Evaluation of faecal sampling methods for the analysis of Giardia sp. in companion animals

Dissertação de Mestrado Integrado em Medicina Veterinária

Auscultación cervical: alternativa no invasiva en el diagnóstico de disfagia en niños con problemas de neurodesarrollo

Los niños y las niñas con disfunciones neurológicas tienen frecuentemente disfagia, condición que les ocasiona infecciones respiratorias a repetición, desnutrición, mala calidad de vida; su oportuno diagnóstico permite decidir sobre la mejor intervención. La videofluoroscopia y de videoendoscopia son técnicas diagnósticas invasivas, costosas y por lo tanto difíciles de hacerlas, lo que ocasiona retardo en el diagnóstico e intervención. Hoy en día existen nuevas tecnologías médicas no invasivas que pueden ser muy eficaces, una de ellas es la auscultación cervical que escucha los sonidos de la deglución mediante un estetoscopio u otro dispositivo de medición como la colocación de un micrófono o un acelerómetro en la superficie del cuello. Este método tiene como principio que los sonidos y/o movimientos biológicos normales de la deglución son diferentes de los anormales. En este artículo se presenta una revisión de la pertinencia social del diagnóstico de la disfagia, de las aplicaciones clínicas de la auscultación cervical y los dispositivos usados para realizarla, como una base para establecer su potencial de uso para la detección de disfagia en niños con problemas de neurodesarrollo. Estas orientaciones teóricas permiten al médico tener actuaciones más acertadas en el diagnóstico integral de niños y niñas con disfunción neurológica

Diagnóstico de mastite subclínica caprina por meio da detecção de Staphylococcus aureus utilizando a técnica de RT-PCR.

A presente publicação descreve os procedimentos necessários para a identificação e confirmação molecular de estirpes de S. aureus causadoras de mastite subclínica, provenientes de amostras de leite de cabra, por meio da técnica de RT-PCR.

Infective endocarditis: Absence of microbiological diagnosis is an independent predictor of inhospital mortality

Infective endocarditis (IE) is associated with high inhospital mortality. New microbiological diagnostic techniques have reduced the proportion of patients without etiological diagnosis, but in a significant number of patients the cause is still unknown. Our aim was to study the association of the absence of microbiological diagnosis with in-hospital prognosis. Prospective cohort of 2000 consecutive patients with IE. Data were collected in 26 Spanish hospitals. Modified Duke criteria were used to diagnose patients with suspected IE. A total of 290 patients (14.8%) had negative blood cultures. Etiological diagnosis was achieved with other methods (polymerase chain reaction, serology and other cultures) in 121 (6.1%). Finally, there were 175 patients (8.8%) without microbiological diagnosis (Group A) and 1825 with diagnosis (Group B). In-hospital mortality occurred in 58 patients in Group A (33.1%) vs. 487 (26.7%) in Group B, p = 0.07. Patients in Group A had a lower risk profile than those in Group B, with less comorbidity (Charlson index 1.9 ± 2.0 vs. 2.3 ± 2.1, p = 0.03) and lower surgical risk (EuroSCORE 23.6 ± 21.8 vs. 29.6 ± 25.2, p = 0.02). However they presented heart failure more frequently (53% vs. 40%, p = 0.005). Multivariate analysis showed that the absence of microbiological diagnosis was an independent predictor of inhospital mortality (odds ratio 1.8, 95% Confidence Interval 1.1–2.9, p = 0.016). Approximately 9% of patients with IE had no microbiological diagnosis. Absence of microbiological diagnosis was an independent predictor of inhospital mortality.

Evaluación del desempeño de pruebas diagnósticas de tuberculosis en pacientes VIH positivo en dos hospitales públicos de Bogotá

La tuberculosis TB es una de las principales causas de muerte en el mundo en individuos con infección por VIH. En Colombia esta coinfección soporta una carga importante en la población general convirtiéndose en un problema de salud pública. En estos pacientes las pruebas diagnósticas tienen sensibilidad inferior y la enfermedad evoluciona con mayor frecuencia hacia formas diseminadas y rápidamente progresivas y su diagnóstico oportuno representa un reto en Salud. El objetivo de este proyecto es evaluar el desempeño de las pruebas diagnósticas convencionales y moleculares, para la detección de TB latente y activa pacientes con VIH, en dos hospitales públicos de Bogotá. Para TB latente se evaluó la concordancia entre las pruebas QuantiFERON-TB (QTF) y Tuberculina (PPD), sugiriendo superioridad del QTF sobre la PPD. Se evaluaron tres pruebas diagnósticas por su sensibilidad y especificidad, baciloscopia (BK), GenoType®MTBDR plus (Genotype) y PCR IS6110 teniendo como estándar de oro el cultivo. Los resultados de sensibilidad (S) y especificidad (E) de cada prueba con una prevalencia del 19,4 % de TB pulmonar y extrapulmonar en los pacientes que participaron del estudio fue: BK S: 64% E: 99,1%; Genotype S: 77,8% E: 94,5%; PCRIS6110 S: 73% E: 95,5%, de la misma forma se determinaron los valores predictivos positivos y negativos (VPP y VPN) BK: 88,9% y 94,8%, Genotype S: 77,8% E: 94,5%; PCRIS6110 S: 90% y 95,7%. Se concluyó bajo análisis de curva ROC que las pruebas muestran un rendimiento diagnóstico similar por separado en el diagnóstico de TB en pacientes con VIH, aumentando su rendimiento diagnostico cuando se combinan

Diagnostic accuracy of semi-quantitative and quantitative culture techniques for the diagnosis of catheter-related infections in newborns and molecular typing of isolated microorganisms

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Feasibility and diagnostic effectiveness of new capsule endoscopy techniques

Since its approval by FDA in 2001, capsule endoscopy revolutionized the study of small bowel. One of the main limitations of its diffusion has been the high cost. More recently, a new videocapsule system (OMOM CE) has been developed in China and obtained the CE mark. Its cost is approximately half that of other capsule systems. However, there are few studies regarding the clinical experience with this new videocapsule system and none of them has been performed in the western world. Among the limitations of capsule endoscopy, there is also one linked to the diagnostic yield. The rapid transit of the device in the proximal segments implies a high risk of false negatives; an indirect confirmation of this limit is offered by the poor ability to identify the papilla of Vater. In addition, recent studies show that in patients with obscure gastrointestinal bleeding, the negative outcome of capsule endoscopy is correlated to a significant risk of recurrence of anemia in the short term, as well as the presence of small bowel lesions documented by a second capsule endoscopy. It was recently approved the use of a new device called "CapsoCam" (CapsoVision, Inc. Saratoga) characterized by four side cameras that offer a panoramic view of 360 degrees, instead of the front to 160°. Two recent pilot studies showed comparable safety profiles and diagnostic yield with the more standardized capsule. Namely, side vision has made possible a clear visualization of the papilla in 70% of cases. The aim of our study is to evaluate the feasibility and diagnostic yield of these two new devices, which first may allow a reduction in costs. Moreover, their complementary use could lead to a recovery diagnostic in patients with false negative results in an initial investigation.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Development of a diagnostic tool for condition monitoring of rotating machinery

This paper presents an overview of the CRC for Infrastructure and Engineering Asset Management (CIEAM)’s rotating machine health monitoring project and the status of the research progress. The project focuses on the development of a comprehensive diagnostic tool for condition monitoring and systematic analysis of rotating machinery. Particularly attention focuses on the machine health monitoring of diesel engines, compressors and pumps by using acoustic emission and vibration-based monitoring techniques. The paper also provides a brief summary of the work done by the three main research collaborating partners in the project, namely, Queensland University of Technology (QUT), Curtin University of Technology (CUT) and the University of Western Australia (UWA). Preliminary test and analysis results from this work are also reported in the paper

A circuit-breaker restrike diagnostic algorithm using ATP and wavelet transforms

Circuit breaker restrikes are unwanted occurrence, which can ultimately lead to breaker. Before 2008, there was little evidence in the literature of monitoring techniques based on restrike measurement and interpretation produced during switching of capacitor banks and shunt reactor banks. In 2008 a non-intrusive radiometric restrike measurement method, as well a restrike hardware detection algorithm was developed. The limitations of the radiometric measurement method are a band limited frequency response as well as limitations in amplitude determination. Current detection methods and algorithms required the use of wide bandwidth current transformers and voltage dividers. A novel non-intrusive restrike diagnostic algorithm using ATP (Alternative Transient Program) and wavelet transforms is proposed. Wavelet transforms are the most common use in signal processing, which is divided into two tests, i.e. restrike detection and energy level based on deteriorated waveforms in different types of restrike. A ‘db5’ wavelet was selected in the tests as it gave a 97% correct diagnostic rate evaluated using a database of diagnostic signatures. This was also tested using restrike waveforms simulated under different network parameters which gave a 92% correct diagnostic responses. The diagnostic technique and methodology developed in this research can be applied to any power monitoring system with slight modification for restrike detection.

The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings

The coupling of kurtosis based-indexes and envelope analysis represents one of the most successful and widespread procedures for the diagnostics of incipient faults on rolling element bearings. Kurtosis-based indexes are often used to select the proper demodulation band for the application of envelope-based techniques. Kurtosis itself, in slightly different formulations, is applied for the prognostic and condition monitoring of rolling element bearings, as a standalone tool for a fast indication of the development of faults. This paper shows for the first time the strong analytical connection which holds for these two families of indexes. In particular, analytical identities are shown for the squared envelope spectrum (SES) and the kurtosis of the corresponding band-pass filtered analytic signal. In particular, it is demonstrated how the sum of the peaks in the SES corresponds to the raw 4th order moment. The analytical results show as well a link with an another signal processing technique: the cepstrum pre-whitening, recently used in bearing diagnostics. The analytical results are the basis for the discussion on an optimal indicator for the choice of the demodulation band, the ratio of cyclic content (RCC), which endows the kurtosis with selectivity in the cyclic frequency domain and whose performance is compared with more traditional kurtosis-based indicators such as the protrugram. A benchmark, performed on numerical simulations and experimental data coming from two different test-rigs, proves the superior effectiveness of such an indicator. Finally a short introduction to the potential offered by the newly proposed index in the field of prognostics is given in an additional experimental example. In particular the RCC is tested on experimental data collected on an endurance bearing test-rig, showing its ability to follow the development of the damage with a single numerical index.

Development of a new signal processing diagnostic tool for vibration signals acquired in transient conditions

The diagnostics of mechanical components operating in transient conditions is still an open issue, in both research and industrial field. Indeed, the signal processing techniques developed to analyse stationary data are not applicable or are affected by a loss of effectiveness when applied to signal acquired in transient conditions. In this paper, a suitable and original signal processing tool (named EEMED), which can be used for mechanical component diagnostics in whatever operating condition and noise level, is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED) and the analytical approach of the Hilbert transform. The proposed tool is able to supply diagnostic information on the basis of experimental vibrations measured in transient conditions. The tool has been originally developed in order to detect localized faults on bearings installed in high speed train traction equipments and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on spectral kurtosis or envelope analysis, which represent until now the landmark for bearings diagnostics.

Signal Processing Diagnostic Tool for Rolling Element Bearings Using EMD and MED

The signal processing techniques developed for the diagnostics of mechanical components operating in stationary conditions are often not applicable or are affected by a loss of effectiveness when applied to signals measured in transient conditions. In this chapter, an original signal processing tool is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition, Minimum Entropy Deconvolution and the analytical approach of the Hilbert transform. The tool has been developed to detect localized faults on bearings of traction systems of high speed trains and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on envelope analysis or spectral kurtosis, which represent until now the landmark for bearings diagnostics.