A framework to foster accessibility in post-pandemic virtual higher education.

The SARS-CoV-2 Coronavirus pandemic (COVID-19) forced educational institutions to move their programmes to the virtual world. Several tech-based solutions -including virtual training and tutoring, discussion forums, access to content and information, collaborative platforms, and Open Educational Resources (OER)- were implemented to address this shift and continue to be used in the post-pandemic era due to the advantages they offer, especially for hybrid and blended learning. However, the implementation of these tech-based solutions also revealed several accessibility issues that need to be addressed to fully leverage the technological benefits.

Attenuated total reflection FTIR dataset for identification of type 2 diabetes using saliva.

Diabetes is one of the top 5 non-communicable diseases that occur worldwide according to the World Health Organization. Despite not being a fatal disease, a late diagnosis as well as poor control can cause a fatal outcome, because of that, several studies have been carried out with the aim of proposing additional techniques to the gold standard to assist in the diagnosis and control of this disease in a non-invasive way. Considering the above, and in order to provide a solid starting point for future researches, we share a primary research dataset with 1040 saliva samples obtained by Fourier Transform Infrared Spectroscopy considering the Attenuated Total Reflectance method.

A machine-learning strategy to evaluate the use of FTIR spectra of saliva for a good control of type 2 diabetes.

The World Health Organization has declared that diabetes is one of the four leading causes of death attributable to non-communicable diseases. Currently, many devices allow monitoring blood glucose levels for diabetes control based mainly on blood tests. In this paper, we propose a novel methodology based on the analysis of the Fourier Transform Infrared (FTIR) spectra of saliva using machine learning techniques to characterize controlled and uncontrolled diabetic patients, clustering patients in groups of a low, medium, and high glucose levels, and finally performing the point estimation of a glucose value.