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.

The effects of prone position ventilation on experimental mild acute lung injury induced by intraperitoneal lipopolysaccharide injection in rats

The benefits of prone position ventilation are well demonstrated in the severe forms of acute respiratory distress syndrome, but not in the milder forms. We investigated the effects of prone position on arterial blood gases, lung inflammation, and histology in an experimental mild acute lung injury (ALI) model. ALI was induced in Wistar rats by intraperitoneal Escherichia coli lipopolysaccharide (LPS, 5 mg/kg). After 24 h, the animals with PaO2/FIO2 between 200 and 300 mmHg were randomized into 2 groups: prone position (n = 6) and supine position (n = 6). Both groups were compared with a control group (n = 5) that was ventilated in the supine position. All of the groups were ventilated for 1 h with volume-controlled ventilation mode (tidal volume = 6 ml/kg, respiratory rate = 80 breaths/min, positive end-expiratory pressure = 5 cmH2O, inspired oxygen fraction = 1). Significantly higher lung injury scores were observed in the LPS-supine group compared to the LPS-prone and control groups (0.32 ± 0.03; 0.17 ± 0.03 and 0.13 ± 0.04, respectively) (p < 0.001), mainly due to a higher neutrophil infiltration level in the interstitial space and more proteinaceous debris that filled the airspaces. Similar differences were observed when the gravity-dependent lung regions and non-dependent lung regions were analyzed separately (p < 0.05). The BAL neutrophil content was also higher in the LPS-supine group compared to the LPS-prone and control groups (p < 0.05). There were no significant differences in the wet/dry ratio and gas exchange levels. In this experimental extrapulmonary mild ALI model, prone position ventilation for 1 h, when compared with supine position ventilation, was associated with lower lung inflammation and injury.

Rinitis alérgica tratamiento con montelukast sólo o en combinación con antihistamínicos orales. revisión sistemática

Antecedentes: La rinitis alérgica es una enfermedad secundaria a la exposición a alérgenos con una inflamación de las mucosas nasales mediadas por la Ig-E, tiene síntomas como estornudos, obstrucción nasal, prurito nasal y descarga nasal. Los tratamientos de primera línea son los antihistamínicos orales y Montelukast los cuales se dan como monoterapia, existe la combinación de los dos tratamientos en el mercado, sin embargo se duda de su eficacia combinada para tratar los síntomas nasales. Objetivo: Determinar la eficacia y seguridad del tratamiento combinado de Montelukast con Antihistamínicos orales en el tratamiento de Rinitis Alérgica. Metodología: Se realizó una revisión sistemática de la literatura con metaanálisis de los estudios clínicos que evaluaron la eficacia de los antihistamínicos orales y Montelukast tanto en monoterapia como en terapia combinada. Resultados: De 795 artículos publicados hasta febrero 2016 identificados en las bases de datos electrónicas y literatura gris, se seleccionaron por consenso nueve estudios. Los estudios mostraron una reducción significativa del TNSS de -2,61 (-3.32 a -1,90) de la terapia combinada de Montelukast más antihistamínicos orales en comparación con la monoterapia de cada uno de ellos. Los estudios reportaron que la seguridad de la terapia combinada de Montelukast más antihistamínicos orales no fue diferente a la monoterapia. Conclusiones: La terapia combinada de Montelukast con antihistamínico redujo el puntaje de TNSS en -2,61 (-3.32 a -1,90) por lo que es eficaz y seguro en pacientes con rinitis alérgica.

Relación entre los riesgos percibidos por los trabajadores en minería subterránea y los accidentes de trabajo y enfermedades laborales

Introducción: La minería es considerada uno de los sectores económicos más importantes por su capacidad para generar recursos en su propio sector y en otros sectores como metalmecánica, agricultura e informática entre otros, y por su contribución al desarrollo socioeconómico sostenible de las poblaciones. Objetivo: Determinar la relación entre los riesgos percibidos por los trabajadores que laboran en minería subterránea en 3 departamentos de Colombia y los Accidentes de Trabajo (AT) y Enfermedades Laborales (EL). Materiales y Métodos: Estudio de corte transversal en 476 trabajadores de minería subterránea. Se incluyeron variables independientes (características sociodemográficas y laborales y percepción del riesgo) y variables dependientes (enfermedad laboral y accidente de trabajo), obtenidas a través de una entrevista directa aplicada por profesionales de la salud previamente capacitados. Para el análisis estadístico se utilizó la Prueba Exacta de Fisher, el Odds Ratio (OR) con el Intervalo de Confianza (IC) del 95%. Resultados: En los trabajadores de minería subterránea en los departamentos de Boyacá, Cundinamarca y Santander, se encontró relación estadística significativa entre la accidentalidad con la percepción de riesgo por iluminación (OR= 2.059, IC= 95%: 1.116, 3.798, p=0.013), percepción de riesgo por movimientos repetitivos (OR= 1.951, IC= 95%: 0.998, 3.815, p=0.034), percepción de riesgo por ruido (OR= 2.275, IC= 95%: 0.974, 5.312, p=0.039) y percepción de riesgo por manejo de cargas (OR= 1.778, IC= 95%: 0.969, 3.264, p=0.041). Conclusión: se encontró que existe una relación significativa entre la percepción de riesgo de los trabajadores de minería subterránea con accidentes de trabajo y que no existe relación entre esta percepción y las enfermedades laborales.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

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.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

A framework for analyzing system intelligence of the building control system : a study of the integrated building management system

The lack of satisfactory consensus for characterizing the system intelligence and structured analytical decision models has inhibited the developers and practitioners to understand and configure optimum intelligent building systems in a fully informed manner. So far, little research has been conducted in this aspect. This research is designed to identify the key intelligent indicators, and develop analytical models for computing the system intelligence score of smart building system in the intelligent building. The integrated building management system (IBMS) was used as an illustrative example to present a framework. The models presented in this study applied the system intelligence theory, and the conceptual analytical framework. A total of 16 key intelligent indicators were first identified from a general survey. Then, two multi-criteria decision making (MCDM) approaches, the analytic hierarchy process (AHP) and analytic network process (ANP), were employed to develop the system intelligence analytical models. Top intelligence indicators of IBMS include: self-diagnostic of operation deviations; adaptive limiting control algorithm; and, year-round time schedule performance. The developed conceptual framework was then transformed to the practical model. The effectiveness of the practical model was evaluated by means of expert validation. The main contribution of this research is to promote understanding of the intelligent indicators, and to set the foundation for a systemic framework that provide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed components design configurations.

Molecular epidemiology of respiratory viruses in a paediatric cohort from Indonesia

Acute lower respiratory tract infections (ALRTIs) are a common cause of morbidity and mortality among children under 5 years of age and are found worldwide, with pneumonia as the most severe manifestation. Although the incidence of severe disease varies both between individuals and countries, there is still no clear understanding of what causes this variation. Studies of community-acquired pneumonia (CAP) have traditionally not focused on viral causes of disease due to a paucity of diagnostic tools. However, with the emergence of molecular techniques, it is now known that viruses outnumber bacteria as the etiological agents of childhood CAP, especially in children under 2 years of age. The main objective of this study was to investigate viruses contributing to disease severity in cases of childhood ALRTI, using a two year cohort study following 2014 infants and children enrolled in Bandung, Indonesia. A total of 352 nasopharyngeal washes collected from 256 paediatric ALRTI patients were used for analysis. A subset of samples was screened using a novel microarray pathogen detection method that identified respiratory syncytial virus (RSV), human metapneumovirus (hMPV) and human rhinovirus (HRV) in the samples. Real-time RT-PCR was used both for confirming and quantifying viruses found in the nasopharyngeal samples. Viral copy numbers were determined and normalised to the numbers of human cells collected with the use of 18S rRNA. Molecular epidemiology was performed for RSV A and hMPV using sequences to the glycoprotein gene and nucleoprotein gene respectively, to determine genotypes circulating in this Indonesian paediatric cohort. This study found that HRV (119/352; 33.8%) was the most common virus detected as the cause of respiratory tract infections in this cohort, followed by the viral pathogens RSV A (73/352; 20.7%), hMPV (30/352; 8.5%) and RSV B (12/352; 3.4%). Co-infections of more than two viruses were detected in 31 episodes (defined as an infection which occurred more than two weeks apart), accounting for 8.8% of the 352 samples tested or 15.4% of the 201 episodes with at least one virus detected. RSV A genotypes circulating in this population were predominantly GA2, GA5 and GA7, while hMPV genotypes circulating were mainly A2a (27/30; 90.0%), B2 (2/30; 6.7%) and A1 (1/30; 3.3%). This study found no evidence of disease severity associated either with a specific virus or viral strain, or with viral load. However, this study did find a significant association with co-infection of RSV A and HRV with severe disease (P = 0.006), suggesting that this may be a novel cause of severe disease.

Combination of Kalman filter and least error square techniques in power system

An algorithm based on the concept of combining Kalman filter and Least Error Square (LES) techniques is proposed in this paper. The algorithm is intended to estimate signal attributes like amplitude, frequency and phase angle in the online mode. This technique can be used in protection relays, digital AVRs, DGs, DSTATCOMs, FACTS and other power electronics applications. The Kalman filter is modified to operate on a fictitious input signal and provides precise estimation results insensitive to noise and other disturbances. At the same time, the LES system has been arranged to operate in critical transient cases to compensate the delay and inaccuracy identified because of the response of the standard Kalman filter. Practical considerations such as the effect of noise, higher order harmonics, and computational issues of the algorithm are considered and tested in the paper. Several computer simulations and a laboratory test are presented to highlight the usefulness of the proposed method. Simulation results show that the proposed technique can simultaneously estimate the signal attributes, even if it is highly distorted due to the presence of non-linear loads and noise.

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.

Implementation of a survey form marking system using image processing techniques on an Imputer

This paper describes the feasibility of the application of an Imputer in a multiple choice answer sheet marking system based on image processing techniques.

A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.

Experimental evidences in bearing diagnostics for traction system of high speed trains

Rolling element bearings are the most critical components in the traction system of high speed trains. Monitoring their integrity is a fundamental operation in order to avoid catastrophic failures and to implement effective condition based maintenance strategies. Generally, diagnostics of rolling element bearings is usually performed by analyzing vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. Several papers have been published on this subject in the last two decades, mainly devoted to the development and assessment of signal processing techniques for diagnostics. The experimental validation of such techniques has been traditionally performed by means of laboratory tests on artificially damaged bearings, while their actual effectiveness in specific industrial applications, particularly in rail industry, remains scarcely investigated. This paper is aimed at filling this knowledge gap, by addressing the diagnostics of bearings taken from the service after a long term operation on the traction system of a high speed train. Moreover, in order to test the effectiveness of the diagnostic procedures in the environmental conditions peculiar to the rail application, a specific test-rig has been built, consisting of a complete full-scale train traction system, able to reproduce the effects of wheeltrack interaction and bogie-wheelset dynamics. The results of the experimental campaign show that suitable signal processing techniques are able to diagnose bearing failures even in this harsh and noisy application. Moreover, the most suitable location of the sensors on the traction system is proposed, in order to limit their number.