Classification and selection of the main features for the identification of toxicity in and with machine learning algorithms.

The occurrence of fungi is cosmopolitan, and while some mushroom species are beneficial to human health, others can be toxic and cause illness problems. This study aimed to analyze the organoleptic, ecological, and morphological characteristics of a group of fungal specimens and identify the most significant features to develop models for fungal toxicity classification using genetic algorithms and LASSO regression. The results of the study indicated that odor, spore print color, and habitat were the most significant characteristics identified by the genetic algorithm GALGO.

Comparative study of convolutional neural network architectures for gastrointestinal lesions classification.

The gastrointestinal (GI) tract can be affected by different diseases or lesions such as esophagitis, ulcers, hemorrhoids, and polyps, among others. Some of them can be precursors of cancer such as polyps. Endoscopy is the standard procedure for the detection of these lesions.

Deep artificial neural network based on environmental sound data for the generation of a children activity classification model.

Children activity recognition (CAR) is a subject for which numerous works have been developed in recent years, most of them focused on monitoring and safety. Commonly, these works use as data source different types of sensors that can interfere with the natural behavior of children, since these sensors are embedded in their clothes. This article proposes the use of environmental sound data for the creation of a children activity classification model, through the development of a deep artificial neural network (ANN).

Demographic and Comorbidities Data Description of Population in Mexico with SARS-CoV-2 Infected Patients(COVID19): An Online Tool Analysis.

The Word Health Organization (WHO) declared in March 2020 that we are facing a pandemic designated as COVID-19, which is the acronym of coronavirus disease 2019, caused by a new virus know as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In Mexico, the first cases of COVID-19, was reported by the Secretary of Health on 28 February 2020. More than sixteen thousand cases and more than fifteen thousand deaths have been reported in Mexico, and it continues to rise; therefore, this article proposes two online visualization tools (a web platform) that allow the analysis of demographic data and comorbidities of the Mexican population.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies.

Nested case-control study reveals increased levels of urinary proteins from human kidney toxicity panels in women predicted to develop preeclampsia.

Purpose: The aim of this study was to evaluate the usefulness of urine concentrations of 12 proteins as a risk parameter for developing preeclampsia (PE).

Methods: A nested case-control study was designed to determine protein concentrations in urine from women predicted to develop PE (WPD-PE) and normotensive pregnancies (controls). Protein profiles were determined at 12, 16 and 20 gestational weeks (GW) using the Bio-Plex Pro human kidney toxicity Panel 1 and Panel 2 (Bio-Rad).