Reducing clinical variations with clinical pathways: do pathways work?

OBJECTIVE: To test clinical pathways in a variety of Italian health care organizations in 2000-2002 to measure performance in decreasing process and outcome variations. DESIGN: Creation of indicators, specific for each clinical pathway, to measure variations in the care processes and outcomes. Pre- and post-analysis model to evaluate the possible effect of the clinical pathways on each indicator. SETTING: We tested the clinical pathways in six sites, each with different clinical pathways. RESULTS: Reductions in health care macro-variation phenomena (length of stay, patient pathways, etc.) and in performance micro-variation (variations in diagnostic and therapeutic prescriptions, protocol implementation, etc.) were shown in sites where pathways were implemented successfully. A significant improvement in outcome for patients who were treated according to the clinical pathway for heart failure was also demonstrated. CONCLUSIONS: The overall purpose of clinical pathways is to improve outcome by providing a mechanism to coordinate care and to reduce fragmentation, and ultimately cost. Our results demonstrated that it is possible to achieve this goal. Although controversial elements still exist, we think that clinical pathways can have a positive impact on quality in health care

Numerical simulation of spray coalescence in an eulerian framework : direct quadrature method of moments and multi-fluid method

The scope of the present study is Eulerian modeling and simulation of polydisperse liquid sprays undergoing droplet coalescence and evaporation. The fundamental mathematical description is the Williams spray equation governing the joint number density function f(v, u; x, t) of droplet volume and velocity. Eulerian multi-fluid models have already been rigorously derived from this equation in Laurent et al. (2004). The first key feature of the paper is the application of direct quadrature method of moments (DQMOM) introduced by Marchisio and Fox (2005) to the Williams spray equation. Both the multi-fluid method and DQMOM yield systems of Eulerian conservation equations with complicated interaction terms representing coalescence. In order to validate and compare these approaches, the chosen configuration is a self-similar 2D axisymmetrical decelerating nozzle with sprays having various size distributions, ranging from smooth ones up to Dirac delta functions. The second key feature of the paper is a thorough comparison of the two approaches for various test-cases to a reference solution obtained through a classical stochastic Lagrangian solver. Both Eulerian models prove to describe adequately spray coalescence and yield a very interesting alternative to the Lagrangian solver

Teaching Mathematics to Non-Mathematics Majors through Problem Solving and New Technologies

The role of mathematics in several scientific disciplines is undisputed; work and everyday life take great advantage of its application. Nevertheless, students often tend to not particularly like it and to consider it of little interest. It is also believed that only people with a certain attitude are capable of mastering the subject. In consideration of this, we aimed to help science students develop mathematical competences by designing a course specifically oriented to applications and problem solving. We administered our course to students attending the first year of a program in biotechnology, asking them to work with technologies instilling curiosity and interest, thus achieving a better proficiency as a consequence. Two questionnaires, along with access and proficiency data, allowed us to collect information about students’ attitudes, beliefs, and activity, which we analyzed by means of descriptive statistics. The promotion of the interaction among learners made them active users of the contents, thus allowing for the adaptation of their learning paths according to their personal necessities, as well as the development of teamwork skills and flexibility. Finally, students recognized the usefulness of the problem-solving approach and the role played by software

CFD-based scale-up of hydrodynamics and mixing in bubble columns

Unsteady and three-dimensional Eulerian–Eulerian CFD simulations of bubble column reactors under operating conditions of industrial interest are discussed in this work. The flow pattern in this equipment depends strongly on the interactions between the gas and liquid phases, mainly via the drag force. In this work, a correlation for the drag force coefficient is tested and improved to consider the so-called swarm effect that modifies the drag force at high gas volume fractions. The improved swarm factor proposed in this work is the adjustment of the swarm factor proposed by Simonnet et al. (2008). This new swarm factor is suitable for very high gas volume fractions without generating stability problems, which were encountered with the original formulation. It delivers an accurate prediction of gas volume fraction and liquid velocity in a wide range of tested operating conditions. Results are validated by comparison with experimental data on bubble column reactors at different scales and for several operating conditions. Hydrodynamics is well predicted for every operating condition at different scales. Several turbulence models are tested. Finally, the contribution of Bubble Induced Turbulence (BIT), as proposed by Alméras et al. (2015), on mixing is evaluated via an analysis of the mixing time

The Link Among Neurological Diseases: Extracellular Vesicles as a Possible Brain Injury Footprint

Extracellular vesicles (EVs), referred as membranous vesicles released into body fluids from all cell types, represent a novel model to explain some aspects of the inter-cellular cross talk. It has been demonstrated that the EVs modify the phenotype of target cells, acting through a large spectrum of mechanisms. In the central nervous system, the EVs are responsible of the wide range of physiological processes required for normal brain function and neuronal support, such as immune signaling, cellular proliferation, differentiation, and senescence. Growing evidences link the EV functions to the pathogenic machinery of the neurological diseases, contributing to the disease progression and spreading. Extracellular vesicles are involved in the brain injury by multimodal ways; they propagate inflammation across the blood brain barrier (BBB), mediate neuroprotection and modulate regenerative processes. For these reasons, extracellular vesicles represent a promising biomarker in neurological disorders as well as an interesting starting point for the development of novel therapeutic strategies. Herein, we review the role of the EVs in the pathogenesis of neurological disease, discussing their potential clinical applications

Numerical and Experimental Analysis of the Daughter Distribution in Liquid-Liquid Stirred Tanks

The drop size distributions (DSDs) of a dilute immiscible liquid-liquid mixture were measured in a fully turbulent stirred tank operating at different impeller speeds. The results were used to infer the best daughter distribution function (DDF) leading to the best reproduction of the shape of the DSD. Bell-shaped, U-shaped, M-shaped, and uniform statistical DDFs were studied, producing from two to four daughters from each breakup event. A simplified approach from the literature was adopted to solve the population balance equation that considers the spectrum of the turbulence inside the tank obtained from computational fluid dynamics simulations. The U-shaped distribution producing four fragments better reproduces the shape of the experimental DSD in the studied system

SeVuc: A study on the Security Vulnerabilities of Capsule Networks against adversarial attacks

Capsule Networks (CapsNets) preserve the hierarchical spatial relationships between objects, and thereby bear the potential to surpass the performance of traditional Convolutional Neural Networks (CNNs) in performing tasks like image classification. This makes CapsNets suitable for the smart cyber-physical systems (CPS), where a large amount of training data may not be available. A large body of work has explored adversarial examples for CNNs, but their effectiveness on CapsNets has not yet been studied systematically. In our work, we perform an analysis to study the vulnerabilities in CapsNets to adversarial attacks. These perturbations, added to the test inputs, are small and imperceptible to humans, but can fool the network to mispredict. We propose a greedy algorithm to automatically generate imperceptible adversarial examples in a black-box attack scenario. We show that this kind of attacks, when applied to the German Traffic Sign Recognition Benchmark and CIFAR10 datasets, mislead CapsNets in making a correct classification, which can be catastrophic for smart CPS, like autonomous vehicles. Moreover, we apply the same kind of adversarial attacks to a 5-layer CNN (LeNet), to a 9-layer CNN (VGGNet), and to a 20-layer CNN (ResNet), and analyze the outcome, compared to the CapsNets, to study their different behaviors under the adversarial attacks

Bacterial contamination of saline nasal irrigations in children: An original research

Microbiologic analysis of nasal saline irrigations (NSIs) used in hospitalized children was performed. Of 253 collected samples, 24.9% were positive, and the number of positive samples significantly increased over time (P < .001). Staphylococcus aureus was the most frequently detected bacterium (28.6%). None of the 118 patients who received NSIs developed a nasosinusal infection. Colonization by cutaneous and environmental germs is frequent and develops early. Hygienic measures should be advocated to reduce contamination