Obstructive Sleep Apnea (OSA) is nowadays one of the respiratory pathologies with a higher in-cidence globally in developed countries. This situation led to an increase in the demand for medical appointments and diagnostic studies related to that condition, especially those based on poly-somnographies and cardiorespiratory polygraphies. These studies are limited in resources, causing long waiting lists with the subsequent impact on the patients’ health. Furthermore, it is necessary to mention that OSA’s symptomatology is not very specific, and it is typically present in the general population (excessive sleepiness, snore, etc.). In this regard, this paper proposes a novel intelligent clinical decision support system for the diagnosis of OSA which could be used to help medical teams, both in primary care settings and in units specialized in respiratory pathologies. The aim of the proposed system is to help discriminate the patients suspected of suffering from the pathology from those who are not. To this end, two types of information sets of heterogeneous nature are consid-ered. The first one encompasses objective data, related to the patient's health profile with infor-mation usually available in electronic health records. The second type comprises subjective data, referred to the symptomatology reported by the patient in a previous interview. To process the first group of information, a Machine Learning classification algorithm is used, Bagged Trees in this case. For processing the second information set, related with the symptomatology of the patient, a col-lection of expert systems based on fuzzy inferential systems arranged in cascade are employed. As a result, the system is able to determine two risk indicators related to the patient's risk of suffering from OSA: the Statistical Risk and the Symbolic Risk respectively. Subsequently, by interpreting both risk indicators mentioned it will be possible to determine the severity of the patients’ health, proposing a preliminary evaluation on their condition. For the initial tests of the system, a software artifact has been built using a dataset with 4,978 selected patients, suspected of suffering from OSA, from the Álvaro Cunqueiro Hospital in Vigo. The results obtained are promising, demonstrating the potential usefulness of this type of tools in medical diagnosis. Once the system has been validated with new data from clinical environments, it is considered as possible to obtain a relevant improvement in the quality of the healthcare services, and a reduction in the associated costs.

Background: To describe the effectiveness and safety of COVID-19 vaccine in a series of patients with rheumatoid arthritis (RA). Methods: Retrospective single-center study of RA patients, fulfilling the ACR 1987 or ACR/EULAR 2010 classification criteria, that received first COVID-19 vaccine between December 2020 and October 2021, had post-vaccination serology and subsequent follow-up of at least 6 months in a university hospital. Vaccine effectiveness was evaluated by the serological response and the incidence of post-vaccine COVID-19, and safety by the incidence of adverse events (AE) and RA flares. Adjusted logistic and linear multivariate regression analyses were carried out with Stata® to identify factors associated with vaccine response. Results: We included 118 patients with RA (87.2% women, age 65.4±11.6 years, evolution 12.0±9.6 years). 95.8% received complete vaccination schedule. Most patients (88.1%) developed adequate humoral immunogenicity, and the degree of serological response was significantly related to younger age and previous COVID-19 infection. After vaccination, 18.6% presented mild SARS-CoV-2 infection. Vaccine AE (19.5%) were mostly mild and inversely associated with age (OR 0.95). RA flares were anecdotal (1.7%) and inversely related to age (OR 0.95). Conclusion: Our results suggest that COVID-19 vaccine induces adequate humoral immunogenicity, with an acceptable safety profile in RA patients.

"Suero costeño" a type of sour cream, is a traditional dairy product made mainly in the Colombian Caribbean region and has a variety of uses. The objective was to characterize the physicochemical and rheological properties of “suero costeño” produced in department of Córdoba-Colombia. Samples of " suero costeño " were obtained from local producers, characterized physicochemically (protein, fat, crude fiber, moisture, ash, carbohydrates, chlorides, pH and acidity), and rheologically (stationary tests), adjusting their behavior to the power-law model. The results show great variability in the physicochemical characteristics of the “suero costeño” made by the different artisanal producers, classifying it as a full-fat sour cream or light sour cream, and values of protein, pH and acidity similar to similar traditional dairy products from other countries, such as Reindeer sour cream and Curd or cottage cheese. “Suero costeño” can be classified as a pseudoplastic fluid and depending on the raw materials used and the production process, some present thixotropy, which may be the product of interactions between proteins and fats, which cause changes in the internal structure. Due to this great variability in its physicochemical and rheological characteristics, it is necessary to standardize the process of producing to improve the marketing of this Colombian culinary product.