Concordance and prevalence of subclinical atherosclerosis in different vascular territories.

Background: Subclinical atherosclerosis (SA) in the carotid, femoral, and coronary territories is a powerful predictor of cardiovascular (CV) events. Whether it is sufficient to assess SA in a single vascular territory in early-stage disease is uncertain. We aimed to determine the prevalence and concordance of SA in these vascular beds in asymptomatic patients without known CV disease.

[Comparison of risk scores to predict subclinical atherosclerosis].

Subclinical atherosclerosis is a powerful predictor of cardiovascular events, although it is unknown which of the risk scores is more useful to predict its presence in a Latin American population. The objective was to compare the performance of the risk scores: Framingham, Regicor and Atherosclerotic Cardiovascular Disease Risk Estimator to predict the presence of subclinical atherosclerosis in asymptomatic persons without known cardiovascular disease; as well as determining its prevalence and distribution in the different vascular beds. From 2014 to 2017, patients from 35 to 75 years, asymptomatic and without known cardiovascular disease who underwent a carotid and femoral Doppler echo and calcium score were evaluated.

Giant cells lepromatous leprosy. Diffuse dermatitis with exuberant foreign body giant cells in treated lepromatous leprosy.

Patients with lepromatous leprosy that have received treatment for many years usually get follow up biopsies for persistent skin lesions or positive bacilloscopy even if the values are lower than in the initial bacilloscopy. We report the case of a 48-year old woman with long-standing lepromatous leprosy of 15 years of evolution, with a bacterial index of 4 in the direct smear and the initial skin biopsy. The patient was treated with multidrug therapy for 32 months although the treatment recommended by the World Health Organization (WHO) is only for 12 months.

Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features.

Computational histomorphometric approaches typically use low-level image features for building machine learning classifiers. However, these approaches usually ignore high-level expert knowledge. A computational model (M_im) combines low-, mid-, and high-level image information to predict the likelihood of cancer in whole slide images.

Clinical practice of ambulatory versus home blood pressure monitoring in hypertensive patients.

Objectives: This study aimed to analyze whether blood pressure (BP) measurement is concordant between ambulatory blood pressure monitoring (ABPM) and home blood pressure monitoring (HBPM), and determine whether the decision on treatment changes is similar on the basis of information provided by both methods.

Methods: Treated hypertensive patients were studied with ABPM and HBPM to evaluate therapeutic efficacy and/or diagnose resistant hypertension (HTN). Modification of pharmacological treatment was decided on the basis of pre-established criteria; therefore, the number of therapeutic changes between both techniques was compared.

An unsupervised feature learning framework for basal cell carcinoma image analysis.

Objective: The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model.

Materials And Methods: This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model.

[Middle ear adenoma].

Middle ear neoplasms are rare lesions and difficult to diagnose due to limited information about their biology and the lack of standard criteria for their analysis. Herein, a middle ear neoplasm is described that became apparent because of its appearance in the external ear duct as it protruded from the middle ear through the eardrum. Following resection, the specimen was determined to be a benign epithelial tumor.