University of Las Palmas de Gran Canaria
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Withdrawal/Withholding of Life-Sustaining Therapies in the Intensive Care Unit
No full textLimitations of life-sustaining therapies in the Intensive Care Unit (ICU) are usually applied when therapeutic measurements are considered futile. Withholding and
withdrawal therapies are then applied because therapies cannot achieve the desired
outcomes. When implemented, several aspects should be taken into consideration,
such as cultural, sociological, or personal preferences regarding end-of-life care.
Withholding is the decision not to start or increase a treatment if the benefit is not
clear, and is the most common measure applied, including orders such as do-notresuscitate, do-not-intubate, or non-renal-replacement therapies. Withdrawal is a
less frequent approach, and it is defined as the decision to stop a treatment. Decisionmaking should be multidisciplinary and consensual. It must respect the wishes of the
patient and/or their relatives. These decisions usually carry a substantial emotional
burden, especially for healthcare professionals, who might consider limitation of
life-sustaining therapies as a failure, even though this perception should evolve. In
addition, the implementation of these measures may lead to stressful situations for
professionals, which need to be addressed to avoid a negative impact. Mortality is
the most common outcome that emerges from the use of these measures. However,
a significant number of patients survive to hospitalization. Survival can have consequences that may affect the patient’s subsequent quality of life. Due to the potential
concerns, the difficulty of implementation, and the challenges in the decision-making
process, communication between healthcare professionals, patients, and families/
relatives is an important issue when it comes to limiting life-sustaining therapies
STRATUM project: AI-based point of care computing for neurosurgical 3D decision support tools
No full textIntegrated digital diagnostics are transforming complex surgical procedures, with brain tumour surgery being among the most challenging. STRATUM, a five-year Horizon Europe-funded project, aims to develop an advanced 3D decision support system leveraging real-time multimodal data processing powered by artificial intelligence. A key innovation of STRATUM is its design as an energy-efficient Point-of-Care computing system, seamlessly integrated into neurosurgical workflows. This system will provide surgeons with real-time, AI-driven insights, enhancing decision-making accuracy and efficiency. By optimizing surgical precision and reducing procedure duration, STRATUM is expected to improve patient outcomes while streamlining resource utilization within European healthcare systems.130,5491,9Q2Q2SCIE11,
Spanish Consensus on the Diagnosis and Management of Patients With Activated PI3K Delta Syndrome (APDS)
No full textActivated phosphoinositide 3-kinase (PI3K) delta syndrome (APDS) is an ultrarare genetic disorder characterized by overlapping immunodeficiency and immune dysregulation. Its diagnosis poses challenges owing to its clinical similarities with other inborn errors of immunity (IEIs), compounded by the absence of targeted treatments in today's medical landscape. The standard approach involves symptom management, reducing infection through immunoglobulin replacement therapy and prophylactic antimicrobials, and treating immune dysregulation with immunomodulators. This approach considerably hampers effective management of APDS, as the diverse nature of the disease necessitates a personalized strategy, in which the advantages and risks of immunosuppression are weighed against the potential for recurrent infections and lymphoproliferative complications. To address these challenges, a group of Spanish experts in the management of IEIs, including APDS, collaborated to develop Delphi-based consensus recommendations. The primary goal of the initiative was to offer guidance on various aspects of this complex disease, marking a pioneering effort in Europe. The consensus aims to facilitate early diagnosis and provide clues for individual patient-based decisions that could favor balanced risk-benefit estimations for treatment.10287160,8176,7Q2Q1SCIE11,
A comprehensive review on TiO<sub>2</sub>-based heterogeneous photocatalytic technologies for emerging pollutants removal from water and wastewater: From engineering aspects to modeling approaches
No full textThe increasing presence of emerging pollutants (EPs) in water poses significant environmental and health risks, necessitating effective treatment solutions. Originating from industrial, agricultural, and domestic sources, these contaminants threaten ecological and public health, underscoring the urgent need for innovative and efficient treatment methods. TiO2-based semiconductor photocatalysts have emerged as a promising approach for the degradation of EPs, leveraging their unique band structures and heterojunction schemes. However, few studies have examined the synergistic effects of operating conditions on these contaminants, representing a key knowledge gap in the field. This review addresses this gap by exploring recent trends in TiO2-driven heterogeneous photocatalysis for water and wastewater treatment, with an emphasis on photoreactor setups and configurations. Challenges in scaling up these photoreactors are also discussed. Furthermore, Machine Learning (ML) models play a crucial role in developing predictive frameworks for complex processes, highlighting intricate temporal dynamics essential for understanding EPs behavior. This capability integrates seamlessly with Computational Fluid Dynamics (CFD) modeling, which is also addressed in this review. Together, these approaches illustrate how CFD can simulate the degradation of EPs by effectively coupling chemical kinetics, radiative transfer, and hydrodynamics in both suspended and immobilized photocatalysts. By elucidating the synergy between ML and CFD models, this study offers new insights into overcoming traditional limitations in photocatalytic process design and optimizing operating conditions. Finally, this review presents recommendations for future directions and insights on optimizing and modeling photocatalytic processes.261,7718,0Q1Q1SCIE11,
A Bayesian Belief Network model for the estimation of risk of cardiovascular events in subjects with type 1 diabetes
No full textObjectives: Cardiovascular diseases (CVDs) represent a major risk for people with type 1 diabetes (T1D). Our aim here is to develop a new methodology that overcomes some of the problems and limitations of existing risk calculators. First, they are rarely tailored to people with T1D and, in general, they do not deal with missing values for any risk factor. Moreover, they do not take into account information on risk factors dependencies, which is often available from medical experts. Method: This study introduces a Bayesian Belief Network (BBN) model to quantify CVD risk in individuals with T1D. The developed methodology is applied to a large T1D dataset and its performances are assessed. A simulation study is also carried out to quantify the parameter estimation properties. Results: The performances of individual risk estimation, as measured by the area under the ROC curve and by the C-index, are about 0.75 for both real and simulated data with comparable sample sizes. Conclusions: We observe a good predictive ability of the proposed methodology with accurate parameter estimation. The BBN approach takes into account causal relationships between variables, providing a comprehensive description of the system. This makes it possible to derive useful tools for optimising intervention.1,4817,0Q1Q1SCIE11,
¿Debe continuar el hidroxiisohexil 3-ciclohexeno carboxaldehído (Lyral®) en las baterías estándar de las pruebas epicutáneas?
No full textBackground and objectives: Hydroxyisohexyl 3-cyclohexene carboxaldehyde (HICC), or Lyral®, is a fragrance marker that is part of the Fragrance Mix II (FM II) and is still patched as an independent allergen within the European and other baseline series despite the European Commission banning its use in cosmetics in 2021. We aimed to study the prevalence of sensitization to the HICC in Spain and its simultaneous positivity with the FM II to determine whether it should be part of the Spanish baseline series. Material and method: We analysed all consecutive patients simultaneously patch-tested with HICC and FM II within the Spanish Contact Dermatitis Registry (REIDAC) from June 1st, 2018 through December 31st, 2023. Results: A total of 96 (0.8%) out of 12,029 patients analysed yielded positive to HICC and 396 (3.3%) to FM II. In 53% and 64% of the patients, respectively, findings were considered currently relevant. A total of 72 out of 96 (75%) HICC positives would be detected if only FM II was patched. Conclusions: Prevalence of HICC sensitization in Spain is low and has decreased in recent years. HICC is a prohibited fragrance in cosmetics and FM II detects 3 in 4 sensitized patients. Our results suggest that HICC should remain outside the Spanish baseline series and support its exclusion from the European baseline series.0,297Q3Sello FECYTESC
Robust and Unified Semi-Supervised Unmixing of Hyperspectral Imaging for Linear and Multilinear Models
No full textThe spectral unmixing paradigm is an important analysis tool for hyperspectral (HS) images which allows one to decompose the 2D spatial information from the basic spectral signatures or end-members. In this work, we introduce a semi-supervised perspective for spectral unmixing, where some end-members are known a priori, while the rest are estimated from the HS image. The proposal is relevant in unmixing scenarios where there is only available partial information of end-members, or when the known end-members are not fully representative of the scene. Our formulation simultaneously addresses linear and multilinear mixing models in a unified fashion. The proposed algorithms are referred as ESSEAE (Extended Semi-Supervised End-members and Abundance Extraction) for the linear model, and NESSEAE (Non-linear Extended Semi-Supervised End-members and Abundance Extraction) for the multilinear one. The estimation process is presented as a weighted optimal approximation problem with regularization terms for abundances, end-members and sparse noise components, which is solved by a cyclic coordinate descent optimization (CCDO) scheme. In this work, we derive closed-solutions at each step of the CCDO scheme, and just for the multilinear model, the end-members estimation involves a gradient descent scheme with optimal linear search. We validate first our contributions with synthetic HS images that include Gaussian and sparse noise components to evaluate their robustness, and compare them with supervised and unsupervised perspectives. In addition, we validated the linear scheme with a breast histological sample, and the multilinear approach with the Urban dataset. The use of two datasets from different fields guarantees the generalizability of the proposed formulation. In general, our semi-supervised spectral unmixing schemes provide accurate and robust results with a fast computational time, and as expected, present an overall performance in between the supervised and unsupervised approaches. All scripts for the proposed algorithms are freely available in https://github.com/Nicothe4th/ESSEAE-NESSEAE.5315853140190,963,4Q1Q2SCIE10,
Misperception of non-happy facial features: overshadowing and priming by a smiling mouth
No full textA smile underlies the well-known recognition advantage of prototypical happy faces. However, a smiling mouth also has side effects: It biases a tendency to incorrectly judge as “happy” blended expressions with non-happy eyes (neutral, sad, etc.). This reveals interference with the processing of such mixed-smile expressions, which are otherwise ubiquitous in social settings (hence its practical importance). To account for this effect, we investigated two mechanisms: Perceptual overshadowing driven by the smile visual saliency, and categorical priming driven by the smile diagnostic value. In Experiment 1, we obtained diagnostic values for the mouth and eye regions of facial expressions of emotion. In Experiment 2, facilitation and interference effects of prime mouths on probe eyes were examined as a function of such values. In Experiment 3, overshadowing and priming were compared. Results showed, first, a high diagnostic value of the smiling mouth, followed by disgusted, sad, and angry mouths. Second, in correspondence with such values, the mouth expressions facilitated the recognition of congruent eyes. Importantly, the presence of a smiling mouth especially impaired the accurate recognition of non-happy eyes. This supports the categorical priming hypothesis. And, third, the smiling mouth still caused some (albeit limited) interference with the processing of facial information unrelated to expression (masculine/feminine appearance of the expresser). This is consistent with an overshadowing-inattentional blindness hypothesis. An alternative affective priming hypothesis is discussed.310,8061,9Q1Q2SSCI11,0ERIH PLU