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. The goal of the system is to create real world evidence including the ability to continuously learn to improve clinical practice and outcomes of diabetic patients with COVID-19.

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. This is a great opportunity to leverage on innovative digital health solutions to provide exhausted health care systems with the most cost-effective and efficient tools available to support the clinical management of this population. In this context, the SENSING-AI project is focused on the research toward the implementation of an artificial intelligence-driven digital health solution that supports both the adaptive self-management of people living with long COVID-19 and the health care staff in charge of the management and follow-up of this population. OBJECTIVE: The objective of this protocol is the prospective collection of psychometric and biometric data from 10 patients for training algorithms and prediction models to complement the SENSING-AI cohort. METHODS: Publicly available health and lifestyle data registries will be consulted and complemented with a retrospective cohort of anonymized data collected from clinical information of patients diagnosed with long COVID-19. Furthermore, a prospective patient-generated data set will be captured using wearable devices and validated patient-reported outcomes questionnaires to complement the retrospective cohort. Finally, the 'Findability, Accessibility, Interoperability, and Reuse' guiding principles for scientific data management and stewardship will be applied to the resulting data set to encourage the continuous process of discovery, evaluation, and reuse of information for the research community at large. RESULTS: The SENSING-AI cohort is expected to be completed during 2022. It is expected that sufficient data will be obtained to generate artificial intelligence models based on behavior change and mental well-being techniques to improve patients' self-management, while providing useful and timely clinical decision support services to health care professionals based on risk stratification models and early detection of exacerbations. CONCLUSIONS: SENSING-AI focuses on obtaining high-quality data of patients with long COVID-19 during their daily life. Supporting these patients is of paramount importance in the current pandemic situation, including supporting their health care professionals in a cost-effective and efficient management of long COVID-19. TRIAL REGISTRATION: Clinicaltrials.gov NCT05204615; https://clinicaltrials.gov/ct2/show/NCT05204615. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37704.

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. We have applied data mining to a dataset with 188 CD patients on GFD (75% of them are children below 14 years old), evaluating the presence of gluten immunogenic peptides (GIP) in stools as an adherence to diet marker. The variables considered are gender, age, years following GFD and adherence to the GFD by fecal GIP. The results identify patients on GFD for more than two years (41.5% of the patients) as more prone to poor compliance and so needing more frequent follow-up than patients with less than 2 years on GFD. This is against the usual clinical practice of following less patients on long term GFD, as they are supposed to perform better. Our results support different timing follow-up frequency taking into consideration the number of years on GFD, age and gender. Patients on long term GFD should have a more frequent monitoring as they show a higher level of gluten exposure. A gender perspective should also be considered as non-compliance is partially linked to gender in our results: Males tend to get more gluten exposure, at least in the cultural context where our study was carried out. Children tend to perform better than teenagers or adults.

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. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014-2015 and 2015-2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.

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 TRIQ, 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. TRIQ has been applied to three datasets already studied and evaluated with single measures based on correlation, graphic similarity and GO terms. Triclusters have been extracted from this three datasets using two different algorithms: TriGen and OPTricluster. CONCLUSIONS: TRIQ has successfully provided the same results as a the three single evaluation measures. Furthermore, we have applied TRIQ to results from another algorithm, OPTRicluster, and we have shown how TRIQ has been a valid tool to compare results from different algorithms in a quantitative straightforward manner. Therefore, it appears as a valid measure to represent and summarize the quality of tricluster solutions. It is also feasible for evaluation of non biological triclusters, due to the parametrization of each component of TRIQ.

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. Incorporation of the intermetallic to the matrix, 20 vol % (Ti-Al), is also evaluated. Here, the reinforcing phases formed by the reaction between the titanium matrix and the ceramic particles, as well as the intermetallic addition, promote substantial variations to the microstructure and to the properties of the fabricated composites. The influences of the starting materials and the consolidation temperature (900 °C and 1000 °C) are investigated. By X-ray diffraction, scanning and transmission electron microscopy analysis, the in-situ-formed phases in the matrix and the residual ceramic particles were studied. Furthermore, mechanical properties are studied through tensile and bending tests in addition to other properties, such as Young's modulus, hardness, and densification of the composites. The results show the significant effect of temperature on the microstructure and on the mechanical properties from the same starting powder. Moreover, the Ti-Al addition causes variation in the interface between the reinforcement and the matrix, thereby affecting the behaviour of the TMCs produced at the same temperature.

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 (Wf) and coarse (Wc) powders-were used for the preparation of W/Cu alloys. Three weight ratios of fine and coarse (Wf:Wc) tungsten particles were analyzed. These powders were labelled as "tungsten bimodal powders". The powder blends were consolidated by rapid sinter pressing (RSP) at 900 °C and 150 MPa, and were thus sintered and compacted simultaneously. The elemental powders and W/Cu alloys were studied by optical microscopy (OM) and scanning electron microscopy (SEM). Thermal conductivity, hardness, and densification were measured. Results showed that the synthesis of W/Cu using bimodal tungsten powders significantly affects the final alloy properties. The higher the tungsten content, the more noticeable the effect of the bimodal powder. The best bimodal W powder was the blend with 10 wt % of fine tungsten particles (10-Wf:90-Wc). These specimens present good values of densification and hardness, and higher values of thermal conductivity than other bimodal mixtures.

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. RESULTS: Six different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test. CONCLUSIONS: Analysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms.

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 TixAly were expected to form as well as TiB and TiC. The specimens were fabricated by the powder metallurgy technique known as inductive hot pressing (iHP), using a temperature range between 900 °C and 1400 °C, at 40 MPa for 5 min. Raising the inductive hot pressing temperature may affect the microstructure and properties of the composites. Consequently, the variations of the reinforcing phases were investigated. X-ray diffraction, microstructural analysis, and mechanical properties (Young's modulus and hardness) of the specimens were carried out to evaluate and determine the significant influence of the processing temperature on the behavior of the composites.

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. Biclustering relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. Triclustering appears for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. These triclusters provide hidden information in the form of behavior patterns from temporal experiments with microarrays relating subsets of genes, experimental conditions, and time points. We present an evaluation measure for triclusters called Multi Slope Measure, based on the similarity among the angles of the slopes formed by each profile formed by the genes, conditions, and times of the tricluster.

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. Biclustering emerges as a valuable tool for microarray data analysis since it relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. However, if a third dimension appears in the data, triclustering is the appropriate tool for the analysis. This occurs in longitudinal experiments in which the genes are evaluated under conditions at several time points. All clustering, biclustering, and triclustering techniques guide their search for solutions by a measure that evaluates the quality of clusters. We present an evaluation measure for triclusters called Mean Square Residue 3D. This measure is based on the classic biclustering measure Mean Square Residue. Mean Square Residue 3D has been applied to both synthetic and real data and it has proved to be capable of extracting groups of genes with homogeneous patterns in subsets of conditions and times, and these groups have shown a high correlation level and they are also related to their functional annotations extracted from the Gene Ontology project.

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. Furthermore, this program implements several other utilities for satellite-DNA analysis evolution such as the design of the average consensus sequences, the average base pair contents, the distribution of variant sites, the transition to transversion ratio and different estimates of intra-specific variation and inter-specific variation. Aprioristic hypotheses on factors influencing the molecular drive process and the rates and biases of concerted evolution can be tested with this program. Additionally, satDNA Analyzer generates an output file containing a sequence alignment without shared polymorphisms to be used for further evolutionary analysis by using different phylogenetic softwares. AVAILABILITY: satDNA Analyzer is freely available at http://satdna.sourceforge.net/. SatDNA Analyzer has been designed to operate on Windows, Linux and Mac OS X.