Metabolic syndrome is not associated with erosive hand osteoarthritis: a cross-sectional study using data from the PROCOAC cohort.

To delineate the phenotype of erosive hand osteoarthritis (EHOA) in a Spanish population and assess its correlation with metabolic syndrome. We conducted a cross-sectional study using baseline data from the Prospective Cohort of Osteoarthritis from A Coruña (PROCOAC). Demographic and clinical variables, obtained through questionnaires, clinical examinations, and patient analytics, were compared among individuals with hand OA, with and without EHOA. We performed appropriate univariate and multivariate stepwise regression analyses using SPSS v28. Among 1039 subjects diagnosed with hand OA, 303 exhibited EHOA. Multivariate logistic regression analysis revealed associations with inflamed joints, nodular hand OA, and total AUSCAN. Furthermore, the association with a lower prevalence of knee OA remained significant. The influence of metabolic syndrome (MetS) on EHOA patients was analyzed by including MetS as a covariate in the model. It was observed that MetS does not significantly impact the presence of EHOA, maintaining the effect size of other factors. In conclusion, in the PROCOAC cohort, EHOA is associated with nodular hand OA, inflammatory hand OA, and a higher total AUSCAN. However, EHOA is linked to a lower prevalence of knee OA. Importantly, in our cohort, no relationship was found between EHOA and MetS.

UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm.

The Human Proteome Project (HPP) is leading the international effort to characterize the human proteome. Although the main goal of this project was first focused on the detection of missing proteins, a new challenge arose from the need to assign biological functions to the uncharacterized human proteins and describe their implications in human diseases. Not only the proteins with experimental evidence (uPE1 proteins) but also the uncharacterized missing proteins (uMPs) were the objects of study in this challenge, neXt-CP50. In this work, we developed a new bioinformatic approach to infer biological annotations for the uPE1 proteins and uMPs based on a "guilt-by-association" analysis using public RNA-Seq data sets. We used the correlation of these proteins with the well-characterized PE1 proteins to construct a network. In this way, we applied the PageRank algorithm to this network to identify the most relevant nodes, which were the biological annotations of the uncharacterized proteins. All of the generated information was stored in a database. In addition, we implemented the web application UPEFinder (https://upefinder.proteored.org) to facilitate the access to this new resource. This information is especially relevant for the researchers of the HPP who are interested in the generation and validation of new hypotheses about the functions of these proteins. Both the database and the web application are publicly available (https://github.com/ubioinformat/UPEfinder).

Integrating medical and research information: a big data approach.

Most of the information collected in different fields by Instituto de Investigación Biomédica de A Coruña (INIBIC) is classified as unstructured due to its high volume and heterogeneity. This situation, linked to the recent requirement of integrating it to the medical information, makes it necessary to implant specific architectures to collect and organize it before it can be analysed. The purpose of this article is to present the Hadoop framework as a solution to the problem of integrating research information in the Business Intelligence field. This framework can collect, explore, process and structure the aforementioned information, which allow us to develop an equivalent function to a data mart in an Intelligence Business system.