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

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

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

An Equilibrium State Diagram for Storage Stability and Conservation of Active Ingredients in a Functional Food Based on Polysaccharides Blends

A functional food as a matrix based on a blend of carbohydrate polymers (25% maltodextrin and 75% inulin) with quercetin and Bacillus claussi to supply antioxidant and probiotic properties was prepared by spray drying. The powders were characterized physiochemically, including by moisture adsorption isotherms, X-ray diffraction (XRD), scanning electron microscopy (SEM), and modulated differential scanning calorimetry (MDSC). The type III adsorption isotherm developed at 35 °C presented a monolayer content of 2.79 g of water for every 100 g of dry sample. The microstructure determined by XRD presented three regions identified as amorphous, semicrystalline, and crystalline-rubbery states. SEM micrographs showed variations in the morphology according to the microstructural regions as (i) spherical particles with smooth surfaces, (ii) a mixture of spherical particles and irregular particles with heterogeneous surfaces, and (iii) agglomerated irregular-shape particles. The blend’s functional performance demonstrated antioxidant activities of approximately 50% of DPPH scavenging capacity and viability values of 6.5 Log10 CFU/g. These results demonstrated that the blend displayed functional food behavior over the complete interval of water activities. The equilibrium state diagram was significant for identifying the storage conditions that promote the preservation of functional food properties and those where the collapse of the microstructure occurs

Cytotoxicity of Protein-Carbon Nanotubes on J774 Macrophages Is a Functionalization Grade-Dependent Effect

Carbon nanotubes (CNTs) are used as carriers in medicine due to their ability to be functionalized with chemical substances. However, cytotoxicity analysis is required prior to use for in vivo models. The aim of this study was to evaluate the cytotoxic effect of CNTs functionalized with a 46 kDa surface protein from Entamoeba histolytica (P46-CNTs) on J774A macrophages. With this purpose, CNTs were synthesized by spray pyrolysis and purified (P-CNTs) using sonication for 48 h. A 46 kDa protein, with a 4.6–5.4 pI range, was isolated from E. histolytica HM1:IMSS strain trophozoites using an OFFGEL system. The P-CNTs were functionalized with the purified 46 kDa protein, classified according to their degree of functionalization, and characterized by Raman and Infrared spectroscopy. In vitro cytotoxicity was evaluated by MTT, apoptosis, and morphological assays. The results demonstrated that P46-CNTs exhibited cytotoxicity dependent upon the functionalized grade. Contrary to what was expected, P46-CNTs with a high grade of functionalization were more toxic to J774 macrophages than P46-CNTs with a low grade of functionalization, than P-CNTs, and had a similar level of toxicity as UP-CNT. This suggests that the nature of the functionalized protein plays a key role in the cytotoxicity of these nanoparticles