Instrumento para evaluar la incidencia de los factores asociados al rol docente, investigativo y administrativo realizados por docentes con el síndrome de burnout

<a href="/17788/1/s40537-025-01167-w.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis

The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.

Producción Científica

Hafiz Muhammad Raza Ur Rehman mail , Mahpara Saleem mail , Muhammad Zeeshan Jhandir mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Helena Garay mail helena.garay@uneatlantico.es, Imran Ashraf mail ,

Raza Ur Rehman

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Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review

Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids.

Producción Científica

Oussama Khouili mail , Mohamed Hanine mail , Mohamed Louzazni mail , Miguel Ángel López Flores mail miguelangel.lopez@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Imran Ashraf mail ,

Khouili

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Measurement of chest muscle mass in COVID-19 patients on mechanical ventilation using tomography

Background: Sarcopenia, characterized by a reduction in skeletal muscle mass and function, is a prevalent complication in the Intensive Care Unit (ICU) and is related to increased mortality. This study aims to determine whether muscle and fat mass measurements at the T12 and L1 vertebrae using chest tomography can predict mortality among critically ill COVID-19 patients requiring invasive mechanical ventilation (MV). Methods: Fifty-one critically ill COVID-19 patients on MV underwent chest tomography within 72 h of ICU admission. Muscle mass was measured using the Core Slicer program. Results: After adjustment for potential confounding factors related to background and clinical parameters, a 1-unit increase in muscle mass, subcutaneous, and intra-abdominal fat mass at the L1 level was associated with approximately 1–2% lower odds of negative outcomes and in-hospital mortality. No significant association was found between muscle mass at the T12 level and patient outcomes. Furthermore, no significant results were observed when considering a 1-standard deviation increase as the exposure variable. Conclusion: Measuring muscle mass using chest tomography at the T12 level does not effectively predict outcomes for ICU patients. However, muscle and fat mass at the L1 level may be associated with a lower risk of negative outcomes. Additional studies should explore other potential markers or methods to improve prognostic accuracy in this critically ill population.

Producción Científica

Natalia Daniela Llobera mail , Evelyn Frias-Toral mail , Mariel Aquino mail , María Jimena Reberendo mail , Laura Cardona Díaz mail , Adriana García mail , Martha Montalván mail , Álvaro Velarde Sotres mail alvaro.velarde@uneatlantico.es, Sebastián Chapela mail ,

Llobera

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Olive Leaf Extracts With High, Medium, or Low Bioactive Compounds Content Differentially Modulate Alzheimer's Disease via Redox Biology

Alzheimer's disease (AD) involves β-amyloid plaques and tau hyperphosphorylation, driven by oxidative stress and neuroinflammation. Cyclooxygenase-2 (COX-2) and acetylcholinesterase (AChE) activities exacerbate AD pathology. Olive leaf (OL) extracts, rich in bioactive compounds, offer potential therapeutic benefits. This study aimed to assess the anti-inflammatory, anti-cholinergic, and antioxidant effects of three OL extracts (low, mid, and high bioactive content) in vitro and their protective effects against AD-related proteinopathies in Caenorhabditis elegans models. OL extracts were characterized for phenolic composition, AChE and COX-2 inhibition, as well as antioxidant capacity. Their effects on intracellular and mitochondrial reactive oxygen species (ROS) were tested in C. elegans models expressing human Aβ and tau proteins. Gene expression analyses examined transcription factors (DAF-16, skinhead [SKN]-1) and their targets (superoxide dismutase [SOD]-2, SOD-3, GST-4, and heat shock protein [HSP]-16.2). High-OL extract demonstrated superior AChE and COX-2 inhibition and antioxidant capacity. Low- and high-OL extracts reduced Aβ aggregation, ROS levels, and proteotoxicity via SKN-1/NRF-2 and DAF-16/FOXO pathways, whereas mid-OL showed moderate effects through proteostasis modulation. In tau models, low- and high-OL extracts mitigated mitochondrial ROS levels via SOD-2 but had limited effects on intracellular ROS levels. High-OL extract also increased GST-4 levels, whereas low and mid extracts enhanced GST-4 levels. OL extracts protect against AD-related proteinopathies by modulating oxidative stress, inflammation, and proteostasis. High-OL extract showed the most promise for nutraceutical development due to its robust phenolic profile and activation of key antioxidant pathways. Further research is needed to confirm long-term efficacy.

Producción Científica

Jose M. Romero‐Marquez mail , María D. Navarro‐Hortal mail , Alfonso Varela‐López mail , Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Juan G. Puentes mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Cristina Sánchez‐González mail , Jianbo Xiao mail , Roberto García‐Ruiz mail , Sebastián Sánchez mail , Tamara Y. Forbes‐Hernández mail , José L. Quiles mail jose.quiles@uneatlantico.es,

Romero‐Marquez

<a href="/17570/1/eFood%20-%202025%20-%20Navarro%E2%80%90Hortal%20-%20Effects%20of%20a%20Garlic%20Hydrophilic%20Extract%20Rich%20in%20Sulfur%20Compounds%20on%20Redox%20Biology%20and.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Effects of a Garlic Hydrophilic Extract Rich in Sulfur Compounds on Redox Biology and Alzheimer's Disease Markers in Caenorhabditis Elegans

Garlic is a horticultural product highly valued for its culinary and medicinal attributes. The aim of this study was to evaluate the composition of a garlic hydrophilic extract as well as the influence on redox biology, Alzheimer's Disease (AD) markers and aging, using Caenorhabditis elegans as experimental model. The extract was rich in sulfur compounds, highlighting the presence of other compounds like phenolics, and the antioxidant property was corroborated. Regarding AD markers, the acetylcholinesterase inhibitory capacity was demonstrated in vitro. Although the extract did not modify the amyloid β-induced paralysis degree, it was able to improve, in a dose-dependent manner, some locomotive parameters affected by the hyperphosphorylated tau protein in C. elegans. It could be related to the effect found on GFP-transgenic stains, mainly regarding to the increase in the gene expression of HSP-16.2. Moreover, an initial investigation into the aging process revealed that the extract successfully inhibited the accumulation of intracellular and mitochondrial reactive oxygen species in aged worms. These results provide valuable insights into the multifaceted impact of garlic extract, particularly in the context of aging and neurodegenerative processes. This study lays a foundation for further research avenues exploring the intricate molecular mechanisms underlying garlic effects and its translation into potential therapeutic interventions for age-related neurodegenerative conditions.

Producción Científica

María D. Navarro‐Hortal mail , Jose M. Romero‐Marquez mail , Johura Ansary mail , Cristina Montalbán‐Hernández mail , Alfonso Varela‐López mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Jianbo Xiao mail , Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Cristina Sánchez‐González mail , Tamara Y. Forbes‐Hernández mail , José L. Quiles mail jose.quiles@uneatlantico.es,

Navarro‐Hortal

Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales

Tipo de documento: Tesis (Doctoral)

Fecha de publicación: 2021-04-08

URI: https://repositorio.unini.edu.mx/id/eprint/695

Resumen:

Las condiciones laborales actuales complejas, debido a la combinación de metas establecidas, recursos disponibles y estilos de liderazgo, potencializa factores que afectan la salud física y mental de trabajadores. Un sector afectado por la presencia del Síndrome de Burnout es el que labora en trabajos de servicio, como son los médicos, enfermeras y docentes. Freudenberger inicia el uso del término burnout en 1974, para describir el estrés crónico que llegaban a padecer personas que trabajaban en actividades de servicio. Las investigaciones sobre este tema han ido desarrollándose con los años. En 1981, Maslach & Jackson presentan un instrumento, el Maslach Burnout Inventory (MBI), para evaluar la presencia del Síndrome de Burnout en docentes. En el 2013, Arquero & Donoso, presentan un instrumento, partiendo de la versión del MBI para evaluar la presencia del Síndrome de Burnout en docentes de nivel superior, considerando actividades de investigación, sin embargo, no contempla la multiplicidad de actividades. En 2016, Ilaja & Reyes, señalan la necesidad de la evaluación considerando la multiplicidad de actividades. Es por ello por lo que se identifica y define la necesidad de generar una herramienta validada para este tipo de evaluación. El instrumento Burnout-DOINAD diseñado se valida por 4 expertos y una muestra de 30 docentes, obteniendo un coeficiente de Kendall de 0.425, y un coeficiente de Alfa de Cronbach de 0.953. Posterior a su validación, el instrumento se aplica a una muestra representativa de 147 docentes, obteniendo un coeficiente de Alfa de Cronbach de 0.946. Se diseña y valida un modelo estadístico para identificar los factores con mayor incidencia y se definen indicadores que proporcionen información objetiva a líderes de organizaciones educativas. Se identifica que el rol administrativo como uno de los factores de mayor incidencia para la presencia de un nivel alto de burnout en los docentes. Una limitante en la investigación fue el número de docentes participantes.