7,092 results on '"Nevado A"'
Search Results
2. Heart Rate Variability Biofeedback Intervention Programme to Improve Attention in Primary Schools
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Aranberri Ruiz, Ainara, Nevado, Borja, Migueles Seco, Malen, and Aritzeta Galán, Aitor
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- 2024
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3. Developing Healthcare Language Model Embedding Spaces
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Taylor, Niall, Schofield, Dan, Kormilitzin, Andrey, Joyce, Dan W, and Nevado-Holgado, Alejo
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text. We explore specialized pre-training to adapt smaller LLMs to different healthcare datasets. Three methods are assessed: traditional masked language modeling, Deep Contrastive Learning for Unsupervised Textual Representations (DeCLUTR), and a novel pre-training objective utilizing metadata categories from the healthcare settings. These schemes are evaluated on downstream document classification tasks for each dataset, with additional analysis of the resultant embedding spaces. Contrastively trained models outperform other approaches on the classification tasks, delivering strong performance from limited labeled data and with fewer model parameter updates required. While metadata-based pre-training does not further improve classifications across the datasets, it yields interesting embedding cluster separability. All domain adapted LLMs outperform their publicly available general base LLM, validating the importance of domain-specialization. This research illustrates efficient approaches to instill healthcare competency in compact LLMs even under tight computational budgets, an essential capability for responsible and sustainable deployment in local healthcare settings. We provide pre-training guidelines for specialized healthcare LLMs, motivate continued inquiry into contrastive objectives, and demonstrates adaptation techniques to align small LLMs with privacy-sensitive medical tasks.
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- 2024
4. Bespoke Large Language Models for Digital Triage Assistance in Mental Health Care
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Taylor, Niall, Kormilitzin, Andrey, Lorge, Isabelle, Nevado-Holgado, Alejo, and Joyce, Dan W
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Computer Science - Artificial Intelligence - Abstract
Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) -- a particularly important use-case for mental health where a majority of routinely-collected patient data lacks structured, machine-readable content. A significant problem for the the United Kingdom's National Health Service (NHS) are the long waiting lists for specialist mental healthcare. According to NHS data, in each month of 2023, there were between 370,000 and 470,000 individual new referrals into secondary mental healthcare services. Referrals must be triaged by clinicians, using clinical information contained in the patient's EHR to arrive at a decision about the most appropriate mental healthcare team to assess and potentially treat these patients. The ability to efficiently recommend a relevant team by ingesting potentially voluminous clinical notes could help services both reduce referral waiting times and with the right technology, improve the evidence available to justify triage decisions. We present and evaluate three different approaches for LLM-based, end-to-end ingestion of variable-length clinical EHR data to assist clinicians when triaging referrals. Our model is able to deliver triage recommendations consistent with existing clinical practices and it's architecture was implemented on a single GPU, making it practical for implementation in resource-limited NHS environments where private implementations of LLM technology will be necessary to ensure confidential clinical data is appropriately controlled and governed.
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- 2024
5. Evidence of eosinophil extracellular trap cell death in COPD: does it represent the trigger that switches on the disease?
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Uribe Echevarría L, Leimgruber C, García González J, Nevado A, Álvarez R, García LN, Quintar AA, and Maldonado CA
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COPD ,eosinophils ,neutrophils ,NETosis ,EETosis ,induced sputum ,Diseases of the respiratory system ,RC705-779 - Abstract
Loli Uribe Echevarría,1 Carolina Leimgruber,2 Jorge García González,1 Alberto Nevado,1 Ruth Álvarez,3 Luciana N García,2 Amado A Quintar,2 Cristina A Maldonado2 1Pneumonology Service, Sanatorio Allende (Nueva Córdoba), Córdoba, Argentina; 2Electronic Microscopy Center, Instituto de Investigaciones en Ciencias de la Salud (INICSA-CONICET), Facultad de Ciencias Médicas, Universidad Nacional de Córdoba, Córdoba, Argentina; 3Smoking Cessation Cente, Nuevo Hospital San Roque of Córdoba, Córdoba, Argentina Abstract: In spite of the numerous studies on chronic obstructive pulmonary disease (COPD), the cellular and molecular basis of the disease’s development remain unclear. Neutrophils and eosinophils are known to be key players in COPD. Recently, neutrophil extracellular trap cell death (NETosis), a mechanism due to decondensation and extrusion of chromatin to form extracellular traps, has been demonstrated in COPD. However, there is limited knowledge about eosinophil extracellular trap cell death (EETosis) and its role in the pathogenesis of COPD. The aim of this study was to evaluate EETosis in stable COPD. Induced sputum obtained from healthy smokers and low exacerbation risk COPD A or B group patients or high exacerbation risk COPD C or D group patients were included. Samples were examined using electron microscopy and immunofluorescence. Healthy smokers (n=10) and COPD A (n=19) group exhibited neutrophilic or paucigranulocytic phenotypes, with NETosis being absent in these patients. In contrast, COPD B (n=29), with eosinophilic or mixed phenotypes, showed EETosis and incipient NETosis. COPD C (n=18) and COPD D groups (n=13) were differentiated from low exacerbation rate-COPD group by the abundant cellular debris, with COPD C group having an eosinophilic pattern and numerous cells undergoing EETosis. A hallmark of this group was the abundant released membranes that often appeared phagocytosed by neutrophils, which coincidentally exhibited early NETosis changes. The COPD D group included patients with a neutrophilic or mixed pattern, with abundant neutrophil extracellular trap-derived material. This study is the first to demonstrate EETosis at different stages of stable COPD. The results suggest a role for eosinophils in COPD pathophysiology, especially at the beginning and during the persistence of the disease, regardless of whether the patient quit smoking, with EETosis debris probably triggering uncontrolled NETosis. The main target of these findings should be young smokers with the potential to develop COPD. Keywords: COPD, eosinophils, neutrophils, NETosis, EETosis, induced sputum
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- 2017
6. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations
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Argentieri, M. Austin, Xiao, Sihao, Bennett, Derrick, Winchester, Laura, Nevado-Holgado, Alejo J., Ghose, Upamanyu, Albukhari, Ashwag, Yao, Pang, Mazidi, Mohsen, Lv, Jun, Millwood, Iona, Fry, Hannah, Rodosthenous, Rodosthenis S., Partanen, Jukka, Zheng, Zhili, Kurki, Mitja, Daly, Mark J., Palotie, Aarno, Adams, Cassandra J., Li, Liming, Clarke, Robert, Amin, Najaf, Chen, Zhengming, and van Duijn, Cornelia M.
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- 2024
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7. Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks
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Taylor, Niall, Ghose, Upamanyu, Rohanian, Omid, Nouriborji, Mohammadmahdi, Kormilitzin, Andrey, Clifton, David, and Nevado-Holgado, Alejo
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods. We present an investigation into the suitability of different PEFT methods to clinical decision-making tasks, across a range of model sizes, including extremely small models with as few as $25$ million parameters. Our analysis shows that the performance of most PEFT approaches varies significantly from one task to another, with the exception of LoRA, which maintains relatively high performance across all model sizes and tasks, typically approaching or matching full fine-tuned performance. The effectiveness of PEFT methods in the clinical domain is evident, particularly for specialised models which can operate on low-cost, in-house computing infrastructure. The advantages of these models, in terms of speed and reduced training costs, dramatically outweighs any performance gain from large foundation LLMs. Furthermore, we highlight how domain-specific pre-training interacts with PEFT methods and model size, and discuss how these factors interplay to provide the best efficiency-performance trade-off. Full code available at: tbd.
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- 2024
8. Detecting the Clinical Features of Difficult-to-Treat Depression using Synthetic Data from Large Language Models
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Lorge, Isabelle, Joyce, Dan W., Taylor, Niall, Nevado-Holgado, Alejo, Cipriani, Andrea, and Kormilitzin, Andrey
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Computer Science - Computation and Language - Abstract
Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where despite treatment, they continue to experience significant burden. We sought to develop a Large Language Model (LLM)-based tool capable of interrogating routinely-collected, narrative (free-text) electronic health record (EHR) data to locate published prognostic factors that capture the clinical syndrome of DTD. In this work, we use LLM-generated synthetic data (GPT3.5) and a Non-Maximum Suppression (NMS) algorithm to train a BERT-based span extraction model. The resulting model is then able to extract and label spans related to a variety of relevant positive and negative factors in real clinical data (i.e. spans of text that increase or decrease the likelihood of a patient matching the DTD syndrome). We show it is possible to obtain good overall performance (0.70 F1 across polarity) on real clinical data on a set of as many as 20 different factors, and high performance (0.85 F1 with 0.95 precision) on a subset of important DTD factors such as history of abuse, family history of affective disorder, illness severity and suicidality by training the model exclusively on synthetic data. Our results show promise for future healthcare applications especially in applications where traditionally, highly confidential medical data and human-expert annotation would normally be required.
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- 2024
9. 2q31 microdeletion syndrome with the velocardiofacial phenotype and review of the literature: a case report
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Candelo, Estephania, Giraldo-Ocampo, Sebastian, Nevado, Julian, Lapunzina, Pablo, and Pachajoa, Harry
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- 2024
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10. Low anterior resection with transanal transection and single-stapled anastomosis: technical aspects and initial results
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Vivas López, Alfredo, Villar, Oscar Garcia, Borda, Javier Garcia, Restrepo Nuñez, Rafael, Rubio, Eduardo, Nevado, Cristina, Pelaez, Pablo, Labalde Martinez, Maria, Alias, David, Falcon, Kleber, Lorenzo, Sofia, Perea García, José, and Ferrero, Eduardo
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- 2024
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11. The circulating proteome and brain health: Mendelian randomisation and cross-sectional analyses
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Walker, Rosie M., Chong, Michael, Perrot, Nicolas, Pigeyre, Marie, Gadd, Danni A., Stolicyn, Aleks, Shi, Liu, Campbell, Archie, Shen, Xueyi, Whalley, Heather C., Nevado-Holgado, Alejo, McIntosh, Andrew M., Heitmeier, Stefan, Rangarajan, Sumathy, O’Donnell, Martin, Smith, Eric E., Yusuf, Salim, Whiteley, William N., and Paré, Guillaume
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- 2024
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12. Identifying DNA Sequence Motifs Using Deep Learning
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Poddar, Asmita, Uzun, Vladimir, Tunbridge, Elizabeth, Haerty, Wilfried, and Nevado-Holgado, Alejo
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Quantitative Biology - Genomics ,Computer Science - Machine Learning ,Quantitative Biology - Quantitative Methods - Abstract
Splice sites play a crucial role in gene expression, and accurate prediction of these sites in DNA sequences is essential for diagnosing and treating genetic disorders. We address the challenge of splice site prediction by introducing DeepDeCode, an attention-based deep learning sequence model to capture the long-term dependencies in the nucleotides in DNA sequences. We further propose using visualization techniques for accurate identification of sequence motifs, which enhance the interpretability and trustworthiness of DeepDeCode. We compare DeepDeCode to other state-of-the-art methods for splice site prediction and demonstrate its accuracy, explainability and efficiency. Given the results of our methodology, we expect that it can used for healthcare applications to reason about genomic processes and be extended to discover new splice sites and genomic regulatory elements.
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- 2023
13. The Human Phenotype Ontology in 2024: phenotypes around the world
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Gargano, Michael A, Matentzoglu, Nicolas, Coleman, Ben, Addo-Lartey, Eunice B, Anagnostopoulos, Anna V, Anderton, Joel, Avillach, Paul, Bagley, Anita M, Bakštein, Eduard, Balhoff, James P, Baynam, Gareth, Bello, Susan M, Berk, Michael, Bertram, Holli, Bishop, Somer, Blau, Hannah, Bodenstein, David F, Botas, Pablo, Boztug, Kaan, Čady, Jolana, Callahan, Tiffany J, Cameron, Rhiannon, Carbon, Seth J, Castellanos, Francisco, Caufield, J Harry, Chan, Lauren E, Chute, Christopher G, Cruz-Rojo, Jaime, Dahan-Oliel, Noémi, Davids, Jon R, de Dieuleveult, Maud, de Souza, Vinicius, de Vries, Bert BA, de Vries, Esther, DePaulo, J Raymond, Derfalvi, Beata, Dhombres, Ferdinand, Diaz-Byrd, Claudia, Dingemans, Alexander JM, Donadille, Bruno, Duyzend, Michael, Elfeky, Reem, Essaid, Shahim, Fabrizzi, Carolina, Fico, Giovanna, Firth, Helen V, Freudenberg-Hua, Yun, Fullerton, Janice M, Gabriel, Davera L, Gilmour, Kimberly, Giordano, Jessica, Goes, Fernando S, Moses, Rachel Gore, Green, Ian, Griese, Matthias, Groza, Tudor, Gu, Weihong, Guthrie, Julia, Gyori, Benjamin, Hamosh, Ada, Hanauer, Marc, Hanušová, Kateřina, He, Yongqun, Hegde, Harshad, Helbig, Ingo, Holasová, Kateřina, Hoyt, Charles Tapley, Huang, Shangzhi, Hurwitz, Eric, Jacobsen, Julius OB, Jiang, Xiaofeng, Joseph, Lisa, Keramatian, Kamyar, King, Bryan, Knoflach, Katrin, Koolen, David A, Kraus, Megan L, Kroll, Carlo, Kusters, Maaike, Ladewig, Markus S, Lagorce, David, Lai, Meng-Chuan, Lapunzina, Pablo, Laraway, Bryan, Lewis-Smith, David, Li, Xiarong, Lucano, Caterina, Majd, Marzieh, Marazita, Mary L, Martinez-Glez, Victor, McHenry, Toby H, McInnis, Melvin G, McMurry, Julie A, Mihulová, Michaela, Millett, Caitlin E, Mitchell, Philip B, Moslerová, Veronika, Narutomi, Kenji, Nematollahi, Shahrzad, and Nevado, Julian
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Biological Sciences ,Genetics ,Human Genome ,Machine Learning and Artificial Intelligence ,Networking and Information Technology R&D (NITRD) ,4.1 Discovery and preclinical testing of markers and technologies ,Generic health relevance ,Good Health and Well Being ,Humans ,Biological Ontologies ,Phenotype ,Genomics ,Algorithms ,Rare Diseases ,Environmental Sciences ,Information and Computing Sciences ,Developmental Biology ,Biological sciences ,Chemical sciences ,Environmental sciences - Abstract
The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.
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- 2024
14. SARS-CoV-2 in Captive Nonhuman Primates, Spain, 2020-2023
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Cano-Terriza, David, Beato-Benitez, Adrian, Fernandez-Bastit, Leira, Segales, Joaquim, Vergara-Alert, Julia, Martinez-Nevado, Eva, Carretero, Andrea, Crailsheim, Dietmar, Soriano, Pilar, Planas, Javier, Torro, Mario, and Garcia-Bocanegra, Ignacio
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Biosafety -- Evaluation ,Zoonoses -- Distribution ,Primates -- Health aspects ,Company distribution practices ,Health - Abstract
COVID-19 caused by SARS-CoV-2 is an emerging respiratory disease that likely originated from wildlife in late 2019 (1). Although the main driver of SARS-CoV-2 spread is human-to-human transmission, several wild [...]
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- 2024
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15. The Role of Environmental, Economic, and Social Dimensions of Sustainability in the Quality of Life in Spain
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Huete-Alcocer, Nuria, López-Ruiz, Víctor Raúl, Alfaro-Navarro, José Luis, and Nevado-Peña, Domingo
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- 2024
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16. Nickel-catalysed enantioselective alkene dicarbofunctionalization enabled by photochemical aliphatic C–H bond activation
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Hu, Xia, Cheng-Sánchez, Iván, Kong, Wangqing, Molander, Gary A., and Nevado, Cristina
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- 2024
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17. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data
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Jo, Taeho, Kim, Junpyo, Bice, Paula, Huynh, Kevin, Wang, Tingting, Arnold, Matthias, Meikle, Peter J, Giles, Corey, Kaddurah-Daouk, Rima, Saykin, Andrew J, Nho, Kwangsik, Kueider-Paisley, Alexandra, Doraiswamy, P Murali, Blach, Colette, Moseley, Arthur, Thompson, Will, St John-Williams, Lisa, Mahmoudiandehkhordi, Siamak, Tenenbaum, Jessica, Welsh-Balmer, Kathleen, Plassman, Brenda, Risacher, Shannon L, Kastenmüller, Gabi, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, van Duijin, Cornelia, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Koal, Therese, Mangravite, Lara, Krumsiek, Jan, Suhre, Karsten, Newman, John, Moreno, Herman, Foroud, Tatania, Sacks, Frank, Jansson, Janet, Weiner, Michael W, Aisen, Paul, Petersen, Ronald, Jack, Clifford R, Jagust, William, Trojanowki, John Q, Toga, Arthur W, Beckett, Laurel, Green, Robert C, Morris, John C, Perrin, Richard J, Shaw, Leslie M, Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, Gonzalez, Hector, Ho, Carole, Hsiao, John K, Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Perrin, Richard, and Ryan, Laurie
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Medical Biochemistry and Metabolomics ,Biomedical and Clinical Sciences ,Neurodegenerative ,Dementia ,Aging ,Acquired Cognitive Impairment ,Alzheimer's Disease ,Brain Disorders ,Machine Learning and Artificial Intelligence ,Networking and Information Technology R&D (NITRD) ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Neurosciences ,Neurological ,Humans ,Aged ,Magnetic Resonance Imaging ,Deep Learning ,Alzheimer Disease ,Neuroimaging ,Metabolome ,Lipids ,Cognitive Dysfunction ,Alzheimer's Disease Metabolomics Consortium ,Alzheimer's Disease Neuroimaging Initiative ,Alzheimer's disease ,Deep learning ,Lipidomics ,Machine learning ,Metabolomics ,Clinical Sciences ,Public Health and Health Services ,Clinical sciences ,Epidemiology - Abstract
BackgroundDeep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.MethodsThe c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.FindingsThe application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).InterpretationOur results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.FundingThe specific funding of this article is provided in the acknowledgements section.
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- 2023
18. Long term health outcomes in people with diabetes 12 months after hospitalisation with COVID-19 in the UK: a prospective cohort studyResearch in context
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Safoora Gharibzadeh, Ash Routen, Cameron Razieh, Francesco Zaccardi, Claire Lawson, Clare Gillies, Simon Heller, Melanie Davies, Helen Atkins, Stephen C. Bain, Nazir L. Lone, Krisnah Poinasamy, Tunde Peto, Elizabeth Robertson, Bob Young, Desmond Johnston, Jennifer Quint, Jonathan Valabhji, Khalida Ismail, Michael Marks, Alex Horsley, Annemarie Docherty, Ewen Harrison, James Chalmers, Ling-Pei Ho, Betty Raman, Chris Brightling, Omer Elneima, Rachel Evans, Neil Greening, Victoria C. Harris, Linzy Houchen-Wolloff, Marco Sereno, Aarti Shikotra, Amisha Singapuri, Louise Wain, Claudia Langenberg, John Dennis, John Petrie, Naveed Sattar, Olivia Leavy, Mattew Richardson, Ruth M. Saunders, Anne McArdle, Hamish McASuley, Tom Yates, Kamlesh Khunti, C.E. Brightling, R.A. Evans, L.V. Wain, J.D. Chalmers, V.C. Harris, L.P. Ho, A. Horsley, M. Marks, K. Poinasamy, B. Raman, A. Shikotra, A. Singapuri, R. Dowling, C. Edwardson, O. Elneima, S. Finney, N.J. Greening, B. Hargadon, L. Houchen--Wolloff, O.C. Leavy, H.J.C. McAuley, C. Overton, T. Plekhanova, R.M. Saunders, M. Sereno, C. Taylor, S. Terry, C. Tong, B. Zhao, D. Lomas, E. Sapey, C. Berry, C.E. Bolton, N. Brunskill, E.R. Chilvers, R. Djukanovic, Y. Ellis, D. Forton, N. French, J. George, N.A. Hanley, N. Hart, L. McGarvey, N. Maskell, H. McShane, M. Parkes, D. Peckham, P. Pfeffer, A. Sayer, A. Sheikh, A.A.R. Thompson, N. Williams, W. Greenhalf, M.G. Semple, M. Ashworth, H.E. Hardwick, L. Lavelle-Langham, W. Reynolds, V. Shaw, B. Venson, A.B. Docherty, E.M. Harrison, J.K. Baillie, L. Daines, R. Free, S. Kerr, N.I. Lone, D. Lozano-Rojas, K. Ntotsis, R. Pius, J. Quint, M. Richardson, M. Thorpe, M. Halling-Brown, F. Gleeson, J. Jacob, S. Neubauer, S. Siddiqui, J.M. Wild, S. Aslani, G. Baxter, M. Beggs, C. Bloomfield, M.P. Cassar, A. Chiribiri, E. Cox, D.J. Cuthbertson, V.M. Ferreira, L. Finnigan, S. Francis, P. Jezzard, G.J. Kemp, H. Lamlum, E. Lukaschuk, C. Manisty, G.P. McCann, C. McCracken, K. McGlynn, R. Menke, C.A. Miller, A.J. Moss, T.E. Nichols, C. Nikolaidou, C. O'Brien, G. Ogbole, B. Rangelov, D.P. O'Regan, A. Pakzad, S. Piechnik, S. Plein, I. Propescu, A.A. Samat, L. Saunders, Z.B. Sanders, R. Steeds, T. Treibel, E.M. Tunnicliffe, M. Webster, J. Willoughby, J. Weir McCall, C. Xie, M. Xu, H. Baxendale, M. Brown, B. Gooptu, R.G. Jenkins, D. Jones, I. Koychev, C. Langenberg, A. Lawrie, P.L. Molyneaux, J. Pearl, M. Ralser, N. Sattar, J.T. Scott, T. Shaw, D. Thomas, D. Wilkinson, L.G. Heaney, A. De Soyza, D. Adeloye, J.S. Brown, J. Busby, C. Echevarria, J. Hurst, P. Novotny, C. Nicolaou, I. Rudan, M. Shankar-Hari, S. Walker, B. Zheng, J.R. Geddes, M. Hotopf, K. Abel, R. Ahmed, L. Allan, C. Armour, D. Baguley, D. Baldwin, C. Ballard, K. Bhui, G. Breen, K. Breeze, M. Broome, T. Brugha, E. Bullmore, D. Burn, F. Callard, J. Cavanagh, T. Chalder, D. Clark, A. David, B. Deakin, H. Dobson, B. Elliott, J. Evans, R. Francis, E. Guthrie, P. Harrison, M. Henderson, A. Hosseini, N. Huneke, M. Husain, T. Jackson, I. Jones, T. Kabir, P. Kitterick, A. Korszun, J. Kwan, A. Lingford-Hughes, P. Mansoori, H. McAllister-Williams, K. McIvor, B. Michael, L. Milligan, R. Morriss, E. Mukaetova-Ladinska, K. Munro, A. Nevado-Holgado, T. Nicholson, S. Paddick, C. Pariante, J. Pimm, K. Saunders, M. Sharpe, G. Simons, J.P. Taylor, R. Upthegrove, S. Wessely, S. Amoils, C. Antoniades, A. Banerjee, A. Bularga, P. Chowienczyk, J.P. Greenwood, A.D. Hughes, K. Khunti, C. Lawson, N.L. Mills, A.N. Sattar, C.L. Sudlow, M. Toshner, P.J.M. Openshaw, D. Altmann, R. Batterham, N. Bishop, P.C. Calder, C.M. Efstathiou, J.L. Heeney, T. Hussell, P. Klenerman, F. Liew, J.M. Lord, P. Moss, S.L. Rowland-Jones, W. Schwaeble, R.S. Thwaites, L. Turtle, S. Walmsley, D. Wraith, M.J. Rowland, A. Rostron, B. Connolly, D.F. McAuley, D. Parekh, J. Simpson, C. Summers, J. Porter, R.J. Allen, R. Aul, S. Barratt, P. Beirne, J. Blaikley, R.C. Chambers, N. Chaudhuri, C. Coleman, E. Denneny, L. Fabbri, P.M. George, M. Gibbons, B. Guillen Guio, I. Hall, E. Hufton, I. Jarrold, G. Jenkins, S. Johnson, M.G. Jones, S. Jones, F. Khan, P. Mehta, J. Mitchell, J.E. Pearl, K. Piper Hanley, P. Rivera-Ortega, L.C. Saunders, D. Smith, M. Spears, L.G. Spencer, S. Stanel, I. Stewart, D. Thickett, R. Thwaites, S. Walsh, D.G. Wootton, L. Wright, S. Heller, M.J. Davies, H. Atkins, S. Bain, J. Dennis, K. Ismail, D. Johnston, P. Kar, P. McArdle, A. McGovern, T. Peto, J. Petrie, E. Robertson, K. Shah, J. Valabhji, B. Young, L.S. Howard, Mark Toshner, J. Newman, L. Price, A. Reddy, J. Rossdale, C. Sudlow, M. Wilkins, S.J. Singh, W.D.-C. Man, N. Armstrong, E. Baldry, M. Baldwin, N. Basu, M. Beadsworth, L. Bishop, A. Briggs, M. Buch, G. Carson, H. Chinoy, C. Dawson, E. Daynes, S. Defres, L. Gardiner, P. Greenhaff, S. Greenwood, M. Harvie, L. HOuchen-Wolloff, S. MacDonald, A. McArdle, A. McMahon, M. McNarry, G. Mills, C. Nolan, K. O'Donnell, Pimm, J. Sargent, L. Sigfrid, M. Steiner, D. Stensel, A.L. Tan, I. Vogiatzis, J. Whitney, D. Wilson, M. Witham, T. Yates, C. Laing, K. Bramham, P. Chowdhury, A. Frankel, L. Lightstone, S. McAdoo, K. McCafferty, M. Ostermann, N. Selby, C. Sharpe, M. Willicombe, L. Houchen-Wolloff, J. Bunker, R. Gill, C. Hastie, R. Nathu, N. Rogers, N. Smith, A. Shaw, L. Armstrong, B. Hairsine, H. Henson, C. Kurasz, L. Shenton, S. Fairbairn, A. Dell, N. Hawkings, J. Haworth, M. Hoare, A. Lucey, V. Lewis, G. Mallison, H. Nassa, C. Pennington, A. Price, C. Price, A. Storrie, G. Willis, S. Young, K. Chong-James, C. David, W.Y. James, A. Martineau, O. Zongo, A. Sanderson, V. Brown, T. Craig, S. Drain, B. King, N. Magee, D. McAulay, E. Major, J. McGinness, R. Stone, A. Haggar, A. Bolger, F. Davies, J. Lewis, A. Lloyd, R. Manley, E. McIvor, D. Menzies, K. Roberts, W. Saxon, D. Southern, C. Subbe, V. Whitehead, H. El-Taweel, J. Dawson, L. Robinson, D. Saralaya, L. Brear, K. Regan, K. Storton, J. Fuld, A. Bermperi, I. Cruz, K. Dempsey, A. Elmer, H. Jones, S. Jose, S. Marciniak, C. Ribeiro, J. Taylor, L. Watson, J. Worsley, R. Sabit, L. Broad, A. Buttress, T. Evans, M. Haynes, L. Jones, L. Knibbs, A. McQueen, C. Oliver, K. Paradowski, J. Williams, E. Harris, C. Sampson, C. Lynch, E. Davies, C. Evenden, A. Hancock, K. Hancock, M. Rees, L. Roche, N. Stroud, T. Thomas-Woods, M. Babores, J. Bradley-Potts, M. Holland, N. Keenan, S. Shashaa, H. Wassall, E. Beranova, H. Weston, T. Cosier, L. Austin, J. Deery, T. Hazelton, H. Ramos, R. Solly, S. Turney, L. Pearce, W. McCormick, S. Pugmire, W. Stoker, A. Wilson, L.A. Aguilar Jimenez, G. Arbane, S. Betts, K. Bisnauthsing, A. Dewar, G. Kaltsakas, H. Kerslake, M.M. Magtoto, P. Marino, L.M. Martinez, T.S. Solano, E. Wynn, W. Storrar, M. Alvarez Corral, A. Arias, E. Bevan, D. Griffin, J. Martin, J. Owen, S. Payne, A. Prabhu, A. Reed, C. Wrey Brown, T. Burdett, J. Featherstone, A. Layton, C. Mills, L. Stephenson, N. Easom, P. Atkin, K. Brindle, M.G. Crooks, K. Drury, R. Flockton, L. Holdsworth, A. Richards, D.L. Sykes, S. Thackray-Nocera, C. Wright, K.E. Lewis, A. Mohamed, G. Ross, S. Coetzee, K. Davies, R. Hughes, R. Loosley, L. O'Brien, Z. Omar, H. McGuinness, E. Perkins, J. Phipps, A. Taylor, H. Tench, R. Wolf-Roberts, O. Kon, D.C. Thomas, S. Anifowose, L. Burden, E. Calvelo, B. Card, C. Carr, D. Copeland, P. Cullinan, P. Daly, L. Evison, T. Fayzan, H. Gordon, S. Haq, C. King, K. March, M. Mariveles, L. McLeavey, N. Mohamed, S. Moriera, U. Munawar, J. Nunag, U. Nwanguma, L. Orriss-Dib, A. Ross, M. Roy, E. Russell, K. Samuel, J. Schronce, N. Simpson, L. Tarusan, C. Wood, N. Yasmin, R. Reddy, A.-M. Guerdette, M. Hewitt, K. Warwick, S. White, A.M. Shah, C.J. Jolley, O. Adeyemi, R. Adrego, H. Assefa-Kebede, J. Breeze, S. Byrne, P. Dulawan, A. Hayday, A. Hoare, A. Knighton, M. Malim, S. Patale, I. Peralta, N. Powell, A. Ramos, K. Shevket, F. Speranza, A. Te, A. Ashworth, J. Clarke, C. Coupland, M. Dalton, E. Wade, C. Favager, J. Greenwood, J. Glossop, L. Hall, T. Hardy, A. Humphries, J. Murira, J. Rangeley, G. Saalmink, B. Whittam, N. Window, J. Woods, G. Coakley, L. Allerton, A. Berridge, J. Brown, S. Cooper, A. Cross, S.L. Dobson, J. Earley, K. Hainey, J. Hawkes, V. Highett, S. Kaprowska, A.L. Key, S. Koprowska, N. Lewis-Burke, G. Madzamba, F. Malein, S. Marsh, C. Mears, L. Melling, M.J. Noonan, L. Poll, J. Pratt, E. Richardson, A. Rowe, K.A. Tripp, B. Vinson, L.O. Wajero, S.A. Williams-Howard, J. Wyles, S.N. Diwanji, P. Papineni, S. Gurram, S. Quaid, G.F. Tiongson, E. Watson, B. Al-Sheklly, C. Avram, P. Barran, J. Blaikely, N. Choudhury, D. Faluyi, T. Felton, T. Gorsuch, Z. Kausar, N. Odell, R. Osbourne, K. Radhakrishnan, S. Stockdale, D. Trivedi, A. Ayoub, G. Burns, G. Davies, H. Fisher, C. Francis, A. Greenhalgh, P. Hogarth, J. Hughes, K. Jiwa, G. Jones, G. MacGowan, D. Price, H. Tedd, S. Thomas, S. West, S. Wright, A. Young, M.J. McMahon, P. Neill, D. Anderson, H. Bayes, D. Grieve, I.B. McInnes, A. Brown, A. Dougherty, K. Fallon, L. Gilmour, K. Mangion, A. Morrow, K. Scott, R. Sykes, R. Touyz, E.K. Sage, F. Barrett, A. Donaldson, M. Patel, D. Bell, R. Hamil, K. Leitch, L. Macliver, J. Quigley, A. Smith, B. Welsh, G. Choudhury, S. Clohisey, A. Deans, J. Furniss, S. Kelly, D.E. Newby, D. Connell, A. Elliott, C. Deas, S. Mohammed, J. Rowland, A.R. Solstice, D. Sutherland, C.J. Tee, D. Arnold, S. Barrett, H. Adamali, A. Dipper, S. Dunn, A. Morley, L. Morrison, L. Stadon, S. Waterson, H. Welch, B. Jayaraman, T. Light, P. Almeida, J. Bonnington, M. Chrystal, C. Dupont, A. Gupta, L. Howard, W. Jang, S. Linford, L. Matthews, R. Needham, A. Nikolaidis, S. Prosper, K. Shaw, A.K. Thomas, N.M. Rahman, M. Ainsworth, A. Alamoudi, A. Bates, A. Bloss, A. Burns, P. Carter, M. Cassar, K.M. Channon, J. Chen, F. Conneh, T. Dong, R.I. Evans, E. Fraser, X. Fu, M. Havinden-Williams, N. Kanellakis, P. Kurupati, X. Li, C. Megson, K. Motohashi, D. Nicoll, G. Ogg, E. Pacpaco, M. Pavlides, Y. Peng, N. Petousi, J. Propescu, N. Rahman, N. Talbot, E. Tunnicliffe, B. Patel, R.E. Barker, D. Cristiano, N. Dormand, M. Gummadi, S. Kon, K. Liyanage, C.M. Nolan, S. Patel, O. Polgar, P. Shah, J.A. Walsh, H. Jarvis, S. Mandal, S. Ahmad, S. Brill, L. Lim, D. Matila, O. Olaosebikan, C. Singh, L. Garner, C. Johnson, J. Mackie, A. Michael, J. Pack, K. Paques, H. Parfrey, J. Parmar, N. Diar Bakerly, P. Dark, D. Evans, E. Hardy, A. Harvey, D. Holgate, S. Knight, N. Mairs, N. Majeed, L. McMorrow, J. Oxton, J. Pendlebury, C. Summersgill, R. Ugwuoke, S. Whittaker, W. Matimba-Mupaya, S. Strong-Sheldrake, J. Bagshaw, M. Begum, K. Birchall, R. Butcher, H. Carborn, F. Chan, K. Chapman, Y. Cheng, L. Chetham, C. Clark, Z. Coburn, J. Cole, M. Dixon, A. Fairman, J. Finnigan, H. Foot, D. Foote, A. Ford, R. Gregory, K. Harrington, L. Haslam, L. Hesselden, J. Hockridge, A. Holbourn, B. Holroyd-Hind, L. Holt, A. Howell, E. Hurditch, F. Ilyas, C. Jarman, E. Lee, J.-H. Lee, R. Lenagh, A. Lye, I. Macharia, M. Marshall, A. Mbuyisa, J. McNeill, S. Megson, J. Meiring, L. Milner, S. Misra, H. Newell, T. Newman, C. Norman, L. Nwafor, D. Pattenadk, M. Plowright, P. Ravencroft, C. Roddis, J. Rodger, P. Saunders, J. Sidebottom, J. Smith, L. Smith, N. Steele, G. Stephens, R. Stimpson, B. Thamu, N. Tinker, K. Turner, H. Turton, P. Wade, J. Watson, I. Wilson, A. Zawia, M. Ali, A. Dunleavy, N. Msimanga, M. Mencias, T. Samakomva, S. Siddique, J. Teixeira, V. Tavoukjian, J. Hutchinson, L. Allsop, K. Bennett, P. Buckley, M. Flynn, M. Gill, C. Goodwin, M. Greatorex, H. Gregory, C. Heeley, L. Holloway, M. Holmes, J. Kirk, W. Lovegrove, T.A. Sewell, S. Shelton, D. Sissons, K. Slack, S. Smith, D. Sowter, S. Turner, V. Whitworth, I. Wynter, L. Warburton, S. Painter, J. Tomlinson, C. Vickers, T. Wainwright, D. Redwood, J. Tilley, S. Palmer, G.A. Davies, L. Connor, A. Cook, T. Rees, F. Thaivalappil, C. Thomas, A. Butt, M. Coulding, S. Kilroy, J. McCormick, J. McIntosh, H. Savill, V. Turner, J. Vere, E. Fraile, J. Ugoji, S.S. Kon, H. Lota, G. Landers, M. Nasseri, S. Portukhay, A. Hormis, A. Daniels, J. Ingham, L. Zeidan, M. Chablani, L. Osborne, N. Ahwireng, B. Bang, D. Basire, A. Checkley, R. Evans, M. Heightman, T. Hillman, S. Janes, R. Jastrub, M. Lipman, S. Logan, M. Merida Morillas, H. Plant, J.C. Porter, K. Roy, E. Wall, B. Williams, N. Ahmad Haider, C. Atkin, R. Baggott, M. Bates, A. Botkai, A. Casey, B. Cooper, J. Dasgin, K. Draxlbauer, N. Gautam, J. Hazeldine, T. Hiwot, S. Holden, K. Isaacs, V. Kamwa, D. Lewis, S. Madathil, C. McGhee, K. Mcgee, A. Neal, A. Newton Cox, J. Nyaboko, Z. Peterkin, H. Qureshi, L. Ratcliffe, J. Short, T. Soulsby, J. Stockley, Z. Suleiman, T. Thompson, M. Ventura, S. Walder, C. Welch, S. Yasmin, K.P. Yip, P. Beckett, C. Dickens, U. Nanda, M. Aljaroof, H. Arnold, H. Aung, M. Bakali, M. Bakau, M. Bingham, M. Bourne, C. Bourne, P. Cairns, L. Carr, A. Charalambou, C. Christie, S. Diver, S. Edwards, H. Evans, J. Finch, S. Glover, N. Goodman, B. Gootpu, K. Hadley, P. Haldar, W. Ibrahim, L. Ingram, A. Lea, D. Lee, P. McCourt, T. Mcnally, A. Moss, W. Monteiro, M. Pareek, S. Parker, A. Rowland, A. Prickett, I.N. Qureshi, R. Russell, N. Samani, M. Sharma, J. Skeemer, M. Soares, E. Stringer, T. Thornton, M. Tobin, E. Turner, T.J.C. Ward, F. Woodhead, J. Wormleighton, A. Yousuf, C. Childs, S. Fletcher, M. Harvey, E. Marouzet, B. Marshall, R. Samuel, T. Sass, T. Wallis, H. Wheeler, R. Dharmagunawardena, E. Bright, P. Crisp, M. Stern, A. Wight, L. Bailey, A. Reddington, A. Ashish, J. Cooper, E. Robinson, A. Broadley, K. Howard, L. Barman, C. Brookes, K. Elliott, L. Griffiths, Z. Guy, D. Ionita, H. Redfearn, C. Sarginson, A. Turnbull, K. Holmes, and K. Lewis
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Diabetes ,Covid-19 ,Long Covid ,Medicine (General) ,R5-920 - Abstract
Summary: Background: People with diabetes are at increased risk of hospitalisation, morbidity, and mortality following SARS-CoV-2 infection. Long-term outcomes for people with diabetes previously hospitalised with COVID-19 are, however, unknown. This study aimed to determine the longer-term physical and mental health effects of COVID-19 in people with and without diabetes. Methods: The PHOSP-COVID study is a multicentre, long-term follow-up study of adults discharged from hospital between 1 February 2020 and 31 March 2021 in the UK following COVID-19, involving detailed assessment at 5 and 12 months after discharge. The association between diabetes status and outcomes were explored using multivariable linear and logistic regressions. Findings: People with diabetes who survived hospital admission with COVID-19 display worse physical outcomes compared to those without diabetes at 5- and 12-month follow-up. People with diabetes displayed higher fatigue (only at 5 months), frailty, lower physical performance, and health-related quality of life and poorer cognitive function. Differences in outcomes between diabetes status groups were largely consistent from 5 to 12-months. In regression models, differences at 5 and 12 months were attenuated after adjustment for BMI and presence of other long-term conditions. Interpretation: People with diabetes reported worse physical outcomes up to 12 months after hospital discharge with COVID-19 compared to those without diabetes. These data support the need to reduce inequalities in long-term physical and mental health effects of SARS-CoV-2 infection in people with diabetes. Funding: UK Research and Innovation and National Institute for Health Research. The study was approved by the Leeds West Research Ethics Committee (20/YH/0225) and is registered on the ISRCTN Registry (ISRCTN10980107).
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- 2025
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19. El Intermedio. Un enfoque innovador sobre la sátira de actualidad.
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Ignacio Nevado and Laura Fernández-Ramírez
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La Sexta ,El Intermedio ,infoentretenimiento ,sátira de actualidad ,géneros televisivos ,Communication. Mass media ,P87-96 ,Journalism. The periodical press, etc. ,PN4699-5650 - Abstract
El infoshow es el macrogénero de mayor crecimiento a nivel global durante las últimas décadas. Esto se debe, en gran medida, a los procesos de desregulación de los mercados televisivos, que han fomentado que los contenidos se adapten cada vez más a los gustos y demandas de la audiencia. En España, el auge del infoshow comenzó en la década de los 2000, en un contexto de crisis económica y desafección política que propició un mayor interés del público general por temas económicos, sociales y políticos, todos ellos muy propicios a la crítica y la espectacularización. En este contexto, la sátira de actualidad se consolidó como un género destacado en la televisión española. Esta investigación se centra en El Intermedio, producido por Globomedia para La Sexta. Este programa formaba parte de los primeros contenidos con los que la cadena inició sus emisiones oficiales, en el año 2006, y sigue presente en su prime time dieciocho años después. Se trata, sin duda, del programa más longevo y, también, uno de los de mayor éxito de su parrilla. Aunque sigue la tradición de formatos previos, El Intermedio ofrece un enfoque innovador alejándose del ámbito puramente humorístico para ofrecer un acercamiento alternativo a las noticias de actualidad. Mediante el análisis de contenido de sus diez primeras temporadas se analiza el perfil de sus presentadores y colaboradores y su estructura de contenidos. Los resultados muestran un enfoque innovador en el tratamiento de las noticias y en el equilibrio entre la función informativa y el entretenimiento.
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- 2024
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20. La conservación del escenario del Teatro romano de Medellín (Badajoz, España)
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Manuel Viola Nevado
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preservación, restitución, autenticidad, conocimiento, reúso. ,Conservation and restoration of prints ,NE380 ,Architectural drawing and design ,NA2695-2793 - Abstract
El presente artículo describe la intervención efectuada en el escenario del Teatro romano de Medellín (Badajoz, España) desde enero a abril de 2011. Este teatro, datado en los últimos años del siglo I a.C., fue abandonado, expoliado y ocupado por edificaciones diversas a lo largo de la Edad Media. Las excavaciones arqueológicas acometidas de 2007 a 2010 sacaron a la luz los restos del edificio. En el caso del escenario, los vestigios originales fueron la base sobre la que apuntalar su conservación, que combinó la lectura arqueológica, la anastilosis, la consolidación y la aportación de arquitectura. La intervención ha permitido: la conservación de los restos, la posibilidad de ahondar en su conocimiento, y la recuperación de los accesos y recorridos que hacen posible tanto su visita turística como su recuperación para uso teatral (fig. 1). Desde su apertura al público en 2013 ha recibido multitud de visitas, y en la actualidad es una de las sedes del Festival Internacional de Teatro Clásico de Mérida.
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- 2024
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21. Promoting the Development of Intercultural Competence in Higher Education through Intercultural Learning Interventions
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Sierra-Huedo, María Luisa and Nevado-Llopis, Almudena
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Introduction: This article analyses the impact of an intercultural learning intervention in a Spanish university post-European Higher Education Area implementation. Objective: Our research's main objective consisted of measuring the development of intercultural competence in the first cohort of a Translation and Intercultural Communication bachelor's degree in a Spanish university, before and after taking specific courses in intercultural studies and spending a study abroad semester. Methodology: A mixed methodology was implemented, in which the Intercultural Development Inventory (IDI) was used as a measuring instrument. Additionally, in-depth personal interviews were conducted to complement the data obtained. Results: The results of this study show that the programming and sequencing of specific courses, together with experiences abroad, contribute to the development of intercultural competence. Conclusions: More intercultural learning interventions are needed in higher education courses to develop and build an intercultural campus and educate global citizens. When applying intercultural learning interventions and intercultural methodologies, students develop their intercultural competence, a key competence for 21st-century graduates.
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- 2022
22. Inhibition of the AURKA/YAP1 axis is a promising therapeutic option for overcoming cetuximab resistance in colorectal cancer stem cells
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Rio-Vilariño, Anxo, Cenigaonandia-Campillo, Aiora, García-Bautista, Ana, Mateos-Gómez, Pedro A., Schlaepfer, Marina I., del Puerto-Nevado, Laura, Aguilera, Oscar, García-García, Laura, Galeano, Carlos, de Miguel, Irene, Serrano-López, Juana, Baños, Natalia, Fernández-Aceñero, María Jesús, Lacal, Juan Carlos, Medico, Enzo, García-Foncillas, Jesús, and Cebrián, Arancha
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- 2024
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23. Chiral arylsulfinylamides as reagents for visible light-mediated asymmetric alkene aminoarylations
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Hervieu, Cédric, Kirillova, Mariia S., Hu, Yawen, Cuesta-Galisteo, Sergio, Merino, Estíbaliz, and Nevado, Cristina
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- 2024
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24. Structure-Based Design of CBP/EP300 Degraders: When Cooperativity Overcomes Affinity
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Iván Cheng-Sánchez, Katherine Gosselé, Leonardo Palaferri, Eleen Laul, Gionata Riccabella, Rajiv K. Bedi, Yaozong Li, Anna Müller, Ivan Corbeski, Amedeo Caflisch, and Cristina Nevado
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Chemistry ,QD1-999 - Published
- 2024
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25. Language Brokering in Healthcare in Spain: Insight Based on Testimonies
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Almudena Nevado Llopis, Ana Isabel Foulquié Rubio, and Alina Pelea
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linguistic and cultural mediation ,healthcare services ,children ,young adults ,Language and Literature - Abstract
There were 6,491,502 foreign residents living in Spain in January 2024, a number that indicates a significant increase since the beginning of the 21st century. Among this foreign population, the largest communities are Moroccans, Romanians and British, whose mother tongue is not Spanish. According to the results from several studies conducted over the last decades, when these allophone residents use the Spanish healthcare services, they frequently ask their children to help them communicate with healthcare providers through linguistic and cultural mediation, even when professional interpreting or mediation services are available. How did these children feel while mediating for their parents in this context? Did they have any negative experiences? We intend to provide answers to these questions through semi-structured individual interviews with adults who had mediated for their parents in healthcare settings in Spain when they were children or young adults. These answers contribute to a clear understanding of the consequences of language brokering, thus promoting the use of professional interpreting and mediation services in these settings.
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- 2024
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26. Engagement docente: Autoeficacia, inteligencia emocional y felicidad subjetiva en Educación Física
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Jose María Nevado Luna, Sagrario Del Valle, Francisco Sanchez Sanchez, and Daniel Juarez Santos
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Engagement ,autoeficacia ,inteligencia emocional ,felicidad subjetiva ,educación física ,educación secundaria ,Sports ,GV557-1198.995 - Abstract
El sistema educativo establece la importancia de conocer cuáles deben ser las características de los docentes que garanticen el éxito educativo, capaces de generar un aprendizaje significativo, mediante el desarrollo de una relación docente-discente recíproca, compasiva, donde se fomenten los valores democráticos. El engagement docente podría ser la respuesta a esta problemática, de modo que interesa analizar qué variables influyen y son determinantes para que exista compromiso en la educación. Variables como la autoeficacia, la inteligencia emocional y la felicidad son nucleares. Hallar un modelo que las relacione podría ofrecer nuevas perspectivas en la identidad profesional y formación del profesorado. En este estudio han participado 458 docentes de educación física, se ha llevado a cabo un análisis correlacional, una regresión lineal jerárquica y de mediación, obteniendo un modelo significativo donde el engagement se predice por la autoeficacia, seguido por la inteligencia emocional y la felicidad subjetiva. Por género, las mujeres presentan una mayor percepción en la autoeficacia. Por años de experiencia se hallan diferencias en el nivel de engagement. Las variables analizadas presentan una relación lógica en la interacción entre ellas, siendo diferente entre mujeres y varones. Estos resultados establecen la importancia e influencia de las variables personales sobre el engagement de los docentes de educación física, y, cómo éstas varían en función del género y los años de experiencia. Aspectos por evaluar para diseñar la identidad profesional y, como consecuencia, la formación inicial y continua del profesorado.
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- 2024
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27. 176. DISFAGIA OROFARÍNGEA EN DAÑO CEREBRAL. NUESTRA EXPERIENCIA
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N. Álvarez Bandrés, E. Gómez Tomé, F. Julián Villaverde, C. Bayo Nevado, and S. López Álava
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Neurology. Diseases of the nervous system ,RC346-429 - Published
- 2024
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28. Novel risk loci for COVID-19 hospitalization among admixed American populations
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Silvia Diz-de Almeida, Raquel Cruz, Andre D Luchessi, José M Lorenzo-Salazar, Miguel López de Heredia, Inés Quintela, Rafaela González-Montelongo, Vivian Nogueira Silbiger, Marta Sevilla Porras, Jair Antonio Tenorio Castaño, Julian Nevado, Jose María Aguado, Carlos Aguilar, Sergio Aguilera-Albesa, Virginia Almadana, Berta Almoguera, Nuria Alvarez, Álvaro Andreu-Bernabeu, Eunate Arana-Arri, Celso Arango, María J Arranz, Maria-Jesus Artiga, Raúl C Baptista-Rosas, María Barreda- Sánchez, Moncef Belhassen-Garcia, Joao F Bezerra, Marcos AC Bezerra, Lucía Boix-Palop, María Brion, Ramón Brugada, Matilde Bustos, Enrique J Calderón, Cristina Carbonell, Luis Castano, Jose E Castelao, Rosa Conde-Vicente, M Lourdes Cordero-Lorenzana, Jose L Cortes-Sanchez, Marta Corton, M Teresa Darnaude, Alba De Martino-Rodríguez, Victor del Campo-Pérez, Aranzazu Diaz de Bustamante, Elena Domínguez-Garrido, Rocío Eirós, María Carmen Fariñas, María J Fernandez-Nestosa, Uxía Fernández-Robelo, Amanda Fernández-Rodríguez, Tania Fernández-Villa, Manuela Gago-Dominguez, Belén Gil-Fournier, Javier Gómez-Arrue, Beatriz González Álvarez, Fernan Gonzalez Bernaldo de Quirós, Anna González-Neira, Javier González-Peñas, Juan F Gutiérrez-Bautista, María José Herrero, Antonio Herrero-Gonzalez, María A Jimenez-Sousa, María Claudia Lattig, Anabel Liger Borja, Rosario Lopez-Rodriguez, Esther Mancebo, Caridad Martín-López, Vicente Martín, Oscar Martinez-Nieto, Iciar Martinez-Lopez, Michel F Martinez-Resendez, Angel Martinez-Perez, Juliana F Mazzeu, Eleuterio Merayo Macías, Pablo Minguez, Victor Moreno Cuerda, Silviene F Oliveira, Eva Ortega-Paino, Mara Parellada, Estela Paz-Artal, Ney PC Santos, Patricia Pérez-Matute, Patricia Perez, M Elena Pérez-Tomás, Teresa Perucho, Mellina Pinsach-Abuin, Guillermo Pita, Ericka N Pompa-Mera, Gloria L Porras-Hurtado, Aurora Pujol, Soraya Ramiro León, Salvador Resino, Marianne R Fernandes, Emilio Rodríguez-Ruiz, Fernando Rodriguez-Artalejo, José A Rodriguez-Garcia, Francisco Ruiz-Cabello, Javier Ruiz-Hornillos, Pablo Ryan, José Manuel Soria, Juan Carlos Souto, Eduardo Tamayo, Alvaro Tamayo-Velasco, Juan Carlos Taracido-Fernandez, Alejandro Teper, Lilian Torres-Tobar, Miguel Urioste, Juan Valencia-Ramos, Zuleima Yáñez, Ruth Zarate, Itziar de Rojas, Agustín Ruiz, Pascual Sánchez, Luis Miguel Real, SCOURGE Cohort Group, Encarna Guillen-Navarro, Carmen Ayuso, Esteban Parra, José A Riancho, Augusto Rojas-Martinez, Carlos Flores, Pablo Lapunzina, and Ángel Carracedo
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GWAS ,COVID-19 ,SNP ,Medicine ,Science ,Biology (General) ,QH301-705.5 - Abstract
The genetic basis of severe COVID-19 has been thoroughly studied, and many genetic risk factors shared between populations have been identified. However, reduced sample sizes from non-European groups have limited the discovery of population-specific common risk loci. In this second study nested in the SCOURGE consortium, we conducted a genome-wide association study (GWAS) for COVID-19 hospitalization in admixed Americans, comprising a total of 4702 hospitalized cases recruited by SCOURGE and seven other participating studies in the COVID-19 Host Genetic Initiative. We identified four genome-wide significant associations, two of which constitute novel loci and were first discovered in Latin American populations (BAZ2B and DDIAS). A trans-ethnic meta-analysis revealed another novel cross-population risk locus in CREBBP. Finally, we assessed the performance of a cross-ancestry polygenic risk score in the SCOURGE admixed American cohort. This study constitutes the largest GWAS for COVID-19 hospitalization in admixed Latin Americans conducted to date. This allowed to reveal novel risk loci and emphasize the need of considering the diversity of populations in genomic research.
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- 2024
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29. POLQ immunostaining behaves as a prognostic factor for pancreatic carcinoma
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Laura del Puerto-Nevado, María J. Fernández-Aceñero, Arancha Cebrián, Yuliia Fatych, Luis I. Díez-Valladares, Elia Pérez-Aguirre, Sofía de la Serna, Alejandra García-Botella, Javier Martínez-Useros, Jesús García-Foncillas, and Pedro A. Mateos-Gómez
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POLQ ,pancreatic carcinoma ,immunostaining ,overall survival ,prognostic factor ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
BackgroundDNA polymerase theta (POLQ) is a translesion synthesis polymerase essential for the repair of double strand breaks by the error-prone TMEJ (Theta Mediated End Joining) pathway. Although POLQ participates in maintaining genome stability, several studies have shown that its overexpression correlates with cancer progression and poor prognosis. Due to the fact that its role as a biomarker in pancreatic cancer remains unexplored, we aimed to study the usefulness of POLQ H-score as a prognostic factor in a pancreatic cancer patient cohort.MethodsWe evaluated POLQ gene expression using a web-based tool to deliver gene expression profiling and interactive analyses based on TCGA and GTEx (GEPIA) and we examined the POLQ immunostaining in 152 biliopancreatic cancer surgical specimens using tissue microarrays. Association with survival was evaluated by Kaplan Meier curves and uni-multivariate Cox regression.ResultsGEPIA analysis showed statistical differences according to POLQ mRNA levels in Disease Free Survival (DFS) (log rank 0.023, HR 2.8, p=0.029) and Overall Survival (OS) (log rank 0.011, HR 3.1, p=0.016). For immunohistochemistry (IHC) evaluation, POLQ H-score was calculated, and showed statistical differences for OS in Kaplan Meier curves (log rank 0.001) and uni-multivariate analysis (HR 2.27; 95% CI 1.24-4.15, p=0.008).ConclusionsOur results indicate that POLQ is an independent prognostic factor in pancreatic cancer when analyzed by immunostaining, which is in agreement with the results shown by the POLQ gene expression analysis (GEPIA).
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- 2024
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30. Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio
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Nevado-Holgado, Alejo, Espie, Colin, Liakata, Maria, Henry, Alasdair, Gu, Jenny, Taylor, Niall, Saunders, Kate, Walker, Tom, and Miller, Chris
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
SleepioTM is a digital mobile phone and web platform that uses techniques from cognitive behavioural therapy (CBT) to improve sleep in people with sleep difficulty. As part of this process, Sleepio captures data about the sleep behaviour of the users that have consented to such data being processed. For neural networks, the scale of the data is an opportunity to train meaningful models translatable to actual clinical practice. In collaboration with Big Health, the therapeutics company that created and utilizes Sleepio, we have analysed data from a random sample of 401,174 sleep diaries and built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner. We demonstrate that this neural network is more accurate than standard statistical methods in predicting the sleep quality of an individual based on his/her behaviour from the last 10 days. We compare model performance in a wide range of hyperparameter settings representing various scenarios. We further show that the neural network can be used to produce personalised recommendations of what sleep habits users should follow to maximise sleep quality, and show that these recommendations are substantially better than the ones generated by standard methods. We finally show that the neural network can explain the recommendation given to each participant and calculate confidence intervals for each prediction, all of which are essential for clinicians to be able to adopt such a tool in clinical practice.
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- 2022
31. Unlocking the power of AI models: exploring protein folding prediction through comparative analysis
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Tejera-Nevado Paloma, Serrano Emilio, González-Herrero Ana, Bermejo Rodrigo, and Rodríguez-González Alejandro
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protein structure prediction ,protein folding estimation ,protein conformation analysis ,artificial intelligence models ,deep learning models ,root-mean-square deviation ,Biotechnology ,TP248.13-248.65 - Abstract
Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated. These proteins contain four domains of unknown function and are present in Leishmania spp. ARM refers to an antimony resistance marker. The study’s main objective is to assess the accuracy of the model’s predictions, thereby providing insights into the complexities and supporting metrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with Trypanosoma cruzi and Trypanosoma brucei, leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available.
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- 2024
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32. Condicionantes de acceso a nuevos medicamentos dermatológicos en España: resultados del proyecto EQUIDAD
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M. Sánchez-Díaz, Á. Flórez, M. Ara-Martín, S. Arias-Santiago, R. Botella-Estrada, J. Cañueto, G. Carretero-Hernández, P. Coto-Segura, E. de Eusebio-Murillo, F.J. García-Latasa de Araníbar, V. García-Patos Briones, J. Gardeazabal García, C. Gómez-Fernández, S. Hernández-Ostiz, R. Izu Belloso, A. López-Ávila, P. Manchado López, A. Martín-Santiago, I. Martinez de Espronceda Ezquerro, A. Mateu-Puchades, P. Mercader-García, J. Notario Rosa, L. Palacio Aller, C. Pérez-Hortet, L. Quintana-Castanedo, R. Rivera, V. Rocamora Duran, I.M. Rodríguez-Nevado, R. Ruiz-Villaverde, J. Suárez, L. Trasobares-Marugán, C. Vizán-de Uña, I. Yanguas, A. Zulaica-Garate, I. García-Doval, M.Á. Descalzo, M. Grau-Pérez, and J.M. Carrascosa-Carrillo
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Equity ,Drugs ,Psoriasis ,Atopic dermatitis ,Oncology ,Spain ,Dermatology ,RL1-803 ,Internal medicine ,RC31-1245 - Abstract
Resumen: Antecedentes: En España, aunque el Ministerio de Sanidad elabora el informe de posicionamiento terapéutico (IPT) y las condiciones de reembolso de los fármacos, las Comunidades Autónomas (CC. AA.) gestionan los servicios de salud y deciden sobre las condiciones de prescripción en su ámbito territorial. El objetivo del estudio EQUIDAD fue describir los condicionantes para la prescripción de los nuevos fármacos en Dermatología en las CC. AA. y sus posibles diferencias. Material y métodos: Estudio transversal realizado en abril-mayo del 2023. Dos dermatólogos con responsabilidades directivas de cada Comunidad Autónoma (C. A.) informaron sobre los condicionantes autonómicos y locales en la prescripción de los fármacos cuyo IPT para el tratamiento de enfermedades dermatológicas fue publicado en los años 2016-2022. Los datos fueron recogidos mediante un cuestionario online. Resultados: Un total de 33 investigadores de 17 CC. AA. participaron en el estudio. Se observaron inequidades entre CC. AA. para el acceso a los nuevos fármacos. Existieron condicionantes autonómicos adicionales al IPT en psoriasis en el 64,7% de las CC. AA., siendo este porcentaje menor en dermatitis atópica (35,3%) o melanoma (11,8%). El más frecuente fue el requisito de un orden de prescripción previo para el uso del fármaco. En algunas CC. AA. se detectaron además variaciones y condicionantes locales (diferencias entre centros de una misma C. A.). Conclusiones: Existe una multiplicidad de criterios tanto a nivel autonómico como local que añade restricciones adicionales a las establecidas por los IPT y que plantean una situación de inequidad entre los pacientes y los profesionales de las diferentes CC. AA. en el acceso a los nuevos fármacos. Abstract: Background: Although the Spanish Ministry of Health prepares national therapeutic positioning reports (TPRs) and drug reimbursement policies, each of the country's 17 autonomous communities (ACs) is responsible for health care services and prescription requirements in its territory. The aim of the EQUIDAD study was to describe and explore potential differences in prescription requirements for new dermatology drugs across the autonomous communities. Material and methods: Cross-sectional study conducted in April and May, 2023. Two dermatologists with management responsibilities from each autonomous community reported on territorial and more local prescription requirements for drugs covered by national TPRs issued between 2016 and 2022. Results: Thirty-three researchers from 17 autonomous communities participated. The data submitted revealed between-community inequities in access to new drugs. Overall, 64.7% of the regions imposed additional prescription requirements to those mentioned in the TPRs for psoriasis. This percentage was lower for atopic dermatitis (35.3%) and melanoma (11.8%). The most common requirement for accessing a new drug was a previous prescription for another drug. Differences and additional requirements were also detected atthe local level (i.e., differences between hospitals within the same autonomous community). Conclusions: Spain's autonomous communities have multiple regional and local prescription requirements that are not aligned with national TPR recommendations. These differences result in inequitable access to new drugs for both patients and practitioners across Spain.
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- 2024
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33. Coronary and carotid artery dysfunction and KV7 overexpression in a mouse model of Hutchinson-Gilford progeria syndrome
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Macías, Álvaro, Nevado, Rosa M., González-Gómez, Cristina, Gonzalo, Pilar, Andrés-Manzano, María Jesús, Dorado, Beatriz, Benedicto, Ignacio, and Andrés, Vicente
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- 2024
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34. Clinical Prompt Learning with Frozen Language Models
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Taylor, Niall, Zhang, Yi, Joyce, Dan, Nevado-Holgado, Alejo, and Kormilitzin, Andrey
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Computer Science - Computation and Language ,ACM-class: J.2 - Abstract
Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown impressive performance on a number of natural language tasks with common benchmarking text datasets in full, few-shot, and zero-shot train-evaluation setups. Recently, it has even been observed that large but frozen pre-trained language models (PLMs) with prompt learning outperform smaller but fine-tuned models. However, as with many recent NLP trends, the performance of even the largest PLMs such as GPT-3 do not perform well on specialized domains (e.g. medical text), and the common practice to achieve State of the Art (SoTA) results still consists of pre-training and fine-tuning the PLMs on downstream tasks. The reliance on fine-tuning large PLMs is problematic in clinical settings where data is often held in non-GPU environments, and more resource efficient methods of training specialized domain models is crucial. We investigated the viability of prompt learning on clinically meaningful decision tasks and directly compared with more traditional fine-tuning methods. Results are partially in line with the prompt learning literature, with prompt learning able to match or improve on traditional fine-tuning with substantially fewer trainable parameters and requiring less training data. We argue that prompt learning therefore provides lower computational resource costs applicable to clinical settings, that can serve as an alternative to fine-tuning ever increasing in size PLMs. Complementary code to reproduce experiments presented in this work can be found at: https://github.com/NtaylorOX/Public_Clinical_Prompt., Comment: 18 pages, 6 figures, 6 tables
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- 2022
35. Endothelial-to-Mesenchymal Transition Contributes to Accelerated Atherosclerosis in Hutchinson-Gilford Progeria Syndrome
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Hamczyk, Magda R., Nevado, Rosa M., Gonzalo, Pilar, Andrés-Manzano, María J., Nogales, Paula, Quesada, Víctor, Rosado, Aránzazu, Torroja, Carlos, Sánchez-Cabo, Fátima, Dopazo, Ana, Bentzon, Jacob F., López-Otín, Carlos, and Andrés, Vicente
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- 2024
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36. Model development for bespoke large language models for digital triage assistance in mental health care
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Taylor, Niall, Kormilitzin, Andrey, Lorge, Isabelle, Nevado-Holgado, Alejo, Cipriani, Andrea, and Joyce, Dan W.
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- 2024
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37. Reducing residential cooling demand in a sprawling desert city through vertical urban densification
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Lopez-Ordoñez, Carlos, Garcia-Nevado, Elena, Crespo-Cabillo, Isabel, Roset Calzada, Jaime, and Coch, Helena
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- 2024
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38. Genomic changes and stabilization following homoploid hybrid speciation of the Oxford ragwort Senecio squalidus
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Nevado, Bruno, Chapman, Mark A., Brennan, Adrian C., Clark, James W., Wong, Edgar L.Y., Batstone, Tom, McCarthy, Shane A., Tracey, Alan, Torrance, James, Sims, Ying, Abbott, Richard J., Filatov, Dmitry, and Hiscock, Simon J.
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- 2024
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39. Quality of life in the urban context, within the paradigm of digital human capital
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Alfaro-Navarro, José-Luis, López-Ruiz, Víctor-Raúl, Huete-Alcocer, Nuria, and Nevado-Peña, Domingo
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- 2024
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40. Clinical Prompt Learning With Frozen Language Models.
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Niall Taylor, Yi Zhang, Dan W. Joyce, Ziming Gao, Andrey Kormilitzin, and Alejo J. Nevado-Holgado
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- 2024
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41. Enrichment of Immune Dysregulation Disorders in Adult Patients with Human Inborn Errors of Immunity
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Segura-Tudela, Alejandro, López-Nevado, Marta, Nieto-López, Celia, García-Jiménez, Sandra, Díaz-Madroñero, María J., Delgado, Ángeles, Cabrera-Marante, Oscar, Pleguezuelo, Daniel, Morales, Pablo, Paz-Artal, Estela, Gil-Niño, Jorge, Marco, Francisco M., Serrano, Cristina, González-Granado, Luis I., Quesada-Espinosa, Juan F., and Allende, Luis M.
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- 2024
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42. List of contributors
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Aizawa, Shin-Ichi, primary, Alberdi, Pilar, additional, Alexander, David C., additional, Alía, Alberto, additional, Allison, D.G., additional, Amyes, Sebastian G.B., additional, An, Haoran, additional, Andrade, María J., additional, Antelmann, Haike, additional, Arias, Cesar A., additional, Asensio, Miguel A., additional, Axell-House, Dierdre B., additional, Bae, Hee-Won, additional, Baena, Laura Muñoz, additional, Baig, Abdul Mannan, additional, Bailey, Spenser O., additional, Baize, Sylvain, additional, Baldi, Pablo C., additional, Barbosa, Angela Silva, additional, Barbuddhe, Sukhadeo B., additional, Bard, Emilie, additional, Barry, Eileen M., additional, Basarab, Gregory S., additional, Beloborodova, N.V, additional, Bermúdez, Elena, additional, Bidmos, Fadil A., additional, Bisgaard, Magne, additional, Blakely, Garry W., additional, Bloch, Evan, additional, Boesen, Thias Oberg, additional, Bose, Dipayan, additional, Botero, Javier Enrique, additional, Bouabe, Hicham, additional, Bouchard, Michael J., additional, Bozue, Joel A., additional, Bradbury, Richard S., additional, Brett Moreau, G., additional, Cabezas-Cruz, Alejandro, additional, Cai, Rong-Jun, additional, Calderón, Enrique J., additional, Cao, Boyang, additional, Carmena, David, additional, Carvalho, Eneas, additional, Caulfield, Amanda D., additional, Cen, Shan, additional, Chai, Jong-Yil, additional, Chamberland, Robin R., additional, Champredon, David, additional, Chan, Edward D., additional, Charbon, Godefroid, additional, Chato, Connor, additional, Chelomina, G.N., additional, Chen, Jingyu, additional, Chen, Min, additional, Chen, Shuyu, additional, Chen, Suilin, additional, Chen, Yanfei, additional, Chen, Zhaoyuan, additional, Cheng, Aimin, additional, Cheng, Keding, additional, Chiu, Charles Y., additional, Cho, You-Hee, additional, Christensen, Henrik, additional, Chrtdernevskaya, E.A., additional, Contreras, Adolfo, additional, Contreras, Marinela, additional, Córdoba, Juan J., additional, Córdoba, María G., additional, Costa, Rita, additional, Cote, Christopher K., additional, Cui, Xiangling, additional, Cui, Yujun, additional, Dacal, Elena, additional, Dammann, Allison N., additional, Das, Shubhagata, additional, Dashti, Alejandro, additional, de la Fuente, José, additional, de la Garza, Mireya, additional, Delgado, Josué, additional, Delgado-Cuesta, Juan, additional, Deng, Haiteng, additional, Deng, Li, additional, Dey, Debajit, additional, Dhama, Kuldeep, additional, Diego, Juan García-Bernalt, additional, Ding, Hao, additional, Doern, Christopher D., additional, Dorman, Charles J., additional, Du, Zongmin, additional, Dunbar, Sherry A., additional, Duthie, Malcolm, additional, Dybvig, Kevin F., additional, Eakin, Ann E., additional, Eallonardo, Samuel J., additional, Eberly, Allison R., additional, Echeverry, Adriana Jaramillo, additional, Egland, Paul G., additional, El Zowalaty, Mohamed E., additional, Endsley, Janice Jones, additional, Eom, Keeseon S., additional, Evans, Benjamin A., additional, Falkinham, Joseph O., additional, Feng, Siwei, additional, Feng, Yaoyu, additional, Feng, Zongdi, additional, Fernández-Soto, Pedro, additional, Ferreira, Roux-Cil, additional, Flores-Huerta, Nadia, additional, Foster, Timothy J., additional, Fox-Moon, Sandra M., additional, Fraga, Tatiana Rodrigues, additional, Fredricks, David N., additional, Freitag, Nancy E., additional, Frimodt-Møller, Jakob, additional, Fuller, Risa, additional, Ganesh, Balasubramanian, additional, Gao, Ning, additional, García-Carnero, Laura C., additional, Garzetti, Debora, additional, Geoghegan, Joan A., additional, Ghenim, Raed, additional, Giambartolomei, Guillermo H., additional, Gilbert, Nicole M., additional, Gillis, Thomas Phillip, additional, Gladstone, Camilla A., additional, Gómez-Gaviria, Manuela, additional, Gómez-Marín, Jorge E., additional, Gong, Tengfang, additional, González, Ramón A., additional, Gray-Owen, Scott D., additional, Gu, Bing, additional, Guzmán-Téllez, Paula, additional, Hajal, Caroline, additional, Han, Yanping, additional, Hao, Yi, additional, Harrington, Amanda T., additional, Harris, Jason B., additional, Harvill, Eric T., additional, Hasan, S. Saif, additional, He, Guang-Jun, additional, He, Yongqun, additional, Heffron, Jared D., additional, Hidalgo, Paloma, additional, Hindiyeh, Musa Y., additional, Hreha, Teri N., additional, Hu, Xiaoyu, additional, Huang, Guanghua, additional, Huang, Jiangqing, additional, Huang, Liang, additional, Huang, Shifeng, additional, Huang, Xingxu, additional, Huang, Xueting, additional, Huang, Yilun, additional, Huffman, Anthony, additional, Humphreys, Tricia L., additional, Hunstad, David A., additional, Inglis, Timothy J.J., additional, Isaac, Lourdes, additional, Jacobs, Samantha E., additional, Janowicz, Diane M., additional, Jeon, Hyeong-Kyu, additional, Ji, Quanjiang, additional, Jia, Qi, additional, Jia, Wei, additional, Jin, Shouguang, additional, Jneidi, Lama, additional, Jose, Shinsmon, additional, Jung, Bong-Kwang, additional, Kattan, Randa, additional, Kaushik, Rahul, additional, Khare, Reeti, additional, Kim, Eun Sook, additional, Kirn, Thomas J., additional, Koo, Hyun, additional, Köster, Pamela C., additional, Krause, Peter J., additional, Kumar, Sanjai, additional, Kupz, Andreas, additional, Lambert, P.A., additional, Lamont, Richard J., additional, Langford, Paul R., additional, Lebeaux, David, additional, Legname, Giuseppe, additional, Li, Bin, additional, Li, Chunhao, additional, Li, Fen, additional, Li, Jun, additional, Li, Lanjuan, additional, Li, Ruofan, additional, Li, Ruoyu, additional, Li, Ting, additional, Li, Yang-Yang, additional, Li, Yanhua, additional, Li, Zhuorong, additional, Liang, Xiaomeng, additional, Liao, Guojian, additional, Lin, Ping, additional, Ling, Yun, additional, Liu, Bo, additional, Liu, Dongyou, additional, Liu, Guohua, additional, Liu, Huidi, additional, Liu, Jiafeng, additional, Liu, Jintao, additional, Liu, Qi, additional, Liu, Shu-Lin, additional, Liu, Taiping, additional, Liu, Tongbao, additional, Liu, Wei, additional, Liu, Yan, additional, Liu, Yanni, additional, Liu, Yisong, additional, Liu, Yuan, additional, Løbner-Olesen, Anders, additional, Loeffelholz, Michael, additional, Lu, Hongzhou, additional, Luna, Brian, additional, Ma, Bingting, additional, Ma, Chengying, additional, Ma, Shuang, additional, Ma, TianLi, additional, Madan, Rajat, additional, Mahle, Rachael E., additional, Mahlen, Steven D., additional, Malik, Satya Veer Singh, additional, Malik, Yashpal Singh, additional, Malvy, Denis, additional, Mann, Barbara J., additional, Marasini, Daya, additional, Maris, Alexander S., additional, Marjomäki, Varpu, additional, Marjuki, Henju, additional, Martín, Alberto, additional, Martín, Irene, additional, Martínez-Castillo, Moisés, additional, Martínez-Pabón, María Cecilia, additional, Mathison, Blaine A., additional, Ma’ayeh, Showgy, additional, McDowell, Andrew, additional, McLaughlin, Stephanie E., additional, McSheffrey, Gordon G., additional, Medrano, Francisco J., additional, Meehan, Conor J., additional, Mehta, Dhwani, additional, Mejía-Oquendo, Manuela, additional, Melo-Cristino, José, additional, Mendoza-Barberá, Elena, additional, Meng, Xinan, additional, Merino, Susana, additional, Merritt, Adam J., additional, Miller, Steve, additional, Miller, William R., additional, Minamino, Tohru, additional, Mirzaei, Mohammadali Khan, additional, Mora-Montes, Héctor M., additional, Mortensen, Joel, additional, Mostafa, Heba H., additional, Muhsen, Khitam, additional, Mujahed, Ahlam, additional, Muro, Antonio, additional, Murphy, Olwen C., additional, Newton, Hayley J., additional, Nguyen, April H., additional, Nichols, Wright W., additional, Niu, Siqiang, additional, Núñez, Félix, additional, Obregon, Dasiel, additional, Okamoto, Akira, additional, Okutani, Akiko, additional, Olabode, Abayomi, additional, Omar, Muna, additional, Ong, Edison, additional, Ouyang, Zhiming, additional, Pacak, Christina A., additional, Pacheco-Yépez, Judith, additional, Palmer, John, additional, Pang, Xiaoli, additional, Paredes-Sabja, Daniel, additional, Peng, Zhong, additional, Peng, Zonggen, additional, Pérez-Nevado, Francisco, additional, Poon, Art, additional, Pospíšilová, Petra, additional, Potts, Caelin C., additional, Pu, Qinqin, additional, Pujic, Petar, additional, Qi, Rui, additional, Qian, Chenyun, additional, Qian, Liu, additional, Qin, Aiping, additional, Qu, Fen, additional, Rakin, Alexander, additional, Ramesh, Ashwin, additional, Ramirez, Mario, additional, Rao, Yu, additional, Ratner, Adam J., additional, Rawool, Deepak B., additional, Rehman, Asma, additional, Ren, Jie, additional, Ren, Ping, additional, Retchless, Adam C., additional, Robertson, Erle S., additional, Rodríguez, Alicia, additional, Rodriguez, Azucena, additional, Rodríguez-Medina, Carolina, additional, Rodriguez-Nava, Veronica, additional, Rohde, Manfred, additional, Romero-Rodríguez, Alba, additional, Rosales-Morgan, Gabriela, additional, Rosenkranz, Andrea L., additional, Ruiz-Moyano, Santiago, additional, Ruokolainen, Visa, additional, Sabateen, Ali, additional, Sahu, Radhakrishna, additional, Sails, Andrew, additional, Sang, Yu, additional, Santana, Clarissa H., additional, Santos, Jesus A., additional, Santos, Renato L., additional, Schmitz, Jonathan E., additional, Serrano-Luna, Jesús, additional, Shen, Jianzhong, additional, Shen, Zhangqi, additional, Shibayama, Mineko, additional, Shirtliff, Mark E., additional, Silva-Costa, Catarina, additional, Silva-Olivares, Angélica, additional, Singh, Niraj Kumar, additional, Šmajs, David, additional, Smith, Robert P., additional, Smith, Sophie, additional, Snyder, Lori A.S., additional, Song, Yinggai, additional, Soro, Aurea Simon, additional, Spearman, Paul, additional, Spellberg, Brad, additional, Sprague, Lisa D., additional, Stratton, Charles W., additional, Strenk, Susan M., additional, Strugnell, Richard A., additional, Sun, Keer, additional, Suo, Xun, additional, Suzuki-Hatano, Silveli, additional, Svärd, Staffan, additional, Talbot, Elizabeth A., additional, Tamez-Castrellón, Alma K., additional, Tan, Nie, additional, Tang, Cynthia Y., additional, Tang, Yi-Wei, additional, Tao, Jia, additional, Tao, Lili, additional, Terrero-Salcedo, David, additional, Tharmalingam, Jayaraman, additional, Thwe, Phyu M., additional, Tiamani, Kawtar, additional, Tomás, Juan M., additional, Topaz, Nadav, additional, Tsai, Ang-Chen, additional, Tsalik, Ephraim L., additional, Tuomanen, Elaine I., additional, Turenne, Christine Y., additional, Tyagi, Anuj, additional, Uprety, Priyanka, additional, Valour, Florent, additional, van Hensbergen, Vincent P., additional, Venkatesan, Arun, additional, Vergis, Jess, additional, Villar, Margarita, additional, Vollmer, Waldemar, additional, Waites, Ken B., additional, Wan, Xiu-Feng, additional, Wang, Guiqing, additional, Wang, Lijun, additional, Wang, Lin, additional, Wang, Linqi, additional, Wang, Xiangru, additional, Wang, Xin, additional, Wang, Xinjie, additional, Wang, Ya-Ting, additional, Wang, Yang, additional, Wang, Yating, additional, Weil, Ana A., additional, Welkos, Susan L., additional, Wengenack, Nancy L., additional, Westblade, Lars F., additional, Whitfield, Chris, additional, Wu, Hui, additional, Wu, Lijuan, additional, Wu, Min, additional, Wu, Yarong, additional, Wu, Zhaowei, additional, Xiang, Ye, additional, Xiao, Di, additional, Xiao, Li, additional, Xiao, Lihua, additional, Xu, Tao, additional, Xu, Wenyue, additional, Xu, Xinping, additional, Xue, Jinling, additional, Yadav, Jay Prakash, additional, Yan, Junxiang, additional, Yan, Yixin, additional, Yang, Changmei, additional, Yang, Ruifu, additional, Yang, Ying, additional, Yao, Kaihu, additional, Yao, Yu-Feng, additional, Yeakle, Kyle C., additional, Yu, Demin, additional, Yu, Hao, additional, Yu, Xue-Jie, additional, Yuan, Zhenghong, additional, Zai, Wenjing, additional, Zhang, Jianzhong, additional, Zhang, Jing-Ren, additional, Zhang, Lanyue, additional, Zhang, Lijie, additional, Zhang, Qiwei, additional, Zhang, Wenbao, additional, Zhang, Wenhong, additional, Zhang, Xinxin, additional, Zhao, Youbao, additional, Zhou, Chuanmin, additional, Zhu, Feng, additional, Zhu, Jingting, additional, and Zhu, Yongqun, additional
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- 2024
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43. Pathogenesis of fungal infections
- Author
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Ruiz-Moyano, Santiago, primary, Pérez-Nevado, Francisco, additional, Rodríguez, Alicia, additional, Córdoba, María G., additional, Martín, Alberto, additional, and Córdoba, Juan J., additional
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- 2024
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44. Millaista matematiikkaa puuseppä työssään tarvitsee ja käyttää?
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Leila Pehkonen and Laia Saló i Nevado
- Subjects
ammatillinen matematiikka ,kehollistunut matematiikka ,numeeriset taidot ,ongelmanratkaisu ,huonekalupuuseppä ,Labor. Work. Working class ,HD4801-8943 ,Business ,HF5001-6182 - Abstract
Kysymme tutkimuksessamme, millaista matematiikkaa huonekalupuusepät työssään tarvitsevat ja käyttävät. Tutkimus asettuu teoreettisesti niin sanotun kansan matematiikan kehyksiin: tarkastelemme matemaattisen professionalismin ulkopuolella olevien ryhmien käyttämää matematiikkaa. Yleensä tutkimuksissa teoreettista tietoa käytetään ohjaamaan sitä, miten asiat pitäisi käytännössä tehdä. Me taas yritämme ymmärtää käytännön toimintaa, jotta teoreettista tietoa voitaisiin mukauttaa vastaamaan paremmin käytäntöjä. Tarkastelemme ensin sellaista arkipäivän työssä käytettävää matematiikkaa, jonka tutkittavat itse tunnistavat ja nimeävät matematiikaksi. Toiseksi tarkastelemme heidän työssään kohtaamiaan ongelmanratkaisua vaativia tilanteita. Keskeinen käsite tarkastelussamme on matemaattinen tieto, joka on osa numeerisia taitoja. Ongelmanratkaisutilanteet osana matematiikkaa voivat olla joko hyvin tai huonosti jäsenneltyjä. Edellisiin ratkaisut ovat täsmällisiä, usein yksiselitteisiä. Jälkimmäiset taas mahdollistavat monenlaiset ratkaisut. Tutkimustehtävään vastaamiseksi olemme käyttäneet kahdenlaista aineistoa: neljän huonekalupuusepän työpajoissa tuotettua etnografista haastattelu-, havainnointi- ja kuva-aineistoa sekä kolmen muun huonekalupuusepän haastatteluaineistoa. Analyysimme mukaan arjen rutiineissa huonekalupuusepät selviävät kontekstisidonnaisella matemaattisella tiedolla, josta osa on kehollistunutta. Ongelmanratkaisutilanteissa taas aktivoituu edellisten ohella myös käsitteellisempi matemaattinen tieto.
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- 2024
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45. Rationale production to support clinical decision-making
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Taylor, Niall, Sha, Lei, Joyce, Dan W, Lukasiewicz, Thomas, Nevado-Holgado, Alejo, and Kormilitzin, Andrey
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
The development of neural networks for clinical artificial intelligence (AI) is reliant on interpretability, transparency, and performance. The need to delve into the black-box neural network and derive interpretable explanations of model output is paramount. A task of high clinical importance is predicting the likelihood of a patient being readmitted to hospital in the near future to enable efficient triage. With the increasing adoption of electronic health records (EHRs), there is great interest in applications of natural language processing (NLP) to clinical free-text contained within EHRs. In this work, we apply InfoCal, the current state-of-the-art model that produces extractive rationales for its predictions, to the task of predicting hospital readmission using hospital discharge notes. We compare extractive rationales produced by InfoCal to competitive transformer-based models pretrained on clinical text data and for which the attention mechanism can be used for interpretation. We find each presented model with selected interpretability or feature importance methods yield varying results, with clinical language domain expertise and pretraining critical to performance and subsequent interpretability., Comment: Machine Learning for Health (ML4H) - Extended Abstract
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- 2021
46. Balance y reivindicación de los siete años de Hastapenak
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Juan Luis Nevado Encinas
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History (General) ,D1-2009 ,Ethnology. Social and cultural anthropology ,GN301-674 - Published
- 2023
47. La actividad en Tiktok de los medios impresos en España
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Fernando Galindo-Rubio, Ángel Fuentes Nevado, and Fernando Suárez-Carballo
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tiktok ,tecnología ,periodismo ,redes sociales ,información ,Communication. Mass media ,P87-96 - Abstract
La red social TikTok se presenta como un formato de comunicación preponderantemente de entretenimiento no solo para las generaciones más jóvenes, sino para un público cada vez más seducido por los algoritmos de recomendación. En este contexto, los medios de información tradicionales plantean sus estrategias de desembarco en TikTok, conscientes de que en ella están ahora las audiencias futuras a las que poder fidelizar. Esta investigación tiene como principal objetivo revisar la actividad de los principales medios impresos de España. Se pretende analizar si el uso de TikTok de los medios impresos en España es o no significativo, si existe una profesionalización estratégica de la actividad editorial de estos medios y, finalmente, si esta actividad se realiza ad hoc, en función de las peculiaridades de la plataforma o si la dinámica es el reciclado de contenido del medio principal. Para ello, se revisarán los posts publicados en la red social por las principales cabeceras nacionales: El País, El Mundo, ABC, La Vanguardia y La Razón, en una secuencia temporal concreta que establece un análisis comparativo entre los principales hitos de interacción que proporciona dicha red social. Las principales conclusiones establecen cómo existe un intento mayor de lo esperado en la actividad que los medios impresos plantean en TikTok. Sin embargo, a pesar de ello, es una actividad poco profesionalizada y reflexionada, ya que no existe una pauta clara de publicación, continuidad o producción específica, sino más bien un contenedor más en el que volcar contenido ya producido.
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- 2023
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48. The influence of match status on the conditional characteristics of tactical sprint actions in professional soccer players
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David Lobo-Triviño, Tomás García-Calvo, Ana Rubio-Morales, Fabio Nevado, Marcos Chena, Juan Piñero-Madrona, Emilio Martín-Ardila, and Javier Raya-González
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football ,team-sport ,high-intensity running ,tactical analysis ,tracking system ,Sports medicine ,RC1200-1245 ,Biology (General) ,QH301-705.5 - Abstract
This study aimed to analyse the influence of the match status on the conditional characteristics of tactical sprint actions among Spanish professional soccer players, considering playing positions. Thirty-two Spanish male professional soccer players from a LaLiga Spanish Second Division (LaLiga SmarthBank) team participated in this study. Actions above 85% of the players’ maximum velocity were analysed based on their tactical purpose. These findings provide valuable information regarding the tactical aspects of sprinting in soccer, emphasizing the influence of playing positions and match status on the distribution of tactical sprint actions. No effects of match status were observed for any game phase. However, when tactical actions were individually studied, it was observed that the maximum velocity in Chase actions was higher when the team was winning, while in Press actions, the maximum velocity was higher when the team was losing and in in Run in behind/Penetrate, the maximum velocity was higher in drawing situations compared to losing situations. No effects of match status on the distance covered during sprinting were observed, and regarding duration, significant differences were only observed in Recovery run actions. In addition, the influence of match status is higher when playing positions are considered, although the within playing positions analysis revealed significant differences only in CM players. These findings provide valuable information for the design of specific training drills considering playing positions, suggesting the need to analyse the previous match in order to structure the training load of the microcycle in a comprehensive manner.
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- 2023
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49. Comparison of the Latin America Regulation Landscape and International Reference Health Authorities to Hasten Drug Registration and Clinical Research Applications
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Argotti, Urimara, Leyens, Lada, Lisbona, Carlos, López, Pilar, Alonso-Orgaz, Sergio, Nevado, Angel, and Cozzi, Virginia
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- 2023
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50. Tailored Prevention of Functional Decline through a Multicomponent Exercise Program in Hospitalized Oncogeriatric Patients: Study Protocol for a Randomized Clinical Trial
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Martínez-Velilla, Nicolas, Arrazubi, V., Zambom-Ferraresi, F., Morilla-Ruiz, I., Sáez de Asteasuu, M. L., Ramírez-Vélez, R., Zambom-Ferraresi, F., De la Casa-Marín, A., Ollo-Martínez, I., Gorospe-García, I., Gurruchaga-Sotés, I., Galbete, A., Cedeño-Veloz, B. A., Martín-Nevado, L., Izquierdo, M., and Vera, R.
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- 2023
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