Carga inmediata con rehabilitación definitiva en maxilar inferior: reporte de caso

ResumenEl objetivo de este artículo es presentar un protocolo simplificado para la instalación inmediata de una prótesis definitiva sobre 4 implantes mandibulares. Fue realizado en una paciente de sexo femenino de 72 años, desdentada total.Se inició el tratamiento con la confección de una prótesis removible convencional hasta la prueba de articulación dentaria, a partir de lo cual se obtuvo una guía multifuncional.En la cirugía se instalaron 4 implantes Strong SW (SIN®, Sao Paulo, Brasil) entre agujeros mentonianos. Se atornillaron pilares Mini-Abutment y se tomó la impresión utilizando la guía.Al segundo día se probó la estructura metálica con el enfilado dentario en cera y al tercer día se realizó la instalación de la prótesis terminada con torque manual.A los 10 días se retiró la sutura y se dio torque a los tornillos protésicos (10N/cm).La paciente ha asistido a controles, sin presentar complicaciones hasta este momento.AbstractThe aim of this paper is to present a simplified protocol for immediate installation of a definitive prosthesis over four jaw implants. This was performed on a totally edentulous female patient of 72 years.The treatment was initiated with the preparation of a conventional removable prosthesis until the dental articulation test from which a multifunctional guide was obtained.At surgery, four Strong SW (SIN®, Sao Paulo, Brazil) implants were installed between mental foramen. Mini-Abutments were screwed and the impression was made using the guide.On the second day the metal structure was tested with the dental articulation in wax and on the third day the definitive prosthesis was installed with manual torque.The suture was removed 10 days after surgery, and the torque was given to prosthetic screws (10N/cm).The patient was followed up every 6 months, with no complications so far

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area

Color en odontología

Tesis (Magíster en Odontología, Mención en Odontología Restauradora)Para reestablecer las características naturales de los dientes mediante restauraciones artificiales, es necesaria la comprensión de algunos elementos básicos que deben ser incorporados en la rehabilitación. Dentro de ellos, el color de los dientes aparece como la característica más compleja de reproducir, debido a que intervienen conceptos pertenecientes a áreas tan disímiles como la física, biología, psicología, mineralogía, medicina y estética. La exactitud en la reproducción del color va más allá de la mera repetición de un tono perteneciente a una guía de color ... es más bien un proceso basado en el conocimiento científico y en la apreciación artística. Desde los tiempos de los griegos, surgían interrogantes relativas al color y la visión: ¿por qué vemos los objetos?, ¿qué es la luz?, y lo que nos preocupa, ¿por qué y cómo se forman los colores?. Platón creía que nuestros ojos emitían partículas que alcanzaban los objetos y a través de las cuales nos era posible ver. Para Aristóteles la luz era un material que "fluía" entre nuestros ojos y los objetos que observamos. Posteriormente, y con el transcurso de la historia, muchos investigadores han estudiado el fenómeno de percepción visual. Así Newton, Huyghens, Young y Maxwell, desarrollaron nuevos conceptos que darían forma a las teorías que actualmente rigen la física de la luz y el color. Para entender el porqué del color de los dientes, y con ello conseguir su adecuada reproducción, es necesario comenzar por definir qué es el color, analizando en primera instancia los conceptos físicos y biológicos relacionados a él. El término color es entendido comúnmente como un atributo de la percepción ligado a los objetos o la luz, es decir, que atribuimos un color específico a un determinado objeto o luz. Si bien, la definición del color cambia de acuerdo al campo del conocimiento sobre el cuál se hable (pintura, diseño, filosofía, física), siempre se ven involucrados la luz y la visión

A Novel Technique for Bulk-Fill Resin-Based Restorations: Achieving Function and Esthetics in Posterior Teeth

Advances in the mechanical properties of composite resins have allowed for their use in posterior teeth. Conventional resins have several problems associated with polymerization shrinkage stress. The development of “bulk-fill” resins has allowed for their use in single increments up to depths of 4 mm, with very low polymerization shrinkage stress. Nevertheless, differences in anatomy and the desire for optimal esthetics present unique difficulties. This article describes a step-by-step technique using flowable bulk-fill resin as a substitute for dentin in a single increment, together with a high-reflective-index resin to restore enamel and decrease clinical time, obtaining anatomically and esthetically acceptable results without detriment to the mechanical properties required to restore the functionality of the posterior teeth

A Step-by-Step Conservative Approach for CAD-CAM Laminate Veneers

The use of CAD/CAM technology has allowed the fabrication of ceramic restorations efficiently and with predictable results. Lithium disilicate is a type of glass ceramic material that can be used for the elaboration of laminate veneers, being monolithic restorations which require characterization through a covering ceramic in order to achieve acceptable esthetic results. The next case report shows a predictable clinical protocol for the rehabilitation of the anterior teeth through the preparation of CAD/CAM veneers (e.max CAD, Ivoclar Vivadent, Liechtenstein) which have been characterized by a nanofluorapatite ceramic (e.max Ceram, Ivoclar Vivadent, Liechtenstein) through the layering technique

Comparative Reduction of Egg Yolk Cholesterol Using Anionic Chelating Agents

Egg yolk is used as an emulsifying agent. Nevertheless, its high concentration of cholesterol is linked to chronic degenerative diseases that cause cardiovascular disease. In this study, three methods for reducing the level of cholesterol in egg yolks were studied. The first method consisted of physical separation of the granules contained in the yolk (NaG). The second method applied was the use of anionic chelating biopolymers, such as arabic gum solution (AG) and mesquite gum solution (MG), and the third method was extraction with a solvent (SA). For this purpose, the cholesterol present in egg yolks, the microstructure, particle size, zeta potential, and its emulsifying capacity were determined. The amount of cholesterol removed was 97.24% using 1% mesquite gum (MG1%), and 93.26% using 1% Arabic gum (AG1%). The zeta potential was determined, and the isoelectric point (ζ = 0) of egg yolk was identified as pH 4.6. While, at this pH, the zeta potential of mesquite gum was −14.8 mV, the zeta potential for the arabic gum was −16 mV. The emulsifying capacity of MG1% was 62.95%, while the emulsifying capacity of AG1% was 63.57%. The complex obtained can be used in the development of functional foods reduced in cholesterol

Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes