Atypical disseminated histiocytic sarcoma in a 7-month-old dog.

A 7-month-old intact female bearded collie dog was admitted after a 2-week history of progressive cough, inappetence, and lethargy, with no response to previous treatment with doxycycline and steroids. Mild attenuation of lung sounds in the right middle hemithorax was the only abnormality detected on physical examination. Abdominal ultrasound and thoracic radiographs were performed and revealed multifocally distributed nodules and masses, well-circumscribed and of variable size in the kidneys and pulmonary parenchyma.

COVID-19 and the Americans with Disabilities Act: Implications for employment and higher education.

Background: The Americans with Disabilities Act (ADA) is a federal law that protects individuals with disabilities from discrimination in all areas of public life. The ADA contributes to equal opportunity across policy areas, including the interconnected domains of higher education and employment. Since the onset of the COVID-19 pandemic in 2020, emerging research has begun to document the disparities in impact on people with disabilities, among other marginalized groups.

Dataset of fundus images for the diagnosis of ocular toxoplasmosis.

Toxoplasmosis chorioretinitis is commonly diagnosed by an ophthalmologist through the evaluation of the fundus images of a patient. Early detection of these lesions may help to prevent blindness. In this article we present a data set of fundus images labeled into three categories: healthy eye, inactive and active chorioretinitis.

Brief report: Evaluation of an adapted youth version of Parents Taking Action for parents of pre/adolescents with autism spectrum disorder in Colombia.

We evaluated the efficacy of the youth version of the program Parents Taking Action in Bogota, Colombia. This program aims to provide information, resources, and strategies about topics of puberty, sexuality, and adolescence for parents of preadolescents with autism spectrum disorder. We examined whether parents in the treatment groups would improve in levels of knowledge, empowerment, self-efficacy, and use of strategies compared to the control group.

Ethnic discrimination, acculturative stress, and sexual risk among Latinx emerging adults: Examining moderation effects of familism support and ethnic identity.

Objectives: Ethnic discrimination and acculturative stress play an important role in sexual risk behaviors for Latinx emerging adults, who are at disproportionate risk for sexually transmitted infections. Factors such as familism support and ethnic identity may be protective, yet research is limited. This study is guided by a culturally adapted stress and coping framework to examine associations of ethnic discrimination and acculturative stress with sexual risk behaviors (i.

A systematic review of the application of machine-learning algorithms in multiple sclerosis.

Introduction: The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

Objective: We present a systematic review of the application of ML algorithms in MS.

Review and statistical analysis of clinical management of feline leishmaniosis caused by Leishmania infantum.

Background: There is limited information about feline leishmaniosis (FeL) management in clinical practice. Leishmania infantum is the species of Leishmania most frequently reported in both dogs and cats in countries of the Mediterranean region (henceforth 'Mediterranean countries'), Central and South America, and Iran. This study was conducted to provide veterinary clinicians with an updated overview of evidence-based information on leishmaniosis in cats.

Automatic Diagnosis of Diabetic Retinopathy from Fundus Images Using Neuro-Evolutionary Algorithms.

Due to the presence of high glucose levels, diabetes mellitus (DM) is a widespread disease that can damage blood vessels in the retina and lead to loss of the visual system. To combat this disease, called Diabetic Retinopathy (DR), retinography, using images of the fundus of the retina, is the most used method for the diagnosis of Diabetic Retinopathy. The Deep Learning (DL) area achieved high performance for the classification of retinal images and even achieved almost the same human performance in diagnostic tasks.

Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty.

Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed numerical-categorical, defining, detecting, and measuring interactions is not a simple task. In this work, based on an entropy-based correlation measure for nominal variables (named as Multivariate Symmetrical Uncertainty (MSU)), we propose a formal and broader definition for the interaction of the variables.

Distribution level electric current consumption and meteorological data set of the east region of Paraguay.

This paper presents a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, which belongs to the Dirección Nacional de Aeronáutica Civil of Paraguay. The final data consists of a total of 22.

A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images.

In the automatic diagnosis of ocular toxoplasmosis (OT), Deep Learning (DL) has arisen as a powerful and promising approach for diagnosis. However, despite the good performance of the models, decision rules should be interpretable to elicit trust from the medical community. Therefore, the development of an evaluation methodology to assess DL models based on interpretability methods is a challenging task that is necessary to extend the use of AI among clinicians.

Dataset from fundus images for the study of diabetic retinopathy.

This article presents a database containing 757 color fundus images acquired at the Department of Ophthalmology of the Hospital de Clínicas, Facultad de Ciencias Médicas (FCM), Universidad Nacional de Asunción (UNA), Paraguay. Firstly, the retinal images were acquired with a clinical procedure presented in this paper. The acquisition of the retinographies was made through the Visucam 500 camera of the Zeiss brand.

Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks.

Ocular toxoplasmosis (OT) is commonly diagnosed through the analysis of fundus images of the eye by a specialist. Despite Deep Learning being widely used to process and recognize pathologies in medical images, the diagnosis of ocular toxoplasmosis(OT) has not yet received much attention. A predictive computational model is a valuable time-saving option if used as a support tool for the diagnosis of OT.

A systematic review of the application of machine-learning algorithms in multiple sclerosis.

Introduction: The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

Objective: We present a systematic review of the application of ML algorithms in MS.

Genome-wide prediction of topoisomerase IIβ binding by architectural factors and chromatin accessibility.

DNA topoisomerase II-β (TOP2B) is fundamental to remove topological problems linked to DNA metabolism and 3D chromatin architecture, but its cut-and-reseal catalytic mechanism can accidentally cause DNA double-strand breaks (DSBs) that can seriously compromise genome integrity. Understanding the factors that determine the genome-wide distribution of TOP2B is therefore not only essential for a complete knowledge of genome dynamics and organization, but also for the implications of TOP2-induced DSBs in the origin of oncogenic translocations and other types of chromosomal rearrangements. Here, we conduct a machine-learning approach for the prediction of TOP2B binding using publicly available sequencing data.

Computational Methods for the Analysis of Genomic Data and Biological Processes.

Today, new technologies, such as microarrays or high-performance sequencing, are producing more and more genomic data [...

A Comparative Study of Supervised Machine Learning Algorithms for the Prediction of Long-Range Chromatin Interactions.

The role of three-dimensional genome organization as a critical regulator of gene expression has become increasingly clear over the last decade. Most of our understanding of this association comes from the study of long range chromatin interaction maps provided by Chromatin Conformation Capture-based techniques, which have greatly improved in recent years. Since these procedures are experimentally laborious and expensive, in silico prediction has emerged as an alternative strategy to generate virtual maps in cell types and conditions for which experimental data of chromatin interactions is not available.

Computational Analysis of the Global Effects of in the Immune Response to Coronavirus Infection Using Gene Networks.

Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs.

A multi-objective approach for designing optimized operation sequence on binary image processing.

In binary image segmentation, the choice of the order of the operation sequence may yield to suboptimal results. In this work, we propose to tackle the associated optimization problem via multi-objective approach. Given the original image, in combination with a list of morphological, logical and stacking operations, the goal is to obtain the ideal output at the lowest computational cost.

Detecting Atrial Fibrillation in Patients With an Embolic Stroke of Undetermined Source (from the DAF-ESUS registry).

Atrial fibrillation (AF) causes a substantial proportion of embolic strokes of undeterminded source (ESUS). Effective detection of subclinical AF (SCAF) has important therapeutic implications. We conducted a prospective study to determine the prevalence of SCAF in patients with ESUS through of a 21-day Holter monitoring.

Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach.

Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis.

Patients Discharged with Home Enteral Nutrition from a Third-Level Hospital in 2018.

Patients who, during admission, begin to use enteral nutrition (EN) and do not recover adequate oral intake need proper planning prior to discharge. The present study is a descriptive analysis of patients discharged with EN from our hospital in 2018. In all, the study included 141 patients (50.

Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay.

Early diagnosis of dengue continues to be a concern for public health in countries with a high incidence of this disease. In this work, we compared two machine learning techniques: artificial neural networks (ANN) and support vector machines (SVM) as assistance tools for medical diagnosis. The performance of classification models was evaluated in a real dataset of patients with a previous diagnosis of dengue extracted from the public health system of Paraguay during the period 2012-2016.