgery. A Case Series of 4 Living Donors.

BACKGROUND: The increasing prevalence of obesity and need for bariatric surgery as well as the expanding use of living donors for liver transplantation means that potential donors could present with this surgical history. We present 4 cases of liver donors with previous bariatric surgery in our living donor liver transplant program. METHODS: A retrospective descriptive analysis of patients with a bariatric surgery history who underwent right hepatectomy in our living donor liver transplant program is presented. RESULTS: Case 1: A 53-year-old man with body mass index (BMI) of 33 who underwent laparoscopic sleeve gastrectomy (LSG). Pretransplant BMI was 21.5. Case 2: A 46-year-old woman with a BMI maximum of 40.8 who underwent LSG and required conversion to Roux-en-Y gastric bypass. Pretransplant BMI was 35.1. Case 3: A 53-year-old woman with a BMI maximum of 31.6 who underwent LSG. Pretransplant BMI was 24.2. Case 4: A 38-year-old man with a BMI maximum of 41.5 who underwent Roux-en-Y gastric bypass 6 years before the hepatectomy. Pretransplant BMI was 29.4. No complications were observed. Average operative time was 367.5 minutes, with a hospital stay of 5.8 days and 100% graft survival to date. DISCUSSION: Utilization of selected donors with previous bariatric surgery appears to be a safe option and increases the donor pool.

Implementation and design of customized ex vivo machine perfusion. Analysis of its first results.

The lack of organs available for transplantation is a global problem. The high mortality rates on the waiting list and the high number of discarded livers are reasons to develop new tools in the preservation and transplantation process. New tools should also be available for low-income countries. This article reports the development of customized normothermic machine perfusion (NMP). An ex vivo dual perfusion machine was designed, composed of a common reservoir organ box (CRO), a centrifugal pump (portal system, low pressure), and a roller pump (arterial system, high pressure). Porcine livers (n = 5) were perfused with an oxygenated normothermic (37℃) strategy for 3 hours. Hemodynamic variables, metabolic parameters, and bile production during preservation were analyzed. Arterial and portal flow remain stable during perfusion. Total bilirubin production was 11.25 mL (4-14.5) at 180 minutes. The median pH value reached 7.32 (7.25-7.4) at 180 minutes. Lactate values decreased progressively to normalization at 120 minutes. This perfusion setup was stable and able to maintain the metabolic activity of a liver graft in a porcine animal model. Design and initial results from this customized NMP are promising for a future clinical application in low-income countries.

Dynastic Potential Crossover Operator.

An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane is computationally costly, in general, requiring exponential time in the worst case. However, when the variable interaction graph of the objective function is sparse, exploration can be done in polynomial time. In this article, we present a recombination operator, called Dynastic Potential Crossover (DPX), that runs in polynomial time and behaves like an optimal recombination operator for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with traditional crossover operators, like uniform crossover and network crossover, and with two recently defined efficient recombination operators: partition crossover and articulation points partition crossover. The empirical comparison uses NKQ Landscapes and MAX-SAT instances. DPX outperforms the other crossover operators in terms of quality of the offspring and provides better results included in a trajectory and a population-based metaheuristic, but it requires more time and memory to compute the offspring.

Severe case of post cholecystectomy vasculobiliary injury successfully treated by right hepatectomy with a jump graft to the remaining left hepatic lobe.

Vasculobiliary injuries (VBI) caused by cholecystectomies are infrequent but extremely serious. We report a case of a severe VBI successfully treated at our center. A 22-year-old woman underwent an open cholecystectomy as treatment for acute cholecystitis and bile duct stones. She was transferred to our center on postoperative Day 4 because of progressive jaundice and encephalopathy. After a proper investigation, we found an extreme VBI with infarction of the right hepatic lobe associated with complete interruption of the portal vein and proper hepatic artery flows and full section of the common hepatic duct. Right hepatectomy with portal-Rex shunt revascularization of the left hepatic lobe and Roux-en-Y hepaticojejunostomy to the left hepatic duct was done. The patient was discharged on the 60th postoperative day. Discussion: This case shows the successful surgical treatment of a severe cholecystectomy's VBI, avoiding an emergency liver transplant.

Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times.

Mass production and use of antibiotics has led to the rise of resistant bacteria, a problem possibly exacerbated by inappropriate and non-optimal application. Antibiotic treatment often follows fixed-dose regimens, with a standard dose of antibiotic administered equally spaced in time. But are such fixed-dose regimens optimal or can alternative regimens be designed to increase efficacy? Yet, few mathematical models have aimed to identify optimal treatments based on biological data of infections inside a living host. In addition, assumptions to make the mathematical models analytically tractable limit the search space of possible treatment regimens (e.g. to fixed-dose treatments). Here, we aimed to address these limitations by using experiments in a Galleria mellonella (insect) model of bacterial infection to create a fully parametrised mathematical model of a systemic Vibrio infection. We successfully validated this model with biological experiments, including treatments unseen by the mathematical model. Then, by applying artificial intelligence, this model was used to determine optimal antibiotic dosage regimens to treat the host to maximise survival while minimising total antibiotic used. As expected, host survival increased as total quantity of antibiotic applied during the course of treatment increased. However, many of the optimal regimens tended to follow a large initial 'loading' dose followed by doses of incremental reductions in antibiotic quantity (dose 'tapering'). Moreover, application of the entire antibiotic in a single dose at the start of treatment was never optimal, except when the total quantity of antibiotic was very low. Importantly, the range of optimal regimens identified was broad enough to allow the antibiotic prescriber to choose a regimen based on additional criteria or preferences. Our findings demonstrate the utility of an insect host to model antibiotic therapies in vivo and the approach lays a foundation for future regimen optimisation for patient and societal benefits.

Inferring Future Landscapes: Sampling the Local Optima Level.

Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size compared to real-life situations. LON sampling algorithms are therefore important. In this article, we study LON construction algorithms for the Quadratic Assignment Problem (QAP). Using machine learning, we use estimated LON features to predict search performance for competitive heuristics used in the QAP domain. The results show that by using random forest regression, LON construction algorithms produce fitness landscape features which can explain almost all search variance. We find that LON samples better relate to search than enumerated LONs do. The importance of fitness levels of sampled LONs in search predictions is crystallised. Features from LONs produced by different algorithms are combined in predictions for the first time, with promising results for this "super-sampling": a model to predict tabu search success explained 99% of variance. Arguments are made for the use-case of each LON algorithm and for combining the exploitative process of one with the exploratory optimisation of the other.

Multi-objective evolutionary design of antibiotic treatments.

Antibiotic resistance is one of the major challenges we face in modern times. Antibiotic use, especially their overuse, is the single most important driver of antibiotic resistance. Efforts have been made to reduce unnecessary drug prescriptions, but limited work is devoted to optimising dosage regimes when they are prescribed. The design of antibiotic treatments can be formulated as an optimisation problem where candidate solutions are encoded as vectors of dosages per day. The formulation naturally gives rise to competing objectives, as we want to maximise the treatment effectiveness while minimising the total drug use, the treatment duration and the concentration of antibiotic experienced by the patient. This article combines a recent mathematical model of bacterial growth including both susceptible and resistant bacteria, with a multi-objective evolutionary algorithm in order to automatically design successful antibiotic treatments. We consider alternative formulations combining relevant objectives and constraints. Our approach obtains shorter treatments, with improved success rates and smaller amounts of drug than the standard practice of administering daily fixed doses. These new treatments consistently involve a higher initial dose followed by lower tapered doses.

A New Generalized Partition Crossover for the Traveling Salesman Problem: Tunneling between Local Optima.

Generalized Partition Crossover (GPX) is a deterministic recombination operator developed for the Traveling Salesman Problem. Partition crossover operators return the best of 2k reachable offspring, where k is the number of recombining components. This article introduces a new GPX2 operator, which finds more recombining components than GPX or Iterative Partial Transcription (IPT). We also show that GPX2 has O(n) runtime complexity, while also introducing new enhancements to reduce the execution time of GPX2. Finally, we experimentally demonstrate the efficiency of GPX2 when it is used to improve solutions found by the multitrial Lin-Kernighan-Helsgaum (LKH) algorithm. Significant improvements in performance are documented on large (n>5000) and very large (n=100,000) instances of the Traveling Salesman Problem.

Google Trends in Infodemiology and Infoveillance: Methodology Framework.

Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.

BACKGROUND: In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. OBJECTIVE: This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term "Google Trends" in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. RESULTS: All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. CONCLUSIONS: The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

Visualising the global structure of search landscapes: genetic improvement as a case study.

The search landscape is a common metaphor to describe the structure of computational search spaces. Different landscape metrics can be computed and used to predict search difficulty. Yet, the metaphor falls short in visualisation terms because it is hard to represent complex landscapes, both in terms of size and dimensionality. This paper combines local optima networks, as a compact representation of the global structure of a search space, and dimensionality reduction, using the t-distributed stochastic neighbour embedding algorithm, in order to both bring the metaphor to life and convey new insight into the search process. As a case study, two benchmark programs, under a genetic improvement bug-fixing scenario, are analysed and visualised using the proposed method. Local optima networks for both iterated local search and a hybrid genetic algorithm, across different neighbourhoods, are compared, highlighting the differences in how the landscape is explored.

Statistical Coupling Analysis-Guided Library Design for the Discovery of Mutant Luciferases.

Directed evolution has proven to be an invaluable tool for protein engineering; however, there is still a need for developing new approaches to continue to improve the efficiency and efficacy of these methods. Here, we demonstrate a new method for library design that applies a previously developed bioinformatic method, Statistical Coupling Analysis (SCA). SCA uses homologous enzymes to identify amino acid positions that are mutable and functionally important and engage in synergistic interactions between amino acids. We use SCA to guide a library of the protein luciferase and demonstrate that, in a single round of selection, we can identify luciferase mutants with several valuable properties. Specifically, we identify luciferase mutants that possess both red-shifted emission spectra and improved stability relative to those of the wild-type enzyme. We also identify luciferase mutants that possess a >50-fold change in specificity for modified luciferins. To understand the mutational origin of these improved mutants, we demonstrate the role of mutations at N229, S239, and G246 in altered function. These studies show that SCA can be used to guide library design and rapidly identify synergistic amino acid mutations from a small library.

Optimising Antibiotic Usage to Treat Bacterial Infections.

The increase in antibiotic resistant bacteria poses a threat to the continued use of antibiotics to treat bacterial infections. The overuse and misuse of antibiotics has been identified as a significant driver in the emergence of resistance. Finding optimal treatment regimens is therefore critical in ensuring the prolonged effectiveness of these antibiotics. This study uses mathematical modelling to analyse the effect traditional treatment regimens have on the dynamics of a bacterial infection. Using a novel approach, a genetic algorithm, the study then identifies improved treatment regimens. Using a single antibiotic the genetic algorithm identifies regimens which minimise the amount of antibiotic used while maximising bacterial eradication. Although exact treatments are highly dependent on parameter values and initial bacterial load, a significant common trend is identified throughout the results. A treatment regimen consisting of a high initial dose followed by an extended tapering of doses is found to optimise the use of antibiotics. This consistently improves the success of eradicating infections, uses less antibiotic than traditional regimens and reduces the time to eradication. The use of genetic algorithms to optimise treatment regimens enables an extensive search of possible regimens, with previous regimens directing the search into regions of better performance.

Estado Nutricional y Hábitos del Estilo de Vida en Preescolares de los Centros Infantiles en Cuenca ­ Ecuador / Nutritional Status and Lifestyle Habits in Preschoolers of Children's Centers in Cuenca - Ecuador

OBJETIVO: Describir el estado nutricional y los hábitos del estilo de vida en preescolares de 2 a 4 años de los Centros Infantiles del Buen Vivir del Ministerio de Inclusión Económica y Social (MIES) en Cuenca ­ Ecuador. METODOLOGÍA: Estudio descriptivo de corte transversal. Se evaluó el estado nutricional en 156 niños/as de acuerdo al criterio de la Organización Mundial de la Salud (OMS) 2007. Se realizó una encuesta que incluyó preguntas sobre tipo de cuidador, tipo de familia, estrato social, hábitos de alimentación, horas frente a una pantalla. RESULTADOS: El 26.9% de los niños/as presentó baja talla/edad y 72.4% talla normal. En el indicador peso/edad 0.6% presentó bajo peso y 1.2% sobrepeso y obesidad. En el indicador índice de masa corporal/edad ningún niño/a presentó emaciación, se observó 35.9% con riesgo de sobrepeso y 8.3% con sobrepeso/obesidad. CONCLUSIONES: Las prevalencias de desnutrición crónica y de exceso de peso son altas, características de un país que atraviesa una transición nutricional.

OBJECTIVE: To describe the nutritional status and lifes-tyle habits in preschool children from 2 to 4 years in Chil-dren's Centers of Good Living in the Ministry of Econo-mic and Social Inclusion (MIES) in Cuenca - Ecuador.METHODOLOGY: It is a descriptive cross-sectional study. The nutritional status of 156 children was evaluated ac-cording to the criteria of the World Health Organization (WHO) 2007. A survey that included questions about ca-regiver type, type of family, social stratum, eating ha-bits, and hours in front a screen was performed.RESULTS: The 26.9% of children presented low height / age and 72.4% normal height. In the weight / age in-dicator showed about 0.6% underweight and 1.2% overweight and obesity. In the body mass index indica-tor / age no child presented emaciation, a 35.9% at risk of overweight and 8.3% overweight / obesity was ob-served.CONCLUSIONS: The prevalence of chronic malnutrition and overweight are high, which are characteristics of a country experiencing a nutrition transition.