University of Las Palmas de Gran Canaria
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IEIVariantFilter: a bioinformatics tool to speed up genetic diagnosis of inborn errors of immunity patients
No full textSevere infectious diseases remain the leading cause of death in children and young adults worldwide. Monogenic inborn errors of immunity (IEIs) are traditionally defined as a heterogeneous group of rare inborn genetic diseases affecting the functioning of the immune system. Greater awareness has led to the clinical definition of 485 monogenic IEIs and whole exome sequencing (WES) is becoming increasingly relevant for IEI genetic diagnosis. The current protocol for IEI genetic studies includes manual filtering of the list of genes obtained as a WES read-out providing a short list of candidate genes. This procedure is time-consuming and can produce mistakes due to human error in manual filtering. IEIVariantFilter is a new web-based bioinformatics tool to speed up and refine the genetic diagnosis of IEI patients oriented for users in the biomedical field without needing bioinformatics expertise. IEIVariantFilter prioritizes genetic variants based on ranges of zygosity, the quality of reads, the predicted variant effect, and genes related to immunity, considering a consanguineous hypothesis whenever necessary. IEIVariantFilter facilitates gene and variant list prioritization, speeding up the identification of candidate disease-causing variants for validation by experimental studies. The software improves the genetic diagnosis of patients, thereby facilitating precision medicine and fast and proper treatment.82,454Q19,
Adherence to Compression Garments in Lymphedema Patients: A Cross-Sectional Study
No full textBackground/Objectives: This study evaluated adherence to compression garments, their role in edema stabilization, and factors influencing patient compliance. Materials and Methods: This observational, descriptive, cross-sectional study evaluated adherence to compression garments in 92 patients with lymphedema treated at the University Hospital of Gran Canaria Dr. Negr & iacute;n. In addition, sociodemographic, clinical, and design-related factors influencing adherence were analyzed. The evaluation was conducted via telephone questionnaires and a review of medical records. Results: The study revealed low adherence to compression garments in patients with lymphedema. Adherence was related to the garments' etiology, severity, and perceived comfort. Conclusions: The findings highlight the need to improve education strategies and personalize treatment recommendations to enhance adherence.90,5932,4Q2Q1SCIE11,
Jobs and tasks as a starting point for work automation research
No full textAs automation technology has proliferated in tourism through robots, chatbots and kiosks, research on work automation has also increased, leading to concerns about the impact of automation technologies on employment. However, research has generally ignored the tasks performed in each job. This issue has been highlighted in the literature as an explanation for why the expected impact of automation technology on job losses has not occurred. This study focuses on the case of sun and beach hotels and provides the distribution of employment across jobs, and the tasks of the most common jobs in these hotels. We suggest that this detailed information is needed to improve the rigour of research on work automation.41,9165,7Q1Q1ERIH PLU
Transfer learning for a tabular-to-image approach: A case study for cardiovascular disease prediction
No full textObjective: Machine learning (ML) models have been extensively used for tabular data classification but recent works have been developed to transform tabular data into images, aiming to leverage the predictive performance of convolutional neural networks (CNNs). However, most of these approaches fail to convert data with a low number of samples and mixed-type features. This study aims: to evaluate the performance of the tabular-to-image method named low mixed-image generator for tabular data (LM-IGTD); and to assess the effectiveness of transfer learning and fine-tuning for improving predictions on tabular data. Methods: We employed two public tabular datasets with patients diagnosed with cardiovascular diseases (CVDs): Framingham and Steno. First, both datasets were transformed into images using LM-IGTD. Then, Framingham, which contains a larger set of samples than Steno, is used to train CNN-based models. Finally, we performed transfer learning and fine-tuning using the pre-trained CNN on the Steno dataset to predict CVD risk. Results: The CNN-based model with transfer learning achieved the highest AUCORC in Steno (0.855), outperforming ML models such as decision trees, K-nearest neighbors, least absolute shrinkage and selection operator (LASSO) support vector machine and TabPFN. This approach improved accuracy by 2% over the best-performing traditional model, TabPFN. Conclusion: To the best of our knowledge, this is the first study that evaluates the effectiveness of applying transfer learning and fine-tuning to tabular data using tabular-to-image approaches. Through the use of CNNs' predictive capabilities, our work also advances the diagnosis of CVD by providing a framework for early clinical intervention and decision-making support.91,164,0Q1Q2SCIE10,
Quantification of the influence of risk factors with application to cardiovascular diseases in subjects with type 1 diabetes
No full textFuture occurrence of a disease can be highly influenced by some specific risk factors. This work presents a comprehensive approach to quantify the event probability as a function of each separate risk factor by means of a parametric model. The proposed methodology is mainly described and applied here in the case of a linear model, but the non-linear case is also addressed. To improve estimation accuracy, three distinct methods are developed and their results are integrated. One of them is Bayesian, based on a non-informative prior. Each of the other two, uses aggregation of sample elements based on their factor values, which is optimized by means of a different specific criterion. For one of these two, optimization is performed by Simulated Annealing. The methodology presented is applicable across various diseases but here we quantify the risk for cardiovascular diseases in subjects with type 1 diabetes. The results obtained combining the three different methods show accurate estimates of cardiovascular risk variation rates for the factors considered. Furthermore, the detection of a biological activation phenomenon for one of the factors is also illustrated. To quantify the performances of the proposed methodology and to compare them with those from a known method used for this type of models, a large simulation study is done, whose results are illustrated here.191,2351,6Q1Q
ANN-based surrogate model for the structural evaluation of jacket support structures for offshore wind turbines
No full textThe expansion of offshore wind farms, driven by better offshore wind conditions and fewer spatial limitations, has promoted the growth of this technology. This study focuses on the design of jacket support structures for Offshore Wind Turbines, which are suitable for deeper waters. However, the structural analysis required for designing these structures is computationally intensive due to multiple load cases and numerous checks. To reduce this computational cost, artificial-neural-network-based surrogate models capable of estimating the feasibility of a jacket structure acting as the support structure for any given wind turbine at a specific site are developed. A synthetic dataset generated through random sampling and evaluated by a structural model is utilized for training and testing the models. Two kind of models are compared: one is trained to estimate global feasibility, while the other estimates compliance with each of the structural partial requirements. Also, several assembly methods are proposed and compared. The best-performing model shows great classification metrics, with a Matthews Correlation Coefficient of 0.674, enabling an initial assessment of the structural feasibility. The low computational cost of artificial neural networks compared to structural models makes this surrogate model useful for accelerating otherwise prohibitive parametric studies or optimization processes.111,2144,6Q1Q1SCIE11,
Deep learning for lameness level detection in dairy cows
No full textLameness detection using raw sensor data is a very challenging task, as the data are devoid of specific information regarding predictors such as gait distribution, weight among legs, etc. We have addressed this challenge using a deep learning technique, named LLP-Cow (Lameness level predictor for Cow), which is an application of artificial intelligence (AI). For objective comparison, LLP-Cow is validated using CowScreeningDB, an unbalanced public dataset composed of sensor data. This dataset is recorded during the normal life of dairy cows. Hence, LLP-Cow models the normal behaviour of cows and consists of feature extraction, application-specific deep network and a voting system. The technique presented is able to model the behaviour of a cow for both binary and multiclass classification. The precision and specificity reported by our technique stand at 0.94 and 0.98 for multiclass and 0.91 and 0.90 for binary protocols for the best case scenario. Moreover, F1 measure, Matthews correlation coefficient and Kappa are 0.94, 0.91, and 0.91, respectively. The technique introduced provides a margin for human intervention through the use of a voting system at the classification stage. The technique presented is therefore an implemented AI system for cow lameness detection that offers room for exploration in terms of real time implementation.131,7497,5Q1Q1SCIE11,
Planning of lipid-lowering treatment in atherosclerotic vascular disease. Consensus SEC/SEA/SEEN/SEMFYC/SEMERGEN/SEMG/SEN/SEACV/S.E.N
No full textEn los últimos años estamos viviendo un avance de las terapias hipolipemiantes, apareciendo en escena nuevos fármacos que actúan en las diferentes vías metabólicas, reduciendo tanto los niveles de colesterol asociado a lipoproteínas de baja densidad (cLDL) que contienen apoproteína B (ApoB), como el riesgo vascular. Sin embargo, los resultados en la consecución de objetivos siguen siendo escasos, así como la utilización de las diferentes terapias que nos ayudan a conseguirlos. Entre los motivos que justifican esta situación destacan: la inadecuada identificación del riesgo vascular, la infrautilización de las terapias, la escasa adherencia al tratamiento recomendado, la falta de organización en cuanto a la adjudicación de roles y algoritmos de actuación en el seguimiento de los pacientes y la necesidad de mejora de la educación e intervenciones psicosociales que influyan tanto en la adherencia como en la consolidación de hábitos de vida saludables. Este documento de consenso tiene como objetivo mejorar el abordaje y seguimiento de las dislipemias de forma integral, definiendo la planificación de las terapias hipolipemiantes como estrategia de control (SEC/SEA/SEEN/SEMFYC/SEMERGEN/SEMG/SEN/SEACV/S.E.N.).In recent years we have been experiencing an advance in lipid-lowering therapies, with the appearance of new drugs that act on the different metabolic pathways, reducing both the levels of cholesterol associated with low-density lipoproteins (LDL-C) containing apoprotein B (ApoB), and vascular risk. However, the results in achieving goals are still scarce, as well as the use of the different therapies that help us to achieve them. Among the reasons that justify this situation are: the inadequate identification of vascular risk, the underuse of therapies, poor adherence to the recommended treatment, the lack of organization in terms of the assignment of roles and algorithms of action in the follow-up of patients and the need for improved education and psychosocial interventions that influence both adherence and consolidation of Healthy lifestyle habits. This consensus document aims to improve the approach and follow-up of dyslipidemia in a comprehensive way, defining the planning of lipid-lowering therapies as a control strategy (SEC/SEA/SEEN/SEMFYC/SEMERGEN/SEMG/SEN/SEACV/S.E.N.).0,424Q3ESCI10,
Author Correction: “Pink power”—the importance of coralline algal beds in the oceanic carbon cycle
No full textCorrection to: Nature Communicationshttps://doi.org/10.1038/s41467-024-52697-5, published online 27 September 2024 In the version of the article initially published, there was an error in the Abstract where “28–1347 g C m−2 day−1” should have read “28–1347 g C m−2”. This has been corrected in the HTML and PDF versions of the article.14,88714,7Q1Q1SCIE10,
Advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, Therapy, and Rehabilitation
No full textCerebral Palsy (CP) requires individualized interventions due to its complex nature affecting movement and coordination. Medical Human Digital Twin (MHDT) technology offers significant advancements in CP management by creating virtual representations of patients’ physical and neurological states. This paper reviews MHDT applications in CP diagnosis, therapy, and rehabilitation. Advanced imaging techniques and sensor integration enhance early and accurate diagnosis through detailed patient modeling. Therapeutic applications focus on personalized treatment plans using AI-driven models, VR/AR, and robotic-assisted devices, ensuring continuous optimization through real-time monitoring. Rehabilitation strategies benefit from immersive technologies and adaptive feedback, improving patient engagement and outcomes. The integration of human empathy with data-driven insights enables more precise and effective care. Ethical considerations, data management challenges, and future research directions, including interdisciplinary collaboration, are discussed. MHDTs present a transformative approach to CP care, promising improved patient outcomes and personalized healthcare solutions.1391250,606Q210,