Recursos lingüísticos empleados en una traducción del siglo XV

Los últimos decenios del s. XIV y primeros del XV muestran claros síntomas de la presencia de nuevas directrices culturales que van a provocar la admiración y el interés por todo lo que representa el mundo greco-Iatino, que se convierte así en un modelo ideal al que hay que imitar; hecho que determinó una de las transformaciones más decisivas en el curso de la historia de España en su ámbito cultural. En este proceso desempeña un papel crucial la labor realizada por D. Enrique de Villena, o de Aragón (1384-1434), autor, que tanto en su producción en prosa como en verso, trató de transplantar a nuestra lengua, los nuevos conceptos que descubrió en ese mundo clásico. Ello provocó al tratar de convertirla en vehículo de expresión digno de verter a ella los textos latinos, el desarrollo de una fecunda tarea de enriquecimiento de la lengua romance por no fallar equivalentes vocablos en la romancial texedura en el rudo y desierto romance, para exprimir los angelicos cocebimientos virgilianos (1). Para elaborar el presente trabajo, sometemos a análisis la traducción realizada por Villena de La Eneida de Virgilio. Nos centramos en el Libro I de la traducción para la determinación de los diversos procedimientos lingüísticos utilizados por el autor al llevar a cabo la traducción y adaptación de los términos latino

Comparative study of the primary cilia in thyrocytes of adult mammals

Since their discovery in different human tissues by Zimmermann in 1898, primary cilia have been found in the vast majority of cell types in vertebrates. Primary cilia are considered to be cellular antennae that occupy an ideal cellular location for the interpretation of information both from the environment and from other cells. To date, in mammalian thyroid gland, primary cilia have been found in the thyrocytes of humans and dogs (fetuses and adults) and in rat embryos. The present study investigated whether the existence of this organelle in follicular cells is a general event in the postnatal thyroid gland of different mammals, using both immunolabeling by immunofluorescence and electron microscopy. Furthermore, we aimed to analyse the presence of primary cilia in various thyroid cell lines. According to our results, primary cilia are present in the adult thyroid gland of most mammal species we studied (human, pig, guinea pig and rabbit), usually as a single copy per follicular cell. Strikingly, they were not found in rat or mouse thyroid tissues. Similarly, cilia were also observed in all human thyroid cell lines tested, both normal and neoplastic follicular cells, but not in cultured thyrocytes of rat origin. We hypothesize that primary cilia could be involved in the regulation of normal thyroid function through specific signaling pathways. Nevertheless, further studies are needed to shed light on the permanence of these organelles in the thyroid gland of most species during postnatal life.Junta de Andalucía. Consejería de Innovación, Ciencia y Empresa CTS-439/2011Junta de Andalucía. Consejería de Innovación, Ciencia y Empresa CTS-229/2011Junta de Andalucía. Consejería de Salud PI-0051-201

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep-learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep-learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our single-cell cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues.This work is supported by the project PID2019-103900GB-I00 funded by MCIN/AEI /10.13039/501100011033 and Programa Operativo FEDER Andalucía 2014–2020 (US-1380953) to L.M.E. Work by L.M.E. and J.A.A.-S.R. has been funded by the Junta de Andalucía (Consejerı´a de economı´a, conocimiento, empresas y Universidad) grant PY18-631 co-funded by FEDER funds. A.T. has been funded by a ‘‘Contrato predoctoral PIF’’ from Universidad de Sevilla. C.G.-V. has been funded by a ‘‘Contrato predoctoral para la formacio´ n de doctores’’ BES-2017-082306. G.B. was supported by a Comunidad de Madrid contract (CAM) and by an FPI grant from MINECO (BES-2022-077789). F.M.-B. was supported by MICINN (PID2020-120367GB-I00) and Fundacio´ n Ramo´ n Areces (CIVP18A3904). P.G.-G. has been funded by Margarita Salas Fellowship – NextGenerationEU. C.H.F.-E. has been funded by Marı´a Zambrano Fellowship – NextGenerationEU. I.A.-C. would like to acknowledge that his work has been partially supported by the University of the Basque Country UPV/EHU grant GIU19/027 and by grant PID2021-126701OB-I00, funded by MCIN/AEI/10.13039/501100011033 and by ‘‘ERDF A way of making Europe." L.M.E. also wants to thank PIE-202120E047 – Conexiones-Life network for networking and input

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep-learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep-learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our single-cell cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep-learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep-learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our single-cell cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues.Ministerio de Ciencia e Innovación PID2019-103900GB-I00, PID2020-120367GB-I00, PID2021-126701OB-I00Junta de Andalucía US-1380953, PY18-631Ministerio de Economía y Competitividad BES-2022-07778

Supplemental information CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Document S1. Figures S1–S6 Table S1. Extracted features from 353 curated cysts (104 cysts at 4 days, 103 cysts at 7 days, 116 cysts at 10 days), related to Figure 2 Table S2. Hyperparameter search space for our proposed 3D ResU-Net, related to Figure 1 Table S3. Performance evaluation of our pipeline (CartoCell) on images of different epithelial tissues and comparison with other state-of-the-art segmentation methods, using the evaluation metrics described in STAR Methods, related to Figure 1 Table S4. Relative error between features extracted using automatically segmented cysts and manually curated cysts (STAR Methods), related to Figure 1 Table S5. Cyst morphology and scutoid location statistics, related to Figure 2 Table S6. Comparison of morphology and packing features of normoxic and hypoxic MDCK cysts, related to Figure 2 Table S7. Classification of the developmental stages of Drosophila egg chambers employed, related to Figure 3 Document S2. Article plus supplemental informationPeer reviewe

Severe manifestations of SARS-CoV-2 in children and adolescents: from COVID-19 pneumonia to multisystem inflammatory syndrome: a multicentre study in pediatric intensive care units in Spain

Background Multisystem inflammatory syndrome temporally associated with COVID-19 (MIS-C) has been described as a novel and often severe presentation of SARS-CoV-2 infection in children. We aimed to describe the characteristics of children admitted to Pediatric Intensive Care Units (PICUs) presenting with MIS-C in comparison with those admitted with SARS-CoV-2 infection with other features such as COVID-19 pneumonia. Methods A multicentric prospective national registry including 47 PICUs was carried out. Data from children admitted with confirmed SARS-CoV-2 infection or fulfilling MIS-C criteria (with or without SARS-CoV-2 PCR confirmation) were collected. Clinical, laboratory and therapeutic features between MIS-C and non-MIS-C patients were compared. Results Seventy-four children were recruited. Sixty-one percent met MIS-C definition. MIS-C patients were older than non-MIS-C patients (p = 0.002): 9.4 years (IQR 5.5-11.8) vs 3.4 years (IQR 0.4-9.4). A higher proportion of them had no previous medical history of interest (88.2% vs 51.7%, p = 0.005). Non-MIS-C patients presented more frequently with respiratory distress (60.7% vs 13.3%, p < 0.001). MIS-C patients showed higher prevalence of fever (95.6% vs 64.3%, p < 0.001), diarrhea (66.7% vs 11.5%, p < 0.001), vomits (71.1% vs 23.1%, p = 0.001), fatigue (65.9% vs 36%, p = 0.016), shock (84.4% vs 13.8%, p < 0.001) and cardiac dysfunction (53.3% vs 10.3%, p = 0.001). MIS-C group had a lower lymphocyte count (p < 0.001) and LDH (p = 0.001) but higher neutrophil count (p = 0.045), neutrophil/lymphocyte ratio (p < 0.001), C-reactive protein (p < 0.001) and procalcitonin (p < 0.001). Patients in the MIS-C group were less likely to receive invasive ventilation (13.3% vs 41.4%, p = 0.005) but were more often treated with vasoactive drugs (66.7% vs 24.1%, p < 0.001), corticosteroids (80% vs 44.8%, p = 0.003) and immunoglobulins (51.1% vs 6.9%, p < 0.001). Most patients were discharged from PICU by the end of data collection with a median length of stay of 5 days (IQR 2.5-8 days) in the MIS-C group. Three patients died, none of them belonged to the MIS-C group. Conclusions MIS-C seems to be the most frequent presentation among critically ill children with SARS-CoV-2 infection. MIS-C patients are older and usually healthy. They show a higher prevalence of gastrointestinal symptoms and shock and are more likely to receive vasoactive drugs and immunomodulators and less likely to need mechanical ventilation than non-MIS-C patients

Severe manifestations of SARS-CoV-2 in children and adolescents: from COVID-19 pneumonia to multisystem inflammatory syndrome: a multicentre study in pediatric intensive care units in Spain

Background Multisystem inflammatory syndrome temporally associated with COVID-19 (MIS-C) has been described as a novel and often severe presentation of SARS-CoV-2 infection in children. We aimed to describe the characteristics of children admitted to Pediatric Intensive Care Units (PICUs) presenting with MIS-C in comparison with those admitted with SARS-CoV-2 infection with other features such as COVID-19 pneumonia. Methods A multicentric prospective national registry including 47 PICUs was carried out. Data from children admitted with confirmed SARS-CoV-2 infection or fulfilling MIS-C criteria (with or without SARS-CoV-2 PCR confirmation) were collected. Clinical, laboratory and therapeutic features between MIS-C and non-MIS-C patients were compared. Results Seventy-four children were recruited. Sixty-one percent met MIS-C definition. MIS-C patients were older than non-MIS-C patients (p = 0.002): 9.4 years (IQR 5.5–11.8) vs 3.4 years (IQR 0.4–9.4). A higher proportion of them had no previous medical history of interest (88.2% vs 51.7%, p = 0.005). Non-MIS-C patients presented more frequently with respiratory distress (60.7% vs 13.3%, p < 0.001). MIS-C patients showed higher prevalence of fever (95.6% vs 64.3%, p < 0.001), diarrhea (66.7% vs 11.5%, p < 0.001), vomits (71.1% vs 23.1%, p = 0.001), fatigue (65.9% vs 36%, p = 0.016), shock (84.4% vs 13.8%, p < 0.001) and cardiac dysfunction (53.3% vs 10.3%, p = 0.001). MIS-C group had a lower lymphocyte count (p < 0.001) and LDH (p = 0.001) but higher neutrophil count (p = 0.045), neutrophil/lymphocyte ratio (p < 0.001), C-reactive protein (p < 0.001) and procalcitonin (p < 0.001). Patients in the MIS-C group were less likely to receive invasive ventilation (13.3% vs 41.4%, p = 0.005) but were more often treated with vasoactive drugs (66.7% vs 24.1%, p < 0.001), corticosteroids (80% vs 44.8%, p = 0.003) and immunoglobulins (51.1% vs 6.9%, p < 0.001). Most patients were discharged from PICU by the end of data collection with a median length of stay of 5 days (IQR 2.5–8 days) in the MIS-C group. Three patients died, none of them belonged to the MIS-C group. Conclusions MIS-C seems to be the most frequent presentation among critically ill children with SARS-CoV-2 infection. MIS-C patients are older and usually healthy. They show a higher prevalence of gastrointestinal symptoms and shock and are more likely to receive vasoactive drugs and immunomodulators and less likely to need mechanical ventilation than non-MIS-C patients

Long-Term Real-World Effectiveness and Safety of Ustekinumab in Crohn’s Disease Patients: The SUSTAIN Study

Background Large real-world-evidence studies are required to confirm the durability of response, effectiveness, and safety of ustekinumab in Crohn’s disease (CD) patients in real-world clinical practice. Methods A retrospective, multicentre study was conducted in Spain in patients with active CD who had received ≥1 intravenous dose of ustekinumab for ≥6 months. Primary outcome was ustekinumab retention rate; secondary outcomes were to identify predictive factors for drug retention, short-term remission (week 16), loss of response and predictive factors for short-term efficacy and loss of response, and ustekinumab safety. Results A total of 463 patients were included. Mean baseline Harvey-Bradshaw Index was 8.4. A total of 447 (96.5%) patients had received prior biologic therapy, 141 (30.5%) of whom had received ≥3 agents. In addition, 35.2% received concomitant immunosuppressants, and 47.1% had ≥1 abdominal surgery. At week 16, 56% had remission, 70% had response, and 26.1% required dose escalation or intensification; of these, 24.8% did not subsequently reduce dose. After a median follow-up of 15 months, 356 (77%) patients continued treatment. The incidence rate of ustekinumab discontinuation was 18% per patient-year of follow-up. Previous intestinal surgery and concomitant steroid treatment were associated with higher risk of ustekinumab discontinuation, while a maintenance schedule every 12 weeks had a lower risk; neither concomitant immunosuppressants nor the number of previous biologics were associated with ustekinumab discontinuation risk. Fifty adverse events were reported in 39 (8.4%) patients; 4 of them were severe (2 infections, 1 malignancy, and 1 fever). Conclusions Ustekinumab is effective and safe as short- and long-term treatment in a refractory cohort of CD patients in real-world clinical practice

Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab

Ustekinumab has shown efficacy in Crohn's Disease (CD) patients. To identify patient profiles of those who benefit the most from this treatment would help to position this drug in the therapeutic paradigm of CD and generate hypotheses for future trials. The objective of this analysis was to determine whether baseline patient characteristics are predictive of remission and the drug durability of ustekinumab, and whether its positioning with respect to prior use of biologics has a significant effect after correcting for disease severity and phenotype at baseline using interpretable machine learning. Patients' data from SUSTAIN, a retrospective multicenter single-arm cohort study, were used. Disease phenotype, baseline laboratory data, and prior treatment characteristics were documented. Clinical remission was defined as the Harvey Bradshaw Index <= 4 and was tracked longitudinally. Drug durability was defined as the time until a patient discontinued treatment. A total of 439 participants from 60 centers were included and a total of 20 baseline covariates considered. Less exposure to previous biologics had a positive effect on remission, even after controlling for baseline disease severity using a non-linear, additive, multivariable model. Additionally, age, body mass index, and fecal calprotectin at baseline were found to be statistically significant as independent negative risk factors for both remission and drug survival, with further risk factors identified for remission