Classification of Leukocytes Using Meta-Learning and Color Constancy Methods

In the human healthcare area, leukocytes are very important blood cells for the diagnosis of different pathologies, like leukemia. Recent technology and image-processing methods have contributed to the image classification of leukocytes. Especially, machine learning paradigms have been used for the classification of leukocyte images. However, reported models do not leverage the knowledge produced by the classification of leukocytes to solve similar tasks. For example, the knowledge can be reused to classify images collected with different types of microscopes and image-processing techniques. Therefore, we propose a meta-learning methodology for the classification of leukocyte images using different color constancy methods involving previous knowledge. Our methodology is trained with a specific task at the meta-level, and the knowledge produced is used to solve a different task at the base-level. For the meta-level, we implemented meta-models based on Xception, and for the base-level, we used support vector machine classifiers. Besides, we analyzed the Shades of Gray color constancy method commonly used in skin lesion diagnosis and now implemented for leukocyte images. Our methodology, at the meta-level, achieved 89.28% for precision, 95.65% for sensitivity, 91.78% for F1-score, and 94.40% for accuracy. These scores are competitive regarding the reported state-of-the-art models, especially the sensitivity which is very important for imbalanced datasets, and our meta-model outperforms previous works by +2.25%. Additionally, for the basophil images that were acquired from a chronic myeloid leukemia-positive sample, our meta-model obtained 100% for sensitivity. Moreover, we present an algorithm that generates a new conditioned output at the base-level obtaining highly competitive scores of 91.56% for sensitivity and F1 scores, 95.61% for precision, and 96.47% for accuracy. The findings indicate that our proposed meta-learning methodology can be applied to other medical image classification tasks and achieve high performances by reusing knowledge and reducing the training time for new similar tasks

Localización de las proteínas específicas del cemento radicular CEMP1 y CAP en células neoplásicas

Introducción: Las proteínas CEMP1 y CAP presentes en los cementoblastos y sus progenitores contribuyen a los procesos de mineralización en tejidos del ligamento periodontal, incluyendo la migración y la proliferación de fibroblastos gingivales; sin embargo su papel y relación con procesos neoplásicos no se han estudiado a profundidad. Para lograr un mejor entendimiento de la posible contribución de estas proteínas en los procesos tumorales, particularmente en las metástasis óseas, se investigó su expresión y localización en tejidos y líneas celulares de cáncer humano. Materiales y métodos: Trece casos de cáncer de próstata y mama que desarrollaron enfermedad metastásica ósea fueron analizados por medio de inmunohistoquímica; mientras que la expresión de las proteínas en dos líneas celulares de carcinoma de próstata (PC-3) y mama (MCF-7) se estudió por medio de ensayos de Western Blot. Resultados: Los tejidos de cáncer revelaron expresión citoplasmática y ocasionalmente nuclear de CAP en células tumorales y estructuras glandulares pequeñas, así como en el citoplasma de los fibroblastos estromales adyacentes al frente de invasión tumoral. En lo correspondiente a CEMP1, su expresión se localizó en el citoplasma de las células tumorales de 5 casos, pero no en el estroma. Ensayos de Wester Blot mostraron expresión de CEMP1 en las células PC-3 y MCF-7; y de CAP en las MCF-7. Conclusiones: Los resultados muestran que las proteínas de cemento radicular CEMP1 y CAP se expresan en tejidos neoplásicos y células neoplásicas, y que posiblemente contribuyen en ciertas condiciones patológicas como el cáncer metastásico en humanos.Palabras clave: CEMP1, CAP, metástasis óseas, TWIST, Runx2.Introduction: CEMP1 and CAP are recognized as cementum proteins, they appear to be limited to cementoblasts and their progenitors, and participate in the mineralization process of periodontal ligament tissues, including the proliferation and migration of periodontal ligament fibroblasts. However, their contribution in neoplastic processes had not been explored. In the present study, we investigated their protein expression and localization in cancer tissues and cells. Materials and Methods: CEMP1 and CAP expressions were analyzed immunohistochemically in 13 cancer cases with bone metastasis. In addition, Wester Blot essays were use to detect expression of the proteins in the prostate (PC-3) and mama (MCF-7) cancer cell lines. Results: CAP expression was detected in all tissues examined. Strong cytoplasmatic and rarely nuclear staining was found in small tumor nests, glandular structures and, in the stromal fibroblasts at the immediate vicinity of the tumor nests. CEMP1 was found in the cytoplasm of tumor cells in 5 cases, but its expression was negative in the stromal tissues. Also, cancer lines PC-3 and MCF-7 showed CEMP1 expression; however, CAP expression was observed only in MCF-7 cells. Conclusions: The results suggest that CEMP1 and CAP are present in tissues other that cementum and possibly contribute to pathological conditions such as metastatic cancer.Keywords: CEMP1, CAP, bone metastasis, TWIST, Runx2

Classification of Leukocytes Using Meta-Learning and Color Constancy Methods

In the human healthcare area, leukocytes are very important blood cells for the diagnosis of different pathologies, like leukemia. Recent technology and image-processing methods have contributed to the image classification of leukocytes. Especially, machine learning paradigms have been used for the classification of leukocyte images. However, reported models do not leverage the knowledge produced by the classification of leukocytes to solve similar tasks. For example, the knowledge can be reused to classify images collected with different types of microscopes and image-processing techniques. Therefore, we propose a meta-learning methodology for the classification of leukocyte images using different color constancy methods involving previous knowledge. Our methodology is trained with a specific task at the meta-level, and the knowledge produced is used to solve a different task at the base-level. For the meta-level, we implemented meta-models based on Xception, and for the base-level, we used support vector machine classifiers. Besides, we analyzed the Shades of Gray color constancy method commonly used in skin lesion diagnosis and now implemented for leukocyte images. Our methodology, at the meta-level, achieved 89.28% for precision, 95.65% for sensitivity, 91.78% for F1-score, and 94.40% for accuracy. These scores are competitive regarding the reported state-of-the-art models, especially the sensitivity which is very important for imbalanced datasets, and our meta-model outperforms previous works by +2.25%. Additionally, for the basophil images that were acquired from a chronic myeloid leukemia-positive sample, our meta-model obtained 100% for sensitivity. Moreover, we present an algorithm that generates a new conditioned output at the base-level obtaining highly competitive scores of 91.56% for sensitivity and F1 scores, 95.61% for precision, and 96.47% for accuracy. The findings indicate that our proposed meta-learning methodology can be applied to other medical image classification tasks and achieve high performances by reusing knowledge and reducing the training time for new similar tasks

Prevalence and factors associated with anxiety and depression symptoms in adults from Chihuahua City, Mexico during COVID-19 pandemic and lockdown measures

Introduction: Levels of anxiety and depression have increased internationally during the COVID-19 pandemic. Objective: To determine the prevalence of anxiety and depression symptoms and identify their associated factors including lockdown measures in the general population over 18 years from Chihuahua, Chihuahua, Mexico during the COVID-19 pandemic. Method: Cross-sectional study, with online survey and snowball sampling. The GAD-7 (anxiety), PHQ-9 (depression) and Likert (social distancing measures) scales were used. Frequencies, measures of central tendency and dispersion were calculated; a bivariate analysis was performed with odds ratio as a measure of association between those with the presence and absence of anxiety and depression symptoms; for the total population and stratifying by sex, calculating the degree of association between the categorical variables using Fisher's exact test and Chi2, considering a p<.05. Results: From 377 participants, 46% had symptoms of anxiety and 43% depressive symptoms. Being a woman, single, young, student, not exercising, smoking, alcohol consumption, practicing social distancing measures, the history of a previous mental disorder or mental health care, were associated with the presence of symptoms of anxiety and/or depression. Discussion and conclusion: High prevalence of anxiety and depression symptoms were found, justifying a follow-up of the mental health of the population. DOI: https://doi.org/10.54167/tecnociencia.v16i1.88

Ciencia Odontológica 2.0

Libro que muestra avances de la Investigación Odontológica en MéxicoEs para los integrantes de la Red de Investigación en Estomatología (RIE) una enorme alegría presentar el segundo de una serie de 6 libros sobre casos clínicos, revisiones de la literatura e investigaciones. La RIE está integrada por cuerpos académicos de la UAEH, UAEM, UAC y UdeG

1er. Coloquio de educación para el diseño en la sociedad 5.0

Las memorias del 1er. Coloquio de Educación para el Diseño en la Sociedad 5.0 debenser entendidas como un esfuerzo colectivo de la comunidad de académicos de la División de Ciencias y Artes para el Diseño, que pone de manifiesto los retos y oportunidades que enfrenta la educación en diseño en un contexto de cambio acelerado y rompimiento de paradigmas.El evento se realizó el pasado mes de mayo de 2018 y se recibieron más de 50 ponencias por parte de las profesoras y profesores de la División.Las experiencias y/o propuestas innovadoras en cuanto a procesos de enseñanza y aprendizaje que presentan los autores en cada uno de sus textos son una invitación a reflexionar sobre nuestra situación actual en la materia, y emprender acciones en la División para continuar brindando una educación de calidad en diseño a nuestras alumnas, alumnos y la sociedad.Adicionalmente, se organizaron tres conferencias magistrales sobre la situación actual de la educación en Diseño y de las Instituciones de Educación Superior, impartidas por el Mtro. Luis Sarale, profesor de la Universidad Nacional de Cuyo en Mendoza (Argentina), y Presidente en su momento, de la Red de Carreras de Diseño en Universidades Públicas Latinoamericanas (DISUR), el Dr. Romualdo López Zárate, Rector de la Unidad Azcapotzalco, así como del Mtro. Luis Antonio Rivera Díaz, Jefe de Departamento de Teoría y Procesos del Diseño de la División de la Ciencias de la Comunicación y Diseño, en la Unidad Cuajimalpa de nuestra institución.La publicación de estas memorias son un esfuerzo divisional, organizado desde la Coordinación de Docencia Divisional y la Coordinación de Tecnologías del Aprendizaje, del Conocimiento y la Comunicación, para contribuir a los objetivos planteados en el documento ACCIONES:Agenda CyAD2021, en particular al eje de Innovación Educativa. Es necesario impulsar a todos los niveles de la División espacios de discusión orientados a reflexionar sobre el presente y futuro en la educación del diseñador, que contribuya a mejorar la calidad de la docencia y favorezca al fortalecimiento de los procesos de enseñanza y aprendizaje.Finalmente, extiendo un amplio reconocimiento a todos los miembros de la División que hicieron posible este evento, así como a todos los ponentes y participantes por compartir su conocimiento para que la División sea cada día mejor

Localización de las proteínas específicas del cemento radicular CEMP1 y CAP en células neoplásicas

Introduction: CEMP1 and CAP are recognized as cementum proteins, they appear to be limited to cementoblasts and their progenitors, and participate in the mineralization process of periodontal ligament tissues, including the proliferation and migration of periodontal ligament fibroblasts. However, their contribution in neoplastic processes had not been explored. In the present study, we investigated their protein expression and localization in cancer tissues and cells. Materials and Methods: CEMP1 and CAP expressions were analyzed immunohistochemically in 13 cancer cases with bone metastasis. In addition, Wester Blot essays were use to detect expression of the proteins in the prostate (PC-3) and mama (MCF-7) cancer cell lines. Results: CAP expression was detected in all tissues examined. Strong cytoplasmatic and rarely nuclear staining was found in small tumor nests, glandular structures and, in the stromal fibroblasts at the immediate vicinity of the tumor nests. CEMP1 was found in the cytoplasm of tumor cells in 5 cases, but its expression was negative in the stromal tissues. Also, cancer lines PC-3 and MCF-7 showed CEMP1 expression; however, CAP expression was observed only in MCF-7 cells. Conclusions: The results suggest that CEMP1 and CAP are present in tissues other that cementum and possibly contribute to pathological conditions such as metastatic.Las proteínas CEMP1 y CAP presentes en los cementoblastos y sus progenitores contribuyen a los procesos de mineralización en tejidos del ligamento periodontal, incluyendo la migración y la proliferación de fibroblastos gingivales; sin embargo su papel y relación con procesos neoplásicos no se han estudiado a profundidad. Para lograr un mejor entendimiento de la posible contribución de estas proteínas en los procesos tumorales, particularmente en las metástasis óseas, se investigó su expresión y localización en tejidos y líneas celulares de cáncer humano. Materiales y métodos: Trece casos de cáncer de próstata y mama que desarrollaron enfermedad metastásica ósea fueron analizados por medio de inmunohistoquímica; mientras que la expresión de las proteínas en dos líneas celulares de carcinoma de próstata (PC-3) y mama (MCF-7) se estudió por medio de ensayos de Western Blot. Resultados: Los tejidos de cáncer revelaron expresión citoplasmática y ocasionalmente nuclear de CAP en células tumorales y estructuras glandulares pequeñas, así como en el citoplasma de los fibroblastos estromales adyacentes al frente de invasión tumoral. En lo correspondiente a CEMP1, su expresión se localizó en el citoplasma de las células tumorales de 5 casos, pero no en el estroma. Ensayos de Wester Blot mostraron expresión de CEMP1 en las células PC-3 y MCF-7; y de CAP en las MCF-7. Los resultados muestran que las proteínas de cemento radicular CEMP1 y CAP se expresan en tejidos neoplásicos y células neoplásicas, y que posiblemente contribuyen en ciertas condiciones patológicas como el cáncer metastásico en humanos

Classification of Leukocytes Using Meta-Learning and Color Constancy Methods

In the human healthcare area, leukocytes are very important blood cells for the diagnosis of different pathologies, like leukemia. Recent technology and image-processing methods have contributed to the image classification of leukocytes. Especially, machine learning paradigms have been used for the classification of leukocyte images. However, reported models do not leverage the knowledge produced by the classification of leukocytes to solve similar tasks. For example, the knowledge can be reused to classify images collected with different types of microscopes and image-processing techniques. Therefore, we propose a meta-learning methodology for the classification of leukocyte images using different color constancy methods involving previous knowledge. Our methodology is trained with a specific task at the meta-level, and the knowledge produced is used to solve a different task at the base-level. For the meta-level, we implemented meta-models based on Xception, and for the base-level, we used support vector machine classifiers. Besides, we analyzed the Shades of Gray color constancy method commonly used in skin lesion diagnosis and now implemented for leukocyte images. Our methodology, at the meta-level, achieved 89.28% for precision, 95.65% for sensitivity, 91.78% for F1-score, and 94.40% for accuracy. These scores are competitive regarding the reported state-of-the-art models, especially the sensitivity which is very important for imbalanced datasets, and our meta-model outperforms previous works by +2.25%. Additionally, for the basophil images that were acquired from a chronic myeloid leukemia-positive sample, our meta-model obtained 100% for sensitivity. Moreover, we present an algorithm that generates a new conditioned output at the base-level obtaining highly competitive scores of 91.56% for sensitivity and F1 scores, 95.61% for precision, and 96.47% for accuracy. The findings indicate that our proposed meta-learning methodology can be applied to other medical image classification tasks and achieve high performances by reusing knowledge and reducing the training time for new similar tasks

Classification of Leukocytes Using Meta-Learning and Color Constancy Methods

In the human healthcare area, leukocytes are very important blood cells for the diagnosis of different pathologies, like leukemia. Recent technology and image-processing methods have contributed to the image classification of leukocytes. Especially, machine learning paradigms have been used for the classification of leukocyte images. However, reported models do not leverage the knowledge produced by the classification of leukocytes to solve similar tasks. For example, the knowledge can be reused to classify images collected with different types of microscopes and image-processing techniques. Therefore, we propose a meta-learning methodology for the classification of leukocyte images using different color constancy methods involving previous knowledge. Our methodology is trained with a specific task at the meta-level, and the knowledge produced is used to solve a different task at the base-level. For the meta-level, we implemented meta-models based on Xception, and for the base-level, we used support vector machine classifiers. Besides, we analyzed the Shades of Gray color constancy method commonly used in skin lesion diagnosis and now implemented for leukocyte images. Our methodology, at the meta-level, achieved 89.28% for precision, 95.65% for sensitivity, 91.78% for F1-score, and 94.40% for accuracy. These scores are competitive regarding the reported state-of-the-art models, especially the sensitivity which is very important for imbalanced datasets, and our meta-model outperforms previous works by +2.25%. Additionally, for the basophil images that were acquired from a chronic myeloid leukemia-positive sample, our meta-model obtained 100% for sensitivity. Moreover, we present an algorithm that generates a new conditioned output at the base-level obtaining highly competitive scores of 91.56% for sensitivity and F1 scores, 95.61% for precision, and 96.47% for accuracy. The findings indicate that our proposed meta-learning methodology can be applied to other medical image classification tasks and achieve high performances by reusing knowledge and reducing the training time for new similar tasks