Evaluation and Enhancement of an Intraoperative Insulin Infusion Protocol via In-Silico Simulation

Intraoperative glycemic control, particularly in cardiac surgical patients, remains challenging. Patients with impaired insulin sensitivity and/or secretion (i.e., type 1 diabetes mellitus) often manifest extremely labile blood glucose measurements during periods of stress and inflammation. Most current insulin infusion protocols are developed based on clinical experiences and consensus among a local group of physicians. Recent advances in human glucose metabolism modeling have established a computer model that invokes algorithms representing many of the pathways involved in glucose dysregulation for patients with diabetes. In this study, we used an FDA approved glucose metabolism model to evaluate an existing institutional intraoperative insulin infusion protocol via closedloop simulation on the virtual diabetic population that comes with the computer model. A comparison of simulated responses to actual retrospective clinical data from 57 type 1 diabetic patients undergoing cardiac surgery managed by the institutional protocol was performed. We then designed a proportional-derivative controller that overcomes the weaknesses exhibited by our old protocol while preserving its strengths. In-silico evaluation results show that our proportional-derivative controller more effectively manages intraoperative hyperglycemia while simultaneously reducing hypoglycemia and glycemic variability. By performing insilico simulation on intraoperative glucose and insulin responses, robust and seemingly efficacious algorithms can be generated that warrant prospective evaluation in human subjects

A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity.

Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)-edge-fog-cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for 'pulverization': applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT-edge-fog-cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings

Estudio descriptivo: valoración de autoeficacia percibida en la alimentación de estudiantes universitarios

Introduction: Self-efficacy refers to personal ability to control one's own behavior, being able to adopt a beneficial behavior and stop practicing one that would be harmful. Its assessment as a tool in health field is becoming increasingly important. The objectives were assess perceived self-efficacy in university students related to eating behaviors considered healthy and determine possible differences between careers. Methods: A psychometric test of food self-efficacy was used, validated and adapted to the Argentine food culture. The instrument was made up of 20 items, with a response option according to the Likert scale (1: lack of ability; 5: being very capable), corresponding to 4 categories: high-fat foods; healthy food; sweet foods and healthy drinks. Participants included 300 students from 3 careers: Biochemistry (BQ) and Bachelor's degrees in Biotechnology (BB) and Nutrition (BN). Results: The reliability of the instrument was 0.83 (Cronbach's Alpha). Population included 80% women and 20% men, 21 ± 4 years old. The healthy drinks category received the highest score, without observing statistical differences between careers (4.47, 4.37 and 4.37). The score obtained by BN corresponds to a greater sense of perceived self-efficacy than BQ and BB (respectively) in: foods high in fat (3.76 vs 3.31 and 3.50; p=0.001); healthy food (4.23 vs 3.75 and 3.90 p=0.003) and sweet foods (3.71 vs 3.53 and 3.55; p=0.016). Conclusions: It is assumed that the assessment of self-efficacy is an important predictor of the actions of individuals in various situations, resulting in a valuable tool to elucidate the particularities and promote nutritional food education in university students of different careers.Introducción: La autoeficacia refiere a la capacidad personal de controlar la propia conducta, siendo capaz de adoptar una beneficiosa y/o dejar de practicar una que resultaría dañina. Su valoración en el ámbito de la salud cobra cada vez más importancia. El objetivo del trabajo fue valorar en universitarios la autoeficacia percibida en conductas alimentarias consideradas saludables y determinar posibles diferencias entre carreras. Métodos: Se empleó un test psicométrico de autoeficacia alimentaria, validado y adaptado a la cultura alimentaria argentina, con opción de respuesta según escala de Likert (1: ausencia de capacidad; 5: ser muy capaz), correspondientes a 4 categorías: alimentos altos en grasa; alimentos dulces; alimentos saludables y bebidas saludables. Participaron 300 estudiantes, 80% mujeres y 20% varones, de 21±4 años, de tres carreras: Bioquímica (BQ) y las Licenciaturas en Biotecnología (LB) y en Nutrición (LN). Resultados: La confiabilidad del instrumento fue 0,83 (Alfa de Cronbach). La categoría bebidas saludables recibió la mayor puntuación, sin observar diferencias estadísticas entre carreras (4,47; 4,37 y 4,37). La puntuación obtenida por LN se correspondió con un mayor sentido de autoeficacia percibida que BQ y LB (respectivamente) en alimentos: altos en grasa (3,76 vs 3,31 y 3,50; p= 0,001); dulces (3,71 vs 3,53 y 3,55; p= 0,016) y saludables (4,23 vs 3,75 y 3,90 p=0,003). Conclusión: Siendo la valoración de la autoeficacia un importante predictor de las acciones de los individuos frente a diversas situaciones, resulta una herramienta valiosa para dilucidar las particularidades y promover la educación alimentaria nutricional en universitarios de diferentes carreras

Mechanisms involved in the β-cell mass increase induced by chronic sucrose feeding to normal rats

The aim of the present study was to clarify the mechanisms by which a sucrose-rich diet (SRD) produces an increase in the pancreatic β-cell mass in the rat. Normal Wistar rats were fed for 30 weeks either an SRD (SRD rats; 63% wt/wt), or the same diet but with starch instead of sucrose in the same proportion (CD rats). We studied body weight, serum glucose and triacylglycerol levels, endocrine tissue and β-cell mass, β-cell replication rate (proliferating cell nuclear antigen; PCNA), islet neogenesis (cytokeratin immunostaining) and β-cell apoptosis (propidium iodide). Body weight (g) recorded in the SRD rats was significantly (P<0.05) larger than that of the CD group (556.0 ± 8.3 vs 470.0 ± 13.1). Both serum glucose and triacylglycerol levels (mmol/l) were also significantly higher (P<0.05) in SRD than in CD rats (serum glucose, 8.11 ± 0.14 vs 6.62 ± 0.17; triacyglycerol, 1.57 ± 0.18 vs 0.47 ± 0.04). The number of pancreatic islets per unit area increased significantly (P<0.05) in SRD rats (3.29 ± 0.1 vs 2.01 ± 0.2). A significant increment (2.6 times) in the mass of endocrine tissue was detected in SRD animals, mainly due to an increase in the β-cell mass (P=0.0025). The islet cell replication rate, measured as the percentage of PCNA-labelled β cells increased 6.8 times in SRD rats (P<0.03). The number of apoptotic cells in the endocrine pancreas decreased significantly (three times) in the SRD animals (P=0.03). The cytokeratin-positive area did not show significant differences between CD and SRD rats. The increase of β-cell mass induced by SRD was accomplished by an enhanced replication of β cells together with a decrease in the rate of β-cell apoptosis, without any evident participation of islet neogenesis. This pancreatic reaction was unable to maintain serum glucose levels of these rats at the level measured in CD animals.Centro de Endocrinología Experimental y AplicadaFacultad de Ciencias Médica

Lower risk of death and cardiovascular events in patients with diabetes initiating glucagon-like peptide-1 receptor agonists or sodium-glucose cotransporter-2 inhibitors: A real-world study in two Italian cohorts

Aim: To examine the efficacy and safety of glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 (SGLT2) inhibitors compared with other antihyperglycaemic agents (AHAs) in large and unselected populations of the Lombardy and Apulia regions in Italy. Materials and Methods: An observational cohort study of first-time users of GLP-1RAs, SGLT2 inhibitors or other AHAs was conducted from 2010 to 2018. Death and cardiovascular (CV) events were evaluated using conditional Cox models in propensity-score-matched populations. Adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated for each region and in a meta-analysis for pooled risks. Results: After propensity-score matching, the Lombardy cohort included 18 716 and 11 683 patients and the Apulia cohort 9772 and 6046 patients for the GLP-1RA and SGLT2 inhibitor groups, respectively. Use of GLP-1RAs was associated with lower rates of death (HR 0.61, CI 0.56-0.65, Lombardy; HR 0.63, CI 0.55-0.71, Apulia), cerebrovascular disease and ischaemic stroke (HR 0.70, CI 0.63-0.79; HR 0.72, CI 0.60-0.87, Lombardy), peripheral vascular disease (HR 0.72, CI 0.64-0.82, Lombardy; HR 0.80, CI 0.67-0.98, Apulia), and lower limb complications (HR 0.67, CI 0.56-0.81, Lombardy; HR 0.69, CI 0.51-0.93, Apulia). Compared with other AHAs, SGLT2 inhibitor use decreased the risk of death (HR 0.47, CI 0.40-0.54, Lombardy; HR 0.43, CI 0.32-0.57, Apulia), cerebrovascular disease (HR 0.75, CI 0.61-0.91, Lombardy; HR 0.72, CI 0.54-0.96, Apulia), and heart failure (HR 0.56, CI 0.46-0.70, Lombardy; HR 0.57, CI 0.42-0.77, Apulia). In the pooled cohorts, a reduction in heart failure was also observed with GLP-1RAs (HR 0.89, 95% CI 0.82-0.97). Serious adverse events were quite low in frequency. Conclusion: Our findings from real-world practice confirm the favourable effect of GLP-1RAs and SGLT2 inhibitors on death and CV outcomes across both regions consistently. Thus, these drug classes should be preferentially considered in a broad type 2 diabetes population beyond those with CV disease

CNN-Based Health Model for Regular Health Factors Analysis in Internet-of-Medical Things Environment

Remote health monitoring applications with the advent of Internet of Things (IoT) technologies have changed traditional healthcare services. Additionally, in terms of personalized healthcare and disease prevention services, these depend primarily on the strategy used to derive knowledge from the analysis of lifestyle factors and activities. Through the use of intelligent data retrieval and classification models, it is possible to study disease, or even predict any abnormal health conditions. To predict such abnormality, the Convolutional neural network (CNN) model is used, which can detect the knowledge related to disease prediction accurately from unstructured medical health records. However, CNN uses a large amount of memory if it uses a fully connected network structure. Moreover, the increase in the number of layers can lead to an increase in the complexity analysis of the model. Therefore, to overcome these limitations of the CNN-model, we propose a CNN-regular target detection and recognition model based on the Pearson Correlation Coefficient and regular pattern behavior, where the term "regular" denotes objects that generally appear in similar contexts and have structures with low variability. In this framework, we develop a CNN-regular pattern discovery model for data classification. First, the most important health-related factors are selected in the first hidden layer, then in the second layer, a correlation coefficient analysis is conducted to classify the positively and negatively correlated health factors. Moreover, regular patterns' behaviors are discovered through mining the regular pattern occurrence among the classified health factors. The output of the model is subdivided into regular-correlated parameters related to obesity, high blood pressure, and diabetes. Two distinct datasets are adopted to mitigate the effects of the CNN-regular knowledge discovery model. The experimental results show that the proposed model has better accuracy, and low computational load, compared with three different machine learning techniques methods

An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method's effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning

An AI approach to Collecting and Analyzing Human Interactions with Urban Environments

Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, in turn, improve human well-being in urban environments. This is a scientifically challenging proposition, as it requires new methods to fuse objective (heterogeneous) data (e.g. people location trails and sensors data) with subjective (perceptual) data (e.g. the citizens’ quality of experience collected through feedback forms). When it comes to vast urban areas, collecting statistically significant data is a daunting task; thus new data-collection methods are required too. In this work, we turn to artificial intelligence (AI) to address these challenges, introducing a method whereby the objective, sensor data is analyzed in real-time to scope down the test matrix of the subjective questionnaires. In turn, subjective responses are parsed through AI models to extract further objective information. The outcome is an interactive data analysis framework for urban environments, which we put to test in the context of a citizens’ well-being project. In our pilot study, each new entry (objective or subjective) is parsed through the AI engine to determine which action maximizes the information gain. This translates into a particular question being fired at a specific moment and place, to a specific person. With our AI data collection method, we can reach statistical significance much faster, achieving (in our city-wide pilot study) a 41% acceleration factor and a 75% reduction in intrusiveness. Our study opens new avenues in urban science, with potential applications in urban planning, citizen’s well-being projects, and sociology, to mention but a few cases

Brain-derived neurotrophic factor is associated with human muscle satellite cell differentiation in response to muscle-damaging exercise

Muscle satellite cell (SC) regulation is a complex process involving many key signalling molecules. Recently, the neurotrophin brain-derived neurotropic factor (BDNF) has implicated in SC regulation in animals. To date, little is known regarding the role of BDNF in human SC function in vivo. Twenty-nine males (age, 21 ± 0.5 years) participated in the study. Muscle biopsies from the thigh were obtained prior to a bout of 300 maximal eccentric contractions (Pre), and at 6 h, 24 h, 72 h, and 96 h postexercise. BDNF was not detected in any quiescent (Pax7+/MyoD−) SCs across the time-course. BDNF colocalized to 39% ± 5% of proliferating (Pax7+/MyoD+) cells at Pre, which increased to 84% ± 3% by 96 h (P < 0.05). BDNF was only detected in 13% ± 5% of differentiating (Pax7−/MyoD+) cells at Pre, which increased to 67% ± 4% by 96 h (P < 0.05). The number of myogenin+ cells increased 95% from Pre (1.6 ± 0.2 cells/100 myofibres (MF)) at 24 h (3.1 ± 0.3 cells/100 MF) and remained elevated until 96 h (cells/100 MF), P < 0.05. The proportion of BDNF+/myogenin+ cells was 26% ± 0.3% at Pre, peaking at 24 h (49% ± 3%, P < 0.05) and remained elevated at 96 h (P < 0.05). These data are the first to demonstrate an association between SC proliferation and differentiation and BDNF expression in humans in vivo, with BDNF colocalization to SCs increasing during the later stages of proliferation and early differentiation