Two-year death prediction models among patients with Chagas Disease using machine learning-based methods.

Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the high morbidity and mortality rate. Machine Learning (ML) can identify patterns in data that can be used to increase our understanding of a specific problem or make predictions about the future.

Therapeutic perceptions in antisense RNA-mediated gene regulation for COVID-19.

COVID-19 was first reported in Wuhan, China, in December 2019. It is widely accepted that the world will not return to its prepandemic normality until safe and effective vaccines are available and a global vaccination program has been successfully implemented. Antisense RNAs are single-stranded RNAs that occur naturally or are synthetic and enable hybridizing and protein-blocking translation.

Validity and precision of the International Physical Activity Questionnaire for climacteric women using computational intelligence techniques.

This study aimed to evaluate the validity and precision of the International Physical Activity Questionnaire (IPAQ) for climacteric women using computational intelligence techniques. The instrument was applied to 873 women aged between 40 and 65 years. Considering the proposal to regroup the set of data related to the level of physical activity of climacteric women using the IPAQ, we used 2 algorithms: Kohonen and k-means, and, to evaluate the validity of these clusters, 3 indexes were used: Silhouette, PBM and Dunn.

Local tissue electrical parameters predict oral mucositis in HNSCC patients: A diagnostic accuracy double-blind, randomized controlled trial.

Oral Mucositis (OM) is a common adverse effect of head and neck squamous cell carcinoma (HNSCC) treatment. The purpose of this study was to investigate the significance of early changes in tissue electrical parameters (TEPs) in predicting the development of OM in HNSCC patients receiving radiation therapy (RT). The current study combined two study designs.