PDSS: A Pharmacological Decision Support System for Diabetics Patients with COVID-19.

With the advent of SARS-CoV-2, several studies have shown that there is a higher mortality rate in patients with diabetes and, in some cases, it is one of the side effects of overcoming the disease. However, there is no clinical decision support tool or specific treatment protocols for these patients. To tackle this issue, in this paper we present a Pharmacological Decision Support System (PDSS) providing intelligent decision support for COVID-19 diabetic patient treatment selection, based on an analysis of risk factors with data from electronic medical records using Cox regression.

An Artificial Intelligence-Driven Digital Health Solution to Support Clinical Management of Patients With Long COVID-19: Protocol for a Prospective Multicenter Observational Study.

Background: COVID-19 pandemic has revealed the weaknesses of most health systems around the world, collapsing them and depleting their available health care resources. Fortunately, the development and enforcement of specific public health policies, such as vaccination, mask wearing, and social distancing, among others, has reduced the prevalence and complications associated with COVID-19 in its acute phase. However, the aftermath of the global pandemic has called for an efficient approach to manage patients with long COVID-19.

Rationale for Timing of Follow-Up Visits to Assess Gluten-Free Diet in Celiac Disease Patients Based on Data Mining.

The assessment of compliance of gluten-free diet (GFD) is a keystone in the supervision of celiac disease (CD) patients. Few data are available documenting evidence-based follow-up frequency for CD patients. In this work we aim at creating a criterion for timing of clinical follow-up for CD patients using data mining.

Deep Learning Techniques to Improve the Performance of Olive Oil Classification.

The olive oil assessment involves the use of a standardized sensory analysis according to the "panel test" method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not.

TRIQ: a new method to evaluate triclusters.

Background: Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the genes extracted from the Gene Ontology project (GO).

Results: We propose , a single evaluation measure that combines the three measures previously described: correlation, graphic validation and functional annotation, providing a single value as result of the validation of a tricluster solution and therefore simplifying the steps inherent to research of comparison and selection of solutions.

Analysis of the Microstructure and Mechanical Properties of Titanium-Based Composites Reinforced by Secondary Phases and B₄C Particles Produced via Direct Hot Pressing.

In the last decade, titanium metal matrix composites (TMCs) have received considerable attention thanks to their interesting properties as a consequence of the clear interface between the matrix and the reinforcing phases formed. In this work, TMCs with 30 vol % of B₄C are consolidated by hot pressing. This technique is a powder metallurgy rapid process.

Influence of Sintering Temperature on the Microstructure and Mechanical Properties of In Situ Reinforced Titanium Composites by Inductive Hot Pressing.

This research is focused on the influence of processing temperature on titanium matrix composites reinforced through Ti, Al, and B₄C reactions. In order to investigate the effect of Ti-Al based intermetallic compounds on the properties of the composites, aluminum powder was incorporated into the starting materials. In this way, in situ TiAl were expected to form as well as TiB and TiC.

Analysis of the Influence of Starting Materials and Processing Conditions on the Properties of W/Cu Alloys.

In this work, a study of the influence of the starting materials and the processing time used to develop W/Cu alloys is carried out. Regarding powder metallurgy as a promising fabrication route, the difficulties in producing W/Cu alloys motivated us to investigate the influential factors on the final properties of the most industrially demanding alloys: 85-W/15-Cu, 80-W/20-Cu, and 75-W/25-Cu alloys. Two different tungsten powders with large variation among their particle size-fine (W) and coarse (W) powders-were used for the preparation of W/Cu alloys.

Data Mining Techniques Applied to Hydrogen Lactose Breath Test.

Objectives: Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production.

Methods: Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients.

MSL: A Measure to Evaluate Three-dimensional Patterns in Gene Expression Data.

Microarray technology is highly used in biological research environments due to its ability to monitor the RNA concentration levels. The analysis of the data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior.

Mining 3D patterns from gene expression temporal data: a new tricluster evaluation measure.

Microarrays have revolutionized biotechnological research. The analysis of new data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are applied to create groups of genes that exhibit a similar behavior.

SatDNA Analyzer: a computing tool for satellite-DNA evolutionary analysis.

Unlabelled: satDNA Analyzer is a program, implemented in C++, for the analysis of the patterns of variation at each nucleotide position considered independently amongst all units of a given satellite-DNA family when comparing it between a pair of species. The program classifies each site accordingly as monomorphic or polymorphic, discriminates shared from non-shared polymorphisms and classifies each non-shared polymorphism according to the model proposed by Strachan et al. in six different stages of transition during the spread of a variant repeat unit toward its fixation.