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Síntomas depresivos en estudiantes universitarios de la Carrera de Medicina en la Universidad Técnica de Manabí, Ecuador
Thesis
Subjects > Psychology
Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
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Introducción: La depresión es una enfermedad disruptiva del estado de ánimo con morbilidad asociada donde se experimenta tristeza recurrente y persistente que suele cronificarse con el tiempo, pérdida del interés por las cosas que antes le resultaban placenteras; afectando las áreas vitales. La presente investigación surge de la necesidad de indagar la manifestación clínica de síntomas depresivos en los estudiantes en formación profesional en medicina, con el fin de corroborar la problemática biopsicosocial que viven los estudiantes por los altos niveles de estrés que se encuentran enfrentando diariamente. Objetivo: El objetivo de la investigación es evaluar los síntomas depresivos en los estudiantes de sexto, séptimo y octavo semestre de la carrera de Medicina de la Universidad Técnica de Manabí, Ecuador. Metodología: Para ello se realizó un estudio con diseño observacional, exploratorio y descriptivo, de corte transversal; obteniendo así representaciones importantes de las distintas categorías presentes en el Inventario de Depresión de Beck BDI-II. Resultados: En los resultaron se evidenciaron el predominio del sexo femenino con un 57% y el estado civil de soltero es la mayoría de los estudiantes encuestados con un 96%, la edad predominante es entre los 22 y 24 años con un 61%. Los ítems de Tristeza, Sentimientos de culpa, Pérdida de interés, Pérdida de energía, Autocrítica, Cambios en los hábitos de sueño, Cansancio o fatiga; fueron los que presentaron respuestas más críticas; con porcentajes entre 32% y 50%. La prueba de homogeneidad de varianza mediante el estadístico de Levene, se cumple, a excepción de los ítems 05 (sentimientos de culpa) y 15 (pérdida de energía), por tanto, para los ítems 05 y 15 se lleva a cabo la prueba no paramétrica de Kruskall Wallis. Para comprobación de la normalidad está la prueba de Kolmogorov-Smirnov, en el cual todos los p-valores fueron muy cercanos a cero lo que indican el no cumplimiento de la normalidad, sin embargo, no es un requisito fundamental para los ANOVAS. En lo Anovas se probó la siguiente hipótesis, que el puntaje promedio de los estudiantes del sexto, séptimo y octavo curso no tiene diferencias significativas en cuanto a cada ítem. Conclusión: Atendiendo al diseño de investigación, se pudo observar que la mitad de los estudiantes no revelan sintomatología depresiva; sin embargo, la otra mitad si arroja un nivel de depresión con riesgo moderado de 22%, depresión leve 15% y depresión severa con el 13%; encontrándose que la mitad de la población estudiada si se adolece con la expresión de los síntomas que señala el inventario BDI-II.
metadata
Avellan Cedeño, Maria Lisbeth
mail
marialis_avell91@hotmail.com
(2023)
Síntomas depresivos en estudiantes universitarios de la Carrera de Medicina en la Universidad Técnica de Manabí, Ecuador.
Masters thesis, UNSPECIFIED.
Abstract
Introducción: La depresión es una enfermedad disruptiva del estado de ánimo con morbilidad asociada donde se experimenta tristeza recurrente y persistente que suele cronificarse con el tiempo, pérdida del interés por las cosas que antes le resultaban placenteras; afectando las áreas vitales. La presente investigación surge de la necesidad de indagar la manifestación clínica de síntomas depresivos en los estudiantes en formación profesional en medicina, con el fin de corroborar la problemática biopsicosocial que viven los estudiantes por los altos niveles de estrés que se encuentran enfrentando diariamente. Objetivo: El objetivo de la investigación es evaluar los síntomas depresivos en los estudiantes de sexto, séptimo y octavo semestre de la carrera de Medicina de la Universidad Técnica de Manabí, Ecuador. Metodología: Para ello se realizó un estudio con diseño observacional, exploratorio y descriptivo, de corte transversal; obteniendo así representaciones importantes de las distintas categorías presentes en el Inventario de Depresión de Beck BDI-II. Resultados: En los resultaron se evidenciaron el predominio del sexo femenino con un 57% y el estado civil de soltero es la mayoría de los estudiantes encuestados con un 96%, la edad predominante es entre los 22 y 24 años con un 61%. Los ítems de Tristeza, Sentimientos de culpa, Pérdida de interés, Pérdida de energía, Autocrítica, Cambios en los hábitos de sueño, Cansancio o fatiga; fueron los que presentaron respuestas más críticas; con porcentajes entre 32% y 50%. La prueba de homogeneidad de varianza mediante el estadístico de Levene, se cumple, a excepción de los ítems 05 (sentimientos de culpa) y 15 (pérdida de energía), por tanto, para los ítems 05 y 15 se lleva a cabo la prueba no paramétrica de Kruskall Wallis. Para comprobación de la normalidad está la prueba de Kolmogorov-Smirnov, en el cual todos los p-valores fueron muy cercanos a cero lo que indican el no cumplimiento de la normalidad, sin embargo, no es un requisito fundamental para los ANOVAS. En lo Anovas se probó la siguiente hipótesis, que el puntaje promedio de los estudiantes del sexto, séptimo y octavo curso no tiene diferencias significativas en cuanto a cada ítem. Conclusión: Atendiendo al diseño de investigación, se pudo observar que la mitad de los estudiantes no revelan sintomatología depresiva; sin embargo, la otra mitad si arroja un nivel de depresión con riesgo moderado de 22%, depresión leve 15% y depresión severa con el 13%; encontrándose que la mitad de la población estudiada si se adolece con la expresión de los síntomas que señala el inventario BDI-II.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Depresión, Sintomatología, Salud mental, Estrés, Estudiante de medicina |
| Subjects: | Subjects > Psychology |
| Divisions: | Europe University of Atlantic > Teaching > Final Master Projects Ibero-american International University > Teaching > Final Master Projects |
| Date Deposited: | 02 Dec 2024 23:30 |
| Last Modified: | 03 Dec 2024 23:30 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/8317 |
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Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice
The health benefits of mulberry fruit are closely associated with its phenolic compounds. However, the effects of enzymatic treatments on the digestion patterns of these compounds in mulberry juice remain largely unknown. This study investigated the impact of pectinase (PE), pectin lyase (PL), and cellulase (CE) on the release of phenolic compounds in whole mulberry juice. The digestion patterns were further evaluated using an in vitro simulated digestion model. The results revealed that PE significantly increased chlorogenic acid content by 77.8 %, PL enhanced cyanidin-3-O-glucoside by 20.5 %, and CE boosted quercetin by 44.5 %. Following in vitro digestion, the phenolic compound levels decreased differently depending on the treatment, while cyanidin-3-O-rutinoside content increased across all groups. In conclusion, the selected enzymes effectively promoted the release of phenolic compounds in mulberry juice. However, during gastrointestinal digestion, the degradation of phenolic compounds surpassed their enhanced release, with effects varying based on the compound's structure.
Peihuan Luo mail , Jian Ai mail , Qiongyao Wang mail , Yihang Lou mail , Zhiwei Liao mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Elwira Sieniawska mail , Weibin Bai mail , Lingmin Tian mail ,
Luo
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A novel machine learning-based proposal for early prediction of endometriosis disease
Background Endometriosis is one of the causes of female infertility, with some studies estimating its prevalence at around 10 % of reproductive-age women worldwide and between 30 and 50 % in symptomatic women. However, its diagnosis is complex and often delayed, highlighting the need for more accessible and accurate diagnostic methods. The difficulty lies in its diverse etiology and the variability of symptoms among those affected. Methods This study proposes a predictive model based on supervised machine learning for the early identification of endometriosis, providing support for decision-making by healthcare professionals. For this purpose, an anonymised dataset of 5,143 female patients diagnosed with endometriosis at the private fertility clinic Inebir was used. The model integrates clinical records and genetic analysis through supervised machine learning algorithms, focusing on clinical variables and pathogenic and potentially pathogenic genetic variants. Results The developed predictive model achieves high accuracy in identifying the presence of endometriosis, highlighting the importance of combining clinical and genetic data in diagnosis. The integration of this data into the DELFOS platform, a clinical decision support system, demonstrates the utility of machine learning in improving the diagnosis of endometriosis. Conclusions The findings underscore the potential of clinical and genetic factors in the early diagnosis of endometriosis using supervised machine learning algorithms. This study contributes to the classification of clinical variables that influence endometriosis, offering a valuable tool for clinicians in making therapeutic and management decisions for their female patients.
Elena Enamorado-Díaz mail , Leticia Morales-Trujillo mail , Julián-Alberto García-García mail , Ana Teresa Marcos Rodríguez mail anateresa.marcos@uneatlantico.es, José Manuel Navarro-Pando mail jose.navarro@uneatlantico.es, María-José Escalona-Cuaresma mail ,
Enamorado-Díaz
<a href="/17569/1/Food%20Frontiers%20-%202025%20-%20Romero%E2%80%90Marquez%20-%20Olive%20Leaf%20Extracts%20With%20High%20%20Medium%20%20or%20Low%20Bioactive%20Compounds%20Content.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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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.
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 class="ep_document_link" 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"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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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.
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
<a href="/17573/1/s41598-025-96332-9.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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Novel hybrid transfer neural network for wheat crop growth stages recognition using field images
Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases ‘Crown Root’, ‘Tillering’, ‘Mid Vegetative’, ‘Booting’, ‘Heading’, ‘Anthesis’, and ‘Milking’, comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions.
Aisha Naseer mail , Madiha Amjad mail , Ali Raza mail , Kashif Munir mail , Aseel Smerat mail , Henry Fabian Gongora mail henry.gongora@uneatlantico.es, Carlos Eduardo Uc Ríos mail carlos.uc@unini.edu.mx, Imran Ashraf mail ,
Naseer