Influence of Drilling Technique on the Radiographic, Thermographic, and Geomorphometric Effects of Dental Implant Drills and Osteotomy Site Preparations

The aim of this comparative study is to analyze the influence of drilling technique on the radiographic, thermographic, and geomorphometric e ects of dental implant drills and osteotomy site preparations. One hundred and twenty osteotomy site preparations were performed on sixty epoxy resin samples using three unused dental implant drill systems and four drilling techniques performed with a random distribution into the following study groups: Group A: drilling technique performed at 800 rpm with irrigation (n = 30); Group B: drilling technique performed at 45 rpm without irrigation (n = 30); Group C: drilling technique performed at 45 rpm with irrigation (n = 30); and Group D: drilling technique performed at 800 rpm without irrigation (n = 30). The osteotomy site preparation morphologies performed by the 4.1 mm diameter dental implant drills from each study group were analyzed and compared using a cone beam computed tomography (CBCT) scan. The termographic e ects generated by the 4.1 mm diameter dental implant drills from each study group were registered using a termographic digital camera and the unused and 4.1 mm diameter dental implant drills that were used 30 times from each study group were exposed to a micro computed tomography (micro-CT) analysis to obtain a Standard Tessellation Language (STL) digital files that determined the wear comparison by geomorphometry. Statistically significant di erences were observed between the thermographic and radiographic results of the study groups (p < 0.001). The e ect of cooling significatively reduced the heat generation during osteotomy site preparation during high-speed drilling; furthermore, osteotomy site preparation was not a ected by the wear of the dental implant drills after 30 uses, regardless of the drilling technique.Odontologí

Spatio-temporal tumor heterogeneity in metastatic CRC tumors: a mutational-based approach

[EN] It is well known that activating mutations in the KRAS and NRAS genes are associated with poor response to anti-EGFR therapies in patients with metastatic colorectal cancer (mCRC). Approximately half of the patients with wild-type (WT) KRAS colorectal carcinoma do not respond to these therapies. This could be because the treatment decision is determined by the mutational profile of the primary tumor, regardless of the presence of small tumor subclones harboring RAS mutations in lymph nodes or liver metastases. We analyzed the mutational profile of the KRAS, NRAS, BRAF and PI3KCA genes using low-density microarray technology in samples of 26 paired primary tumors, 16 lymph nodes and 34 liver metastases from 26 untreated mCRC patients (n=76 samples). The most frequent mutations found in primary tumors were KRAS (15%) and PI3KCA (15%), followed by NRAS (8%) and BRAF (4%). The distribution of the mutations in the 16 lymph node metastases analyzed was as follows: 4 (25%) in KRAS gene, 3 (19%) in NRAS gene and 1 mutation each in PI3KCA and BRAF genes (6%). As expected, the most prevalent mutation in liver metastasis was in the KRAS gene (35%), followed by PI3KCA (9%) and BRAF (6%). Of the 26 cases studied, 15 (58%) displayed an overall concordance in the mutation status detected in the lymph node metastases and liver metastases compared with primary tumor, suggesting no clonal evolution. In contrast, the mutation profiles differed in the primary tumor and lymph node/metastases samples of the remaining 11 patients (48%), suggesting a spatial and temporal clonal evolution. We confirm the presence of different mutational profiles among primary tumors, lymph node metastases and liver metastases. Our results suggest the need to perform mutational analysis in all available tumor samples of patients before deciding to commence anti-EGFR treatment

Diseño de material de promoción e incremento de impacto del grado en Estudios portugueses y brasileños

Memoria ID12-0082. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2012-2013

ICAP-1 loss impairs CD8+ thymocyte development and leads to reduced marginal zone B cells in mice

ICAP-1 regulates β1-integrin activation and cell adhesion. Here, we used ICAP-1-null mice to study ICAP-1 potential involvement during immune cell development and function. Integrin α4β1-dependent adhesion was comparable between ICAP-1-null and control thymocytes, but lack of ICAP-1 caused a defective single-positive (SP) CD8+ cell generation, thus, unveiling an ICAP-1 involvement in SP thymocyte development. ICAP-1 bears a nuclear localization signal and we found it displayed a strong nuclear distribution in thymocytes. Interestingly, there was a direct correlation between the lack of ICAP-1 and reduced levels in SP CD8+ thymocytes of Runx3, a transcription factor required for CD8+ thymocyte generation. In the spleen, ICAP-1 was found evenly distributed between cytoplasm and nuclear fractions, and ICAP-1–/– spleen T and B cells displayed upregulation of α4β1-mediated adhesion, indicating that ICAP-1 negatively controls their attachment. Furthermore, CD3+- and CD19+-selected spleen cells from ICAP-1-null mice showed reduced proliferation in response to T- and B-cell stimuli, respectively. Finally, loss of ICAP-1 caused a remarkable decrease in marginal zone B- cell frequencies and a moderate increase in follicular B cells. Together, these data unravel an ICAP-1 involvement in the generation of SP CD8+ thymocytes and in the control of marginal zone B-cell numbers

ESCRIVARTE II - pintando o mundo com poesia

Memoria ID-203. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2019-2020

Aplicación de algoritmos genéticos a la optimización de carteras

En este trabajo se ha resuelto el problema de optimización de carteras financieras haciendo uso de optimización multiobjetivo y algoritmos genéticos. Para ello, se comienzan explicando algunos conceptos fundamentales, así como las investigaciones que se han hecho hasta el momento relacionadas con el tema. Entre ellas destacan proyectos muy similares a lo que se quiere implementar en este trabajo, pero analizando datos, tipos de carteras o modelos multiobjetivo muy concretos. Desde este punto de partida, se ha procedido a desarrollar el problema genético y, posteriormente, implementar el código del algoritmo en Python. En el área del problema genético se describen todos los aspectos fundamentales para ser capaz de comprender como funciona el código sin necesidad de leerlo. El fin del algoritmo que se ha creado es encontrar las carteras que consigan maximizar su rentabilidad con el mínimo riesgo posible. Así, la función objetivo a optimizar por el algoritmo va a tener dos componentes que se encuentran en conflicto: maximizar rentabilidad y minimizar riesgo. Es por esto por lo que se habla de problema multiobjetivo, el cual se formula antes de empezar a explicar el algoritmo de forma más concreta. Como bien es característico de la optimización multiobjetivo, la salida principal del algoritmo es un conjunto de Pareto. En este tipo de funciones no solo hay una solución válida, ya que los valores de las variables que obtengan los mejores resultados para cada uno de los objetivos van a ser diferentes. Es por esto por lo que, en lugar de buscar una única solución que genere el mejor valor para un solo objetivo, se va a tratar de hallar un conjunto de soluciones buenas que tengan en cuenta todos los criterios. La idea de óptimo de Pareto define toda situación en la que no es posible optimizar más una función sin perjudicar a la evaluación de otra. Es un punto donde todos los criterios se encuentran en equilibrio. Dadas dos soluciones a y b, si a no es peor que b y, a la vez, b es no es peor que a, entonces a y b son igual de buenas. Todas las soluciones que no son superadas por otras son llamadas soluciones no dominadas. Una solución domina a otra si es mejor o igual en todos los objetivos y mejor en al menos uno de ellos. Así, se entiende como conjunto de Pareto a todas las soluciones no dominadas. Además, el algoritmo también genera una serie de gráficos que muestran cómo han evolucionado las soluciones a lo largo de las distintas iteraciones. Por otro lado, se escogen una serie de datos para utilizar como ejemplo para probar el modelo creado. Todos estos datos son valores históricos de determinados títulos en un periodo de un año. Los datos relativos a los primeros seis meses de dicho año se utilizan para que el algoritmo haga una predicción de cuáles serían las mejores inversiones posibles. Después, los datos correspondientes a los siguientes seis meses se usan para comprobar que hubiera ocurrido si se hubiera seguido alguna de las soluciones encontradas. Una vez se tiene toda esta información acerca de los resultados que se han conseguido con el algoritmo genético, se hace un análisis para evaluar algunos puntos que se habían definido como objetivo del trabajo. Dichos objetivos son los siguientes: 1. Conseguir clasificar las soluciones obtenidas en función de distintos tipos de inversor. 2. Analizar si el índice de Sharpe, uno de los indicadores más utilizados en el ámbito financiero para establecer como de buena es una cartera en función de su rentabilidad y riesgo, discrimina carteras hacia un tipo de perfil de inversor en concreto.---ABSTRACT---In this paper there is solved the problem of financial portfolio optimisation using multi-objective optimization and genetic algorithms. To do so, I begin by explaining some fundamental concepts, as well as the research that has been done so far on the subject. These include projects very similar to what I want to implement in this work, but analysing very specific data, types of portfolios or multi-objective models. From this starting point, the genetic problem has been developed and, subsequently, the algorithm code has been created in Python. In the area of the genetic computing, all the fundamental aspects are described to be able to understand how the code works without having to read it. The aim of the algorithm that has been created is to find the portfolios that manage to maximise their profitability with the minimum possible risk. Thus, the objective function to be optimised by the algorithm will have two conflicting components: maximising profitability and minimising risk. Therefore, I speak of a multiobjective problem, which is formulated before starting to explain the algorithm more in depth. As is characteristic of multi-objective optimisation, the main output of the algorithm is a Pareto set. In this type of functions there is not only one valid solution, since the values of the variables that obtain the best results for each of the objectives will be different. Therefore, instead of looking for a single solution that generates the best value for a single objective, I will try to find a set of good solutions that consider all the criteria. The idea of Pareto optimum defines any situation in which it is not possible to further optimise one function without impairing the evaluation of another. It is a point where all criteria are in equilibrium. Given two solutions a and b, if a is not worse than b and, at the same time, b is not worse than a, then a and b are equally good. All solutions that are not outperformed by others are called non-dominated solutions. A solution dominates another if it is better or equal in all objectives and better in at least one of them. Thus, a Pareto set is understood as all non-dominated solutions. In addition, the algorithm also generates a series of graphs that show how the solutions have evolved throughout the different iterations. Furthermore, a set of data has been picked to be used as an example to test the model. All these data are historical values of certain securities over a period of one year. The data for the first six months of that year are used for the algorithm to make a prediction of what would be the best possible investments. The data of the next six months is then used to see what would have happened if any of the solutions found had been followed. Once the results achieved with the genetic algorithm are available, an analysis has been carried out to evaluate some of the points that had been defined as the objective of the work. These objectives are as follows: 1. To be able to classify the solutions obtained according to different types of inverters. 2. To analyse whether the Sharpe ratio discriminates portfolios towards a particular type of investor profile. This index is one of the most widely used indicators in the financial sphere to establish how good a portfolio is in terms of its profitability and risk

Análisis de la metodología empleada para obtener el adhesivo utilizado en los electrodos de un catéter pulmonar, su desempeño y una posible mejora

Proyecto de graduación (licenciatura en ingeniería química)UCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería Químic

Influence of the Digital Mock-Up and Experience on the Ability to Determine the Prosthetically Correct Dental Implant Position during Digital Planning: An In Vitro Study

The purpose of this study was to analyze the influence of the digital mock-up and operator experience on the dental implant planning position. A total of 200 dental implants were planned, which were distributed into two study groups: A. dental implant planning by dental surgeons with 5&ndash;10 years of experience (n = 80); and B. dental implant planning by dental surgery students without experience (n = 120). Operators were required to plan eight dental implants in the same maxillary edentulous case uploaded in 3D implant-planning software, before and after using the digital mock-up. Deviations between the dental implant planning positions before and after using the digital mock-up were analyzed at platform, apical and angular levels, and were analyzed using a 3D implant-planning software using Student&rsquo;s t test. The paired t-test revealed statistically significant differences between the deviation levels of participants with 5&ndash;10 years&rsquo; experience and no experience at the platform, apical and angular levels. Digital mock-ups allow for more accurate dental implant planning regardless of the experience of the operator. Nevertheless, they are more useful for operators without dental surgery experience

Accuracy of Computer-Aided Dynamic Navigation Compared to Computer-Aided Static Navigation for Dental Implant Placement: An In Vitro Study

Aim: To analyze the accuracy capability of two computer-aided navigation procedures for dental implant placement. Materials and Methods: A total of 40 dental implants were selected, which were randomly distributed into two study groups, namely, group A, consisting of those implants that were placed using a computer-aided static navigation system (n = 20) (guided implant (GI)) and group B, consisting of those implants that were placed using a computer-aided dynamic navigation system (n = 20) (navigation implant (NI)). The placement of the implants from group A was performed using surgical templates that were designed using 3D implant-planning software based on preoperative cone-beam computed tomography (CBCT) and a 3D extraoral surface scan, and the placement of group B implants was planned and performed using the dynamic navigation system. After placing the dental implants, a second CBCT was performed and the degree of accuracy of the planning and placement of the implants was analyzed using therapeutic planning software and Student&rsquo;s t-test. Results: The paired t-test revealed no statistically significant differences between GI and NI at the coronal (p = 0.6535) and apical (p = 0.9081) levels; however, statistically significant differences were observed between the angular deviations of GI and NI (p = 0.0272). Conclusion: Both computer-aided static and dynamic navigation procedures allow accurate implant placement