Chemistry of water-soluble N-heterocyclic carbene platinum complexes

Water-soluble N-Heterocyclic Carbene (NHC) complexes of transition metal are currently captivated increasing attention because of a number of potential applications, such as biphasic catalysis. However, their chemical behavior in aqueous phase has been virtually unexplored so far. This PhD Dissertation concerns fundamental studies on the synthesis and reactivity of sulfonated NHC platinum complexes in water as a solvent, with special emphasis on the hydrolytic stability of Pt?C bonds. Several efficient protocols for their syntheses, and their limitations, have been uncover. The new complexes readily undergo a rich variety of different transformations (e.g., protonolysis, ligand substitution, oxidative addition/reductive elimination, controlled formation of water-soluble and stable nanoparticles, intramolecular C?H bond activations through a singular pathway, etc), and behave as convenient catalysts in the model processes tested here. The great majority of these reactions are quite straightforward and stereoselective, and in all of them ?and under a wide range of conditions (pH, temperature), the Pt?NHC bonds have been found to be fairly robust in aqueous medium. Thus, the presence of the sulfonated ligand, not only provides water-solubility, but also stability to the diversity of complexes studied in this solvent (halide, methyl, hydridealkynyl, ?-alkene, etc., derivatives), and to the new platinum nanoparticles presented here as well. It is remarkable that the persistent coordination of the ligand to the surface of the nanoparticles has been ambiguously demonstrated by determining the platinum?C(NHC) coupling constant for the first time for a nano-system. Finally, it is also worth to note that, according to DFT calculations, the remote sulfonate moiety assists intramolecular C?H bond cleavages, reducing considerably the Gibbs activation energy of the process (5?7 kcal/mol in the gas-phase) when compared to a conventional oxidative addition mechanism. The latter might open avenues to practical applications undergoing under friendly operational settings, far from the harsh reaction conditions often required for this kind of transformations

Chemistry of water-soluble N-heterocyclic carbene platinum complexes

Water-soluble N-Heterocyclic Carbene (NHC) complexes of transition metal are currently captivated increasing attention because of a number of potential applications, such as biphasic catalysis. However, their chemical behavior in aqueous phase has been virtually unexplored so far. This PhD Dissertation concerns fundamental studies on the synthesis and reactivity of sulfonated NHC platinum complexes in water as a solvent, with special emphasis on the hydrolytic stability of Pt?C bonds. Several efficient protocols for their syntheses, and their limitations, have been uncover. The new complexes readily undergo a rich variety of different transformations (e.g., protonolysis, ligand substitution, oxidative addition/reductive elimination, controlled formation of water-soluble and stable nanoparticles, intramolecular C?H bond activations through a singular pathway, etc), and behave as convenient catalysts in the model processes tested here. The great majority of these reactions are quite straightforward and stereoselective, and in all of them ?and under a wide range of conditions (pH, temperature), the Pt?NHC bonds have been found to be fairly robust in aqueous medium. Thus, the presence of the sulfonated ligand, not only provides water-solubility, but also stability to the diversity of complexes studied in this solvent (halide, methyl, hydridealkynyl, ?-alkene, etc., derivatives), and to the new platinum nanoparticles presented here as well. It is remarkable that the persistent coordination of the ligand to the surface of the nanoparticles has been ambiguously demonstrated by determining the platinum?C(NHC) coupling constant for the first time for a nano-system. Finally, it is also worth to note that, according to DFT calculations, the remote sulfonate moiety assists intramolecular C?H bond cleavages, reducing considerably the Gibbs activation energy of the process (5?7 kcal/mol in the gas-phase) when compared to a conventional oxidative addition mechanism. The latter might open avenues to practical applications undergoing under friendly operational settings, far from the harsh reaction conditions often required for this kind of transformations

Avances Recientes en la Síntesis de 3,4-Dihidropiran-2-Ones Organocatalizada por N-Carbenos Heterocíclicos

En los últimos años, los carbenos N-heterocíclicos (NHC) han ganado reconocimiento como moléculas versátiles capaces de actuar como organocatalizadores en diversas reacciones, en particular mediante la activación de aldehídos a través de aductos de tipo Breslow. Esta activación organocatalítica ha permitido la producción de numerosas 3,4-dihidropiran-2-onas y derivados relacionados. En esta revisión, ofrecemos una visión general de la producción de 3,4-dihidropiran-2-onas y derivados mediante procesos organocatalíticos con NHCs en los últimos ocho años. Estos procesos implican el uso de una diversa gama de sustratos, catalizadores y condiciones de reacción, que pueden clasificarse en cicloadiciones de tipo [4+2]-y [3+3]-, principalmente dirigidas a sintetizar este esqueleto debido a su actividad biológica y múltiples estereocentros. Estos procesos se escalan hasta la escala del gramo, y los productos resultantes se dirigen a menudo hacia la epimerización y funcionalización para producir moléculas más complejas con potenciales aplicaciones en el campo biológico. Por último, ofrecemos una perspectiva y las direcciones futuras de este tema en la síntesis orgánica.In recent years, N-heterocyclic carbenes (NHC) have gained recognition as versatile molecules capable of acting as organocatalysts in various reactions, particularly through the activation of aldehydes via Breslow-type adducts. This organocatalytic activation has enabled the production of numerous 3,4-dihydropyran-2-ones and related derivatives. In this review, we provide an overview of the production of 3,4-dihydropyran-2-ones and derivatives via organocatalytic processes involving NHCs over the past eight years. These processes involve the use of a diverse range of substrates, catalysts, and reaction conditions, which can be classified into [4+2]-and [3+3]-type cycloadditions, primarily aimed at synthesizing this skeleton due to its biological activity and multiple stereocenters. These processes are scaled up to the gram scale, and the resulting products are often directed towards epimerization and functionalization to produce more complex molecules with potential applications in the biological field. Finally, we provide a perspective and the future directions of this topic in organic synthesis

Methodology for automatic classification of atypical lymphoid cells from peripheral blood cell images

Morphological analysis is the starting point for the diagnostic approach of more than 80% of the hematological diseases. However, the morphological differentiation among different types of abnormal lymphoid cells in peripheral blood is a difficult task, which requires high experience and skill. Objective values do not exist to define cytological variables, which sometimes results in doubts on the correct cell classification in the daily hospital routine. Automated systems exist which are able to get an automatic preclassification of the normal blood cells, but fail in the automatic recognition of the abnormal lymphoid cells. The general objective of this thesis is to develop a complete methodology to automatically recognize images of normal and reactive lymphocytes, and several types of neoplastic lymphoid cells circulating in peripheral blood in some mature B-cell neoplasms using digital image processing methods. This objective follows two directions: (1) with engineering and mathematical background, transversal methodologies and software tools are developed; and (2) with a view towards the clinical laboratory diagnosis, a system prototype is built and validated, whose input is a set of pathological cell images from individual patients, and whose output is the automatic classification in one of the groups of the different pathologies included in the system. This thesis is the evolution of various works, starting with a discrimination between normal lymphocytes and two types of neoplastic lymphoid cells, and ending with the design of a system for the automatic recognition of normal lymphocytes and five types of neoplastic lymphoid cells. All this work involves the development of a robust segmentation methodology using color clustering, which is able to separate three regions of interest: cell, nucleus and peripheral zone around the cell. A complete lymphoid cell description is developed by extracting features related to size, shape, texture and color. To reduce the complexity of the process, a feature selection is performed using information theory. Then, several classifiers are implemented to automatically recognize different types of lymphoid cells. The best classification results are achieved using support vector machines with radial basis function kernel. The methodology developed, which combines medical, engineering and mathematical backgrounds, is the first step to design a practical hematological diagnosis support tool in the near future.Los análisis morfológicos son el punto de partida para la orientación diagnóstica en más del 80% de las enfermedades hematológicas. Sin embargo, la clasificación morfológica entre diferentes tipos de células linfoides anormales en la sangre es una tarea difícil que requiere gran experiencia y habilidad. No existen valores objetivos para definir variables citológicas, lo que en ocasiones genera dudas en la correcta clasificación de las células en la práctica diaria en un laboratorio clínico. Existen sistemas automáticos que realizan una preclasificación automática de las células sanguíneas, pero no son capaces de diferenciar automáticamente las células linfoides anormales. El objetivo general de esta tesis es el desarrollo de una metodología completa para el reconocimiento automático de imágenes de linfocitos normales y reactivos, y de varios tipos de células linfoides neoplásicas circulantes en sangre periférica en algunos tipos de neoplasias linfoides B maduras, usando métodos de procesamiento digital de imágenes. Este objetivo sigue dos direcciones: (1) con una orientación propia de la ingeniería y la matemática de soporte, se desarrollan las metodologías transversales y las herramientas de software para su implementación; y (2) con un enfoque orientado al diagnóstico desde el laboratorio clínico, se construye y se valida un prototipo de un sistema cuya entrada es un conjunto de imágenes de células patológicas de pacientes analizados de forma individual, obtenidas mediante microscopía y cámara digital, y cuya salida es la clasificación automática en uno de los grupos de las distintas patologías incluidas en el sistema. Esta tesis es el resultado de la evolución de varios trabajos, comenzando con una discriminación entre linfocitos normales y dos tipos de células linfoides neoplásicas, y terminando con el diseño de un sistema para el reconocimiento automático de linfocitos normales y reactivos, y cinco tipos de células linfoides neoplásicas. Todo este trabajo involucra el desarrollo de una metodología de segmentación robusta usando agrupamiento por color, la cual es capaz de separar tres regiones de interés: la célula, el núcleo y la zona externa alrededor de la célula. Se desarrolla una descripción completa de la célula linfoide mediante la extracción de descriptores relacionados con el tamaño, la forma, la textura y el color. Para reducir la complejidad del proceso, se realiza una selección de descriptores usando teoría de la información. Posteriormente, se implementan varios clasificadores para reconocer automáticamente diferentes tipos de células linfoides. Los mejores resultados de clasificación se logran utilizando máquinas de soporte vectorial con núcleo de base radial. La metodología desarrollada, que combina conocimientos médicos, matemáticos y de ingeniería, es el primer paso para el diseño de una herramienta práctica de soporte al diagnóstico hematológico en un futuro cercano

Lunesta

Hasta ahora, el cine solo puede ver a Colombia como un país lleno de problemáticas materiales que suceden una y otra vez, en el trópico de las regiones, ignorando por completo nichos valiosos, e historias escondidas que pueden develar estéticas y sensibilidades nuevas que aporten a la construcción de una memoria visual verídica, variada y de buena calidad. Sobre todo con contenidos que hablen de la riqueza y la complejidad de todo lo que es Colombia. Lo que leerán a continuación, es la sustentación teórica y práctica de lo que fue para el equipo del proyecto “Lunesta” llevar a cabo la realización de este cortometraje, donde se pretende mostrar, por medio de un marco metodológico, todas las etapas de desarrollo para realizar la película. Aquí se presenta el registro escrito y visual de todo lo que fue el trabajo de mesa, donde se llevó a cabo junto con el departamento de producción y dirección, las propuestas de fotografía, arte, sonido y música. Las propuestas de cada departamento se dieron con base a la discusión de la historia, con lecturas de guión grupales e individuales. Las versiones de guión, los bocetos de arte, sonido, fotografía y el planeamiento estructural de la película desde la producción. Todo se hizo apuntando a reflejar la esencia de la historia que se escribió en un principio. Lo que verán aquí, es toda la intención artística de un equipo de realización, y todo lo que se requiere técnicamente para llegar a realizar un cortometraje

Characterization and automatic screening of reactive and abnormal neoplastic B lymphoid cells from peripheral blood

The objective was to advance in the automatic, image-based, characterization and recognition of a heterogeneous set of lymphoid cells from peripheral blood, including normal, reactive, and five groups of abnormal lymphocytes: hairy cells, mantle cells, follicular lymphoma, chronic lymphocytic leukemia, and prolymphocytes. Methods: A number of 4389 images from 105 patients were selected by pathologists, based on morphologic visual appearance, from patients whose diagnosis was confirmed by all the remaining complementary tests. Besides geometry, new color and texture features were extracted using six alternative color spaces to obtain rich information to characterize the cell groups. The recognition system was designed using support vector machines trained with the whole image set. Results: In the experimental tests, individual sets of images from 21 new patients were analyzed by the trained recognition system and compared with the true diagnosis. An overall recognition accuracy of 97.67% was achieved when the cell screening was performed into three groups: normal lymphocytes, abnormal lymphoid cells, and reactive lymphocytes. The accuracy of the whole experimental study was 91.23% when considering the further discrimination of the abnormal lymphoid cells into the specific five groups. Conclusion: The excellent automatic screening of the three groups of normal, reactive, and abnormal lymphocytes is useful as it discriminates between malignancy and not malignancy. The discrimination of the five groups of abnormal lymphoid cells is encouraging toward the idea that the system could be an automated image-based screening method to identify blood involvement by a variety of B lymphomas.Preprin

Optimizing morphology through blood cell image analysis

Introduction Morphological review of the peripheral blood smear is still a crucial diagnostic aid as it provides relevant information related to the diagnosis and is important for selection of additional techniques. Nevertheless, the distinctive cytological characteristics of the blood cells are subjective and influenced by the reviewer's interpretation and, because of that, translating subjective morphological examination into objective parameters is a challenge. Methods The use of digital microscopy systems has been extended in the clinical laboratories. As automatic analyzers have some limitations for abnormal or neoplastic cell detection, it is interesting to identify quantitative features through digital image analysis for morphological characteristics of different cells. Result Three main classes of features are used as follows: geometric, color, and texture. Geometric parameters (nucleus/cytoplasmic ratio, cellular area, nucleus perimeter, cytoplasmic profile, RBC proximity, and others) are familiar to pathologists, as they are related to the visual cell patterns. Different color spaces can be used to investigate the rich amount of information that color may offer to describe abnormal lymphoid or blast cells. Texture is related to spatial patterns of color or intensities, which can be visually detected and quantitatively represented using statistical tools. Conclusion This study reviews current and new quantitative features, which can contribute to optimize morphology through blood cell digital image processing techniques.Peer ReviewedPostprint (published version

Image processing and machine learning in the morphological analysis of blood cells

Introduction: This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. Methods: The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. Results: There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Conclusion: Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies.Peer ReviewedPostprint (published version

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks

Background and Objectives: Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-hematological diseases. However, the variability in staining protocols across different laboratories can affect the color of images and performance of automatic recognition models. The objective of this work is to develop, train and evaluate a new system for the normalization of color staining of peripheral blood cell images, so that it transforms images from different centers to map the color staining of a reference center (RC) while preserving the structural morphological features. Methods: The system has two modules, GAN1 and GAN2. GAN1 uses the PIX2PIX technique to fade original color images to an adaptive gray, while GAN2 transforms them into RGB normalized images. Both GANs have a similar structure, where the generator is a U-NET convolutional neural network with ResNet and the discriminator is a classifier with ResNet34 structure. Digitally stained images were evaluated using GAN metrics and histograms to assess the ability to modify color without altering cell morphology. The system was also evaluated as a pre-processing tool before cells undergo a classification process. For this purpose, a CNN classifier was designed for three classes: abnormal lymphocytes, blasts and reactive lymphocytes. Results: Training of all GANs and the classifier was performed using RC images, while evaluations were conducted using images from four other centers. Classification tests were performed before and after applying the stain normalization system. The overall accuracy reached a similar value around 96% in both cases for the RC images, indicating the neutrality of the normalization model for the reference images. On the contrary, it was a significant improvement in the classification performance when applying the stain normalization to the other centers. Reactive lymphocytes were the most sensitive to stain normalization, with true positive rates (TPR) increasing from 46.3% - 66% for the original images to 81.2% - 97.2% after digital staining. Abnormal lymphocytes TPR ranged from 31.9% - 95.7% with original images to 83% - 100% with digitally stained images. Blast class showed TPR ranges of 90.3% - 94.4% and 94.4% - 100%, for original and stained images, respectively. Conclusions: The proposed GAN-based normalization staining approach improves the performance of classifiers with multicenter data sets by generating digitally stained images with a quality similar to the original images and adaptability to a reference staining standard. The system requires low computation cost and can help improve the performance of automatic recognition models in clinical settings.This work is part of a research project funded by the Ministry of Science and Innovation of Spain, with reference PID2019-104087RB-I00.Peer ReviewedPostprint (published version

Migración parental y salud en Colombia y Perú Tendencias de la literatura publicada entre los años 2000 y 2012

Objetivo General: Identificar las tendencias sobre migración parental y salud en Colombia y Perú de la literatura publicada entre los años 2000 y 2012. Objetivos Específicos: Describir las características de los documentos estudiados, relacionada con MP (migración parental) y DSS (determinantes sociales de salud) estructurales e intermedios.General Objective: To identify trends about parental migration and health in Colombia and Peru from the literature published between the years 2000 and 2012. Specific Objectives: To describe the characteristics of the studied documents related to PM (parental migration) and SDH (social determinants of health) estructurals and intermediaries.Enfermero (a)Pregrad