Estudio comparativo de la fiabilidad y reproducibilidad en la medición de los tamaños dentarios y las medidas de las arcadas dentarias entre registros manuales y digitales 3d obtenidos por escaneado intraoral y extraoral

Tradicionalmente, los ortodoncistas han realizado las mediciones dentales en los modelos de yeso, pero los avances tecnológicos permiten ahora a los ortodoncistas realizar esas mediciones en los modelos digitales. El propósito de este estudio fue comparar la fiabilidad y reproducibilidad de las medidas de los tamaños dentarios y las arcadas dentarias entre el método manual y los métodos digitales 3D obtenidos por un escáner intraoral CEREC Omnicam (Sirona Dental Systems) y dos escáneres extraorales: inEos X5 (Sirona Dental Systems) y Dental Scanner SMART (Open Technologies). Un modelo de yeso, un escaneado intraoral y dos modelos digitales con un escáner extraoral fueron realizadas para cada uno de los 20 sujetos. Las medidas de los tamaños dentarios, la distancia intercanina y la distancia intermolar de los modelos digitales se compararon con los correspondientes modelos de yeso (estándar de oro) Se utilizó el test de ANOVA para establecer la fiabilidad entre los cuatro métodos y el coeficiente de correlación intraclase fue calculado para determinar la reproducibilidad intra- e inter-examinador. Los resultados encontrados fueron que no existieron diferencias estadísticamente significativas entre las medidas hechas directamente en los modelos de yeso y los modelos digitales. Los coeficientes de correlación intraclase tanto intra- e inter-examinador fue alto y considerado bueno para los cuatro métodos de medición. CCI> 0.90. Se concluyó que las mediciones en los modelos digitales obtenidos con un escáner extraoral e intraoral son fiables y reproducibles

Família Enterobacteriaceae, Acinetobacter baumannii e Pseudomonados na microbiota bucal de pacientes mantidos em unidades de terapia intensiva

Enteric organisms, pseudomonads and other opportunistic microorganisms in the oral microbiota have been linked to serious infections in patients hospitalized in intensive care units (ICU). The present study evaluated the presence of family Enterobacteriaceae, Pseudomonas aeruginosa, and Acinetobacter baumannii in the mouth of patients in ICU, correlating it with oral and systemic conditions. Data on health, socioeconomic status, medication use, drug addiction, medical and family histories of patients held for more than 72 hours in the ICU with a diagnosis of severe infection or that developed this condition after entry in said unit were obtained. Fifty patients provided clinical samples of supragingival and subgingival biofilms, saliva and oral mucous membranes were collected, as well as respiratory secretions from patients with pneumonia, blood and urine for sepsis. The presence of target microorganisms was carried out by polymerase chain reaction (PCR) and by culture using selective media. The Chi-square and Mann-Whitney tests were used for statistical analysis, and the significance level was 5%. The intraoral clinical conditions of the patients were poor. The family Enterobacteriaceae was the most prevalent, affecting 39.5% of the supragingival biofilm samples of patients attended in ICU and 18.6% of patients in the control group, besides the rods were the only group found in extraoral samples.

Classe mollicutes na boca de pacientes com dependência química

Drug addiction is one of the biggest public health problems worldwide, not only by the dimensions of the problem, but also by the severity of the damage, creating favorable conditions for opportunistic microorganisms such as class Mollicutes. This study aims to evaluate the presence of the main species and genera of this group in the subgingival biofilm of drug addiction patients, comparing them with non-dependent subjects. For this purpose, data on systemic health conditions, socioeconomic characteristics, drug addiction from 72 patients with drug addiction kept in rehab clinics and 100 non-dependent patients who formed the control group were obtained. Intra and extraoral clinical examinations were performed and samples of subgingival plaque were collected through sterile absorbent paper cones. The presence of different genera and species of the class Mollicutes was evaluated by PCR using the specific primers and conditions for each microorganism. The statistical analysis was performed using the Chi-square test for comparisons of three or more variables and the Mann-Whitney test, with significance level of 5%. Out of species and genera evaluated, Mycoplasma salivarium showed correlation with gingival inflammation in both patient groups and was more frequently detected among drug addiction patients

Enterococcus spp. em espécimes clínicos de pacientes atendidos em unidades de terapia intensiva

The relationship between the occurrence of enterococci in the oral microbiota and serious infections in patients hospitalized in intensive care units (ICU) has been established. This study evaluated the presence of Enterococcus faecalis and other species of this genus in the mouths of patients on ICU, correlating it with oral and systemic conditions. Data on health and socioeconomic, medication use, medical and family history of patients maintained for 72 hours in the ICU, diagnosed with severe infection or who have developed this condition after the entry to the unit were obtained. Fifty patients provided intraoral and extraoral clinical samples for analysis (above and subgingival biofilm, saliva and buccal mucosa, followed by obtaining samples of respiratory secretions for patients with pneumonia, and blood and urine for sepsis). The presence of target microorganisms was performed by polymerase chain reaction (PCR) and culture using selective media. The chi-square and Mann-Whitney tests were used for statistical analysis, and the significance level was 5%. The intraoral clinical conditions of the patients showed poor. E. faecalis was significantly more frequent microorganism, followed by E. faecium. The use of broadspectrum antimicrobial action was associated with the presence of these opportunistic microorganisms. These bacteria were more frequent in patients with periodontitis or gingivitis. The results showed that enterococci associated with serious infectious processes may originate from resident microbiota of patients and its prevalence is not elevated in healthy individuals.

Saliva contamination by opportunistic microorganisms in drug addiction females

In recent decades there has been a significant increase in the consumption of legal and illegal drugs, and most of such compounds are able to induce dependence and this increase was observed mainly in females. This drug addiction increases susceptibility to several infectious agents, especially opportunistic microorganisms. The objective of this study was to evaluate the occurrence of opportunistic bacteria and yeasts in the mouth of drug addiction patients and non-addicted patients with different periodontal conditions. The study included 50 addiction patients and 200 non-addiction subjects. Intra and extraoral clinical examinations were performed and saliva samples were transferred to saline solution and the presence of members of the family Enterobacteriaceae, genera Enterococcus and Pseudomonas, as well fungi of the genus Candida was evaluated by culture. Samples were cultivated onto selective and non-selective media under aerobic conditions, at 37oC, for 24 -48 h. Identification of selected microorganisms were carried out through biochemical tests. Chi-square test was used to evaluate the data when three or more categories were involved. Higher detection frequencies of Candida species, family Enterobacteriaceae, E. faecalis, Pseudomonas sp. and P. aeruginosa in addiction patients were verified. It was found that patients addicted to both genders showed a higher occurrence of members of the Enterobacteriaceae, which were also associated with bone loss only in patients with drug addiction.

Ocorrência de microrganismos periodontopatogênicos em pacientes com dependência química

Drug addiction won dramatic aspects in terms of its dimensions and the effects that it imposes. These chemical agents are able to reduce the immune reactivity and tissue repair, and enhance microbial aggression, aggravating the destruction of the periodontium and other side effects. This study aimed to evaluate the presence of key periodontal pathogens in the mouth of drug addiction patients, comparing it with individuals who do not exhibit this dependence, as well as assess the influence of oral conditions on the occurrence of such microorganisms. For this purpose, data on systemic health conditions, socioeconomic, patterns of licit or illicit drug consumption of 100 patients with chemical dependency kept in rehabilitation clinics and an equal number of non-dependent patients, who formed the control group were obtained. Intra and extraoral clinical examinations were performed and samples of supragingival and subgingival biofilm, saliva and mucous membranes were collected. The presence of the targeted microorganism was assessed by polymerase chain reaction (PCR). It was found that Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola showed close correlation with bone loss and gingival bleeding in drug addiction dependents and control group, but the oral mucous membranes and saliva of addicts showed higher occurrence of these pathogens.

Detección de patrones y anomalías espectrales del terreno mediante espectrometría de imagen de alta resolución : reconocimiento, optimización y evaluación multiescenario

El objeto de esta Tesis doctoral es el desarrollo de una metodologia para la deteccion automatica de anomalias a partir de datos hiperespectrales o espectrometria de imagen, y su cartografiado bajo diferentes condiciones tipologicas de superficie y terreno. La tecnologia hiperespectral o espectrometria de imagen ofrece la posibilidad potencial de caracterizar con precision el estado de los materiales que conforman las diversas superficies en base a su respuesta espectral. Este estado suele ser variable, mientras que las observaciones se producen en un numero limitado y para determinadas condiciones de iluminacion. Al aumentar el numero de bandas espectrales aumenta tambien el numero de muestras necesarias para definir espectralmente las clases en lo que se conoce como Maldicion de la Dimensionalidad o Efecto Hughes (Bellman, 1957), muestras habitualmente no disponibles y costosas de obtener, no hay mas que pensar en lo que ello implica en la Exploracion Planetaria. Bajo la definicion de anomalia en su sentido espectral como la respuesta significativamente diferente de un pixel de imagen respecto de su entorno, el objeto central abordado en la Tesis estriba primero en como reducir la dimensionalidad de la informacion en los datos hiperespectrales, discriminando la mas significativa para la deteccion de respuestas anomalas, y segundo, en establecer la relacion entre anomalias espectrales detectadas y lo que hemos denominado anomalias informacionales, es decir, anomalias que aportan algun tipo de informacion real de las superficies o materiales que las producen. En la deteccion de respuestas anomalas se asume un no conocimiento previo de los objetivos, de tal manera que los pixeles se separan automaticamente en funcion de su informacion espectral significativamente diferenciada respecto de un fondo que se estima, bien de manera global para toda la escena, bien localmente por segmentacion de la imagen. La metodologia desarrollada se ha centrado en la implicacion de la definicion estadistica del fondo espectral, proponiendo un nuevo enfoque que permite discriminar anomalias respecto fondos segmentados en diferentes grupos de longitudes de onda del espectro, explotando la potencialidad de separacion entre el espectro electromagnetico reflectivo y emisivo. Se ha estudiado la eficiencia de los principales algoritmos de deteccion de anomalias, contrastando los resultados del algoritmo RX (Reed and Xiaoli, 1990) adoptado como estandar por la comunidad cientifica, con el metodo UTD (Uniform Targets Detector), su variante RXD-UTD, metodos basados en subespacios SSRX (Subspace RX) y metodo basados en proyecciones de subespacios de imagen, como OSPRX (Orthogonal Subspace Projection RX) y PP (Projection Pursuit). Se ha desarrollado un nuevo metodo, evaluado y contrastado por los anteriores, que supone una variacion de PP y describe el fondo espectral mediante el analisis discriminante de bandas del espectro electromagnetico, separando las anomalias con el algortimo denominado Detector de Anomalias de Fondo Termico o DAFT aplicable a sensores que registran datos en el espectro emisivo. Se han evaluado los diferentes metodos de deteccion de anomalias en rangos del espectro electromagnetico del visible e infrarrojo cercano (Visible and Near Infrared-VNIR), infrarrojo de onda corta (Short Wavelenght Infrared-SWIR), infrarrojo medio (Meadle Infrared-MIR) e infrarrojo termico (Thermal Infrared-TIR). La respuesta de las superficies en las distintas longitudes de onda del espectro electromagnetico junto con su entorno, influyen en el tipo y frecuencia de las anomalias espectrales que puedan provocar. Es por ello que se han utilizado en la investigacion cubos de datos hiperepectrales procedentes de los sensores aeroportados cuya estrategia y diseno en la construccion espectrometrica de la imagen difiere. Se han evaluado conjuntos de datos de test de los sensores AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) y MASTER (MODIS/ASTER Simulator). Se han disenado experimentos sobre ambitos naturales, urbanos y semiurbanos de diferente complejidad. Se ha evaluado el comportamiento de los diferentes detectores de anomalias a traves de 23 tests correspondientes a 15 areas de estudio agrupados en 6 espacios o escenarios: Urbano - E1, Semiurbano/Industrial/Periferia Urbana - E2, Forestal - E3, Agricola - E4, Geologico/Volcanico - E5 y Otros Espacios Agua, Nubes y Sombras - E6. El tipo de sensores evaluados se caracteriza por registrar imagenes en un amplio rango de bandas, estrechas y contiguas, del espectro electromagnetico. La Tesis se ha centrado en el desarrollo de tecnicas que permiten separar y extraer automaticamente pixeles o grupos de pixeles cuya firma espectral difiere de manera discriminante de las que tiene alrededor, adoptando para ello como espacio muestral parte o el conjunto de las bandas espectrales en las que ha registrado radiancia el sensor hiperespectral. Un factor a tener en cuenta en la investigacion ha sido el propio instrumento de medida, es decir, la caracterizacion de los distintos subsistemas, sensores imagen y auxiliares, que intervienen en el proceso. Para poder emplear cuantitativamente los datos medidos ha sido necesario definir las relaciones espaciales y espectrales del sensor con la superficie observada y las potenciales anomalias y patrones objetivos de deteccion. Se ha analizado la repercusion que en la deteccion de anomalias tiene el tipo de sensor, tanto en su configuracion espectral como en las estrategias de diseno a la hora de registrar la radiacion prodecente de las superficies, siendo los dos tipos principales de sensores estudiados los barredores o escaneres de espejo giratorio (whiskbroom) y los barredores o escaneres de empuje (pushbroom). Se han definido distintos escenarios en la investigacion, lo que ha permitido abarcar una amplia variabilidad de entornos geomorfologicos y de tipos de coberturas, en ambientes mediterraneos, de latitudes medias y tropicales. En resumen, esta Tesis presenta una tecnica de deteccion de anomalias para datos hiperespectrales denominada DAFT en su variante de PP, basada en una reduccion de la dimensionalidad proyectando el fondo en un rango de longitudes de onda del espectro termico distinto de la proyeccion de las anomalias u objetivos sin firma espectral conocida. La metodologia propuesta ha sido probada con imagenes hiperespectrales reales de diferentes sensores y en diferentes escenarios o espacios, por lo tanto de diferente fondo espectral tambien, donde los resultados muestran los beneficios de la aproximacion en la deteccion de una gran variedad de objetos cuyas firmas espectrales tienen suficiente desviacion respecto del fondo. La tecnica resulta ser automatica en el sentido de que no hay necesidad de ajuste de parametros, dando resultados significativos en todos los casos. Incluso los objetos de tamano subpixel, que no pueden distinguirse a simple vista por el ojo humano en la imagen original, pueden ser detectados como anomalias. Ademas, se realiza una comparacion entre el enfoque propuesto, la popular tecnica RX y otros detectores tanto en su modalidad global como local. El metodo propuesto supera a los demas en determinados escenarios, demostrando su capacidad para reducir la proporcion de falsas alarmas. Los resultados del algoritmo automatico DAFT desarrollado, han demostrado la mejora en la definicion cualitativa de las anomalias espectrales que identifican a entidades diferentes en o bajo superficie, reemplazando para ello el modelo clasico de distribucion normal con un metodo robusto que contempla distintas alternativas desde el momento mismo de la adquisicion del dato hiperespectral. Para su consecucion ha sido necesario analizar la relacion entre parametros biofisicos, como la reflectancia y la emisividad de los materiales, y la distribucion espacial de entidades detectadas respecto de su entorno. Por ultimo, el algoritmo DAFT ha sido elegido como el mas adecuado para sensores que adquieren datos en el TIR, ya que presenta el mejor acuerdo con los datos de referencia, demostrando una gran eficacia computacional que facilita su implementacion en un sistema de cartografia que proyecte de forma automatica en un marco geografico de referencia las anomalias detectadas, lo que confirma un significativo avance hacia un sistema en lo que se denomina cartografia en tiempo real. The aim of this Thesis is to develop a specific methodology in order to be applied in automatic detection anomalies processes using hyperspectral data also called hyperspectral scenes, and to improve the classification processes. Several scenarios, areas and their relationship with surfaces and objects have been tested. The spectral characteristics of reflectance parameter and emissivity in the pattern recognition of urban materials in several hyperspectral scenes have also been tested. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) and MASTER (MODIS/ASTER Simulator) have been used in this research. It is assumed that there is not prior knowledge of the targets in anomaly detection. Thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by the image segmentation. Several experiments on different scenarios have been designed, analyzing the behavior of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. Results and their consequences in unsupervised classification processes are discussed. Detection of spectral anomalies aims at extracting automatically pixels that show significant responses in relation of their surroundings. This Thesis deals with the unsupervised technique of target detection, also called anomaly detection. Since this technique assumes no prior knowledge about the target or the statistical characteristics of the data, the only available option is to look for objects that are differentiated from the background. Several methods have been developed in the last decades, allowing a better understanding of the relationships between the image dimensionality and the optimization of search procedures as well as the subpixel differentiation of the spectral mixture and its implications in anomalous responses. In other sense, image spectrometry has proven to be efficient in the characterization of materials, based on statistical methods using a specific reflection and absorption bands. Spectral configurations in the VNIR, SWIR and TIR have been successfully used for mapping materials in different urban scenarios. There has been an increasing interest in the use of high resolution data (both spatial and spectral) to detect small objects and to discriminate surfaces in areas with urban complexity. This has come to be known as target detection which can be either supervised or unsupervised. In supervised target detection, algorithms lean on prior knowledge, such as the spectral signature. The detection process for matching signatures is not straightforward due to the complications of converting data airborne sensor with material spectra in the ground. This could be further complicated by the large number of possible objects of interest, as well as uncertainty as to the reflectance or emissivity of these objects and surfaces. An important objective in this research is to establish relationships that allow linking spectral anomalies with what can be called informational anomalies and, therefore, identify information related to anomalous responses in some places rather than simply spotting differences from the background. The development in recent years of new hyperspectral sensors and techniques, widen the possibilities for applications in remote sensing of the Earth. Remote sensing systems measure and record electromagnetic disturbances that the surveyed objects induce in their surroundings, by means of different sensors mounted on airborne or space platforms. Map updating is important for management and decisions making people, because of the fast changes that usually happen in natural, urban and semi urban areas. It is necessary to optimize the methodology for obtaining the best from remote sensing techniques from hyperspectral data. The first problem with hyperspectral data is to reduce the dimensionality, keeping the maximum amount of information. Hyperspectral sensors augment considerably the amount of information, this allows us to obtain a better precision on the separation of material but at the same time it is necessary to calculate a bigger number of parameters, and the precision lowers with the increase in the number of bands. This is known as the Hughes effects (Bellman, 1957) . Hyperspectral imagery allows us to discriminate between a huge number of different materials however some land and urban covers are made up with similar material and respond similarly which produces confusion in the classification. The training and the algorithm used for mapping are also important for the final result and some properties of thermal spectrum for detecting land cover will be studied. In summary, this Thesis presents a new technique for anomaly detection in hyperspectral data called DAFT, as a PP's variant, based on dimensionality reduction by projecting anomalies or targets with unknown spectral signature to the background, in a range thermal spectrum wavelengths. The proposed methodology has been tested with hyperspectral images from different imaging spectrometers corresponding to several places or scenarios, therefore with different spectral background. The results show the benefits of the approach to the detection of a variety of targets whose spectral signatures have sufficient deviation in relation to the background. DAFT is an automated technique in the sense that there is not necessary to adjust parameters, providing significant results in all cases. Subpixel anomalies which cannot be distinguished by the human eye, on the original image, however can be detected as outliers due to the projection of the VNIR end members with a very strong thermal contrast. Furthermore, a comparison between the proposed approach and the well-known RX detector is performed at both modes, global and local. The proposed method outperforms the existents in particular scenarios, demonstrating its performance to reduce the probability of false alarms. The results of the automatic algorithm DAFT have demonstrated improvement in the qualitative definition of the spectral anomalies by replacing the classical model by the normal distribution with a robust method. For their achievement has been necessary to analyze the relationship between biophysical parameters such as reflectance and emissivity, and the spatial distribution of detected entities with respect to their environment, as for example some buried or semi-buried materials, or building covers of asbestos, cellular polycarbonate-PVC or metal composites. Finally, the DAFT method has been chosen as the most suitable for anomaly detection using imaging spectrometers that acquire them in the thermal infrared spectrum, since it presents the best results in comparison with the reference data, demonstrating great computational efficiency that facilitates its implementation in a mapping system towards, what is called, Real-Time Mapping.