Real-time monitoring of progression towards renal failure in primary care patients

First published online: December 16, 2014.

O Sinclinal de ferro de Torre de Moncorvo: um recurso mineral como catalisador de um Tempolntermitente

Dissertação de mestrado integrado em Arquitectura

On the length of bank-firm relationships: an empirical application to a major French bank

NIPE - WP 01/ 2016

Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate

The receiver-operating characteristic (ROC) curve is the most widely used measure for evaluating the performance of a diagnostic biomarker when predicting a binary disease outcome. The ROC curve displays the true positive rate (or sensitivity) and the false positive rate (or 1-specificity) for different cut-off values used to classify an individual as healthy or diseased. In time-to-event studies, however, the disease status (e.g. death or alive) of an individual is not a fixed characteristic, and it varies along the study. In such cases, when evaluating the performance of the biomarker, several issues should be taken into account: first, the time-dependent nature of the disease status; and second, the presence of incomplete data (e.g. censored data typically present in survival studies). Accordingly, to assess the discrimination power of continuous biomarkers for time-dependent disease outcomes, time-dependent extensions of true positive rate, false positive rate, and ROC curve have been recently proposed. In this work, we present new nonparametric estimators of the cumulative/dynamic time-dependent ROC curve that allow accounting for the possible modifying effect of current or past covariate measures on the discriminatory power of the biomarker. The proposed estimators can accommodate right-censored data, as well as covariate-dependent censoring. The behavior of the estimators proposed in this study will be explored through simulations and illustrated using data from a cohort of patients who suffered from acute coronary syndrome.

Redução de contaminações: um projeto de melhoria contínua em ambiente seis sigma

Dissertação de mestrado integrado em Engenharia e Gestão Industrial

Modulation of the secretome of hBMSCs by tailoring the macromolecular gradient in hydrogels to generate tissue-to-tissue interfaces

Tissue-to-tissue interfaces are commonly present in all tissues exhibiting structural, biological and chemical gradients serving a wide range of physiological functions. These interfaces are responsible for mediation of load transfer between two adjacent tissues. They are also important structures in sustaining the cellular communications to retain tissueâ s functional integration and homeostasis. [1] All cells have the capacity to sense and respond to physical and chemical stimulus and when cultured in three-dimensional (3D) environments they tend to perform their function better than in two-dimensional (2D) environments. Spatial and temporal 3D gradient hydrogels better resemble the natural environment of cells in mimicking their extracellular matrix. [2] In this study we hypothesize that differential functional properties can be engineered by modulation of macromolecule gradients in a cell seeded threedimensional hydrogel system. Specifically, differential paracrine secretory profiles can be engineered using human Bone Marrow Stem Cells (hBMSCâ s). Hence, the specific objectives of this study are to: assemble the macromolecular gradient hydrogels to evaluate the suitablity for hBMSCâ s encapsulation by cellular viability and biofunctionality by assessing the paracrine secretion of hBMSCâ s over time. The gradient hydrogels solutions were prepared by blend of macromolecules in one solution such as hyaluronic (HA) acid and collagen (Col) at different ratios. The gradient hydrogels were fabricated into cylindrical silicon moulds with higher ratio solutions assembled at the bottom of the mould and adding the two solutions consecutively on top of each other. The labelling of the macromolecules was performed to confirm the gradient through fluorescence microscopy. Additionally, AFM was conducted to assess the gradient hydrogels stiffness. Gradient hydrogels characterization was performed by HA and Col degradation assay, degree of crosslinking and stability. hBMSCâ s at P3 were encapsulated into each batch solution at 106 cells/ml solution and gradient hydrogels were produced as previously described. The hBMSCâ s were observed under confocal microscopy to assess viability by Live/Dead® staining. Cellular behaviour concerning proliferation and matrix deposition was also performed. Secretory cytokine measurement for pro-inflammatory and angiogenesis factors was carried out using ELISA. At genomic level, qPCR was carried out. The 3D gradient hydrogels platform made of different macromolecules showed to be a suitable environment for hBMSCâ s. The hBMSCâ s gradient hydrogels supported high cell survival and exhibited biofunctionality. Besides, the 3D gradient hydrogels demonstrated differentially secretion of pro-inflammatory and angiogenic factors by the encapsulated hBMSCâ s. References: 1. Mikos, AG. et al., Engineering complex tissues. Tissue Engineering 12,3307, 2006 2. Phillips, JE. et al., Proc Natl Acad Sci USA, 26:12170-5, 2008

Continuous monitoring of concrete mechanical properties since an early age to support construction phasing

The assessment of concrete mechanical properties during construction of concrete structures is of paramount importance for many intrinsic operations. However many of the available non-destructive methods for mechanical properties have limitations for use in construction sites. One of such methodologies is EMM-ARM, which is a variant of classic resonant frequency methods. This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, as to provide real-time information about concrete mechanical properties such as E-modulus and compressive strength. To achieve the aforementioned objective, a set of adaptations to the method have been successfully implemented and tested: (i) the reduction of the beam span; (ii) the use of a different mould material and (iii) a new support system for the beams. Based on these adaptations, a reusable mould was designed to enable easier systematic use of EMMARM. A pilot test was successfully performed under in-situ conditions during a bridge construction.

The effect of secular trends in the classroom furniture mismatch: support for continuous update of school furniture standards

In order to create safer schools, the Chilean authorities published a Standard regarding school furniture dimensions. The aims of this study are twofold: to verify the existence of positive secular trend within the Chilean student population and to evaluate the potential mismatch between the anthropometric characteristics and the school furniture dimensions defined by the mentioned standard. The sample consists of 3078 subjects. Eight anthropometric measures were gathered, together with six furniture dimensions from the mentioned standard. There is an average increase for some dimensions within the Chilean student population over the past two decades. Accordingly, almost 18% of the students will find the seat height to be too high. Seat depth will be considered as being too shallow for 42.8% of the students. It can be concluded that the Chilean student population has increased in stature, which supports the need to revise and update the data from the mentioned Standard. Practitioner Summary: Positive secular trend resulted in high levels of mismatch if furniture is selected according to the current Chilean Standard which uses data collected more than 20 years ago. This study shows that school furniture standards need to be updated over time.

Implementation of a lean six sigma project in a production line

Companies from the motorcycles components branch are dealing with a dynamic environment, resulting from the introduction of new products and the increase of market demand. This dynamic environment requires frequent changes in production lines and requires flexibility in the processes, which can cause reductions in the level of quality and productivity. This paper presents a Lean Six Sigma improvement project performed in a production line of the company's machining sector, in order to eliminate losses that cause low productivity, affecting the fulfillment of the production plan and customer satisfaction. The use of Lean methodology following the DMAIC stages allowed analyzing the factors that influence the line productivity loss. The major problems and causes that contribute to a reduction on productivity and that were identified in this study are the lack of standardization in the setup activities and the excessive stoppages for adjustment of the processes that caused an increase of defects. Control charts, Pareto analysis and cause-and-effect diagrams were used to analyze the problem. On the improvement stage, the changes were based on the reconfiguration of the line layout as well as the modernization of the process. Overall, the project justified an investment in new equipment, the defective product units were reduced by 84% and an increase of 29% of line capacity was noticed.

Blow-up and finite time extinction for p(x, t)-curl systems arising in electromagnetism

"Available online 22 March 2016"

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.