Factores determinantes de la inasistencia de las embarazadas a consulta odontológica en el Subcentro de Salud Llacao, Cuenca - Ecuador.

<a href="/16734/1/nutrients-17-00577.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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Mediterranean Diet and Quality of Life in Adults: A Systematic Review

Background/Objectives: With the increasing life expectancy and, as a result, the aging of the global population, there has been a rise in the prevalence of chronic conditions, which can significantly impact individuals’ health-related quality of life, a multidimensional concept that comprises an individual’s physical, mental, and social wellbeing. While a balanced, nutrient-dense diet, such as Mediterranean diet, is widely recognized for its role in chronic disease prevention, particularly in reducing the risk of cardiovascular diseases and certain cancers, its potential benefits extend beyond these well-known effects, showing promise in improving physical and mental wellbeing, and promoting health-related quality of life. Methods: A systematic search of the scientific literature in electronic databases (Pubmed/Medline) was performed to identify potentially eligible studies reporting on the relation between adherence to the Mediterranean diet and health-related quality of life, published up to December 2024. Results: A total of 28 studies were included in this systematic review, comprising 13 studies conducted among the general population and 15 studies involving various types of patients. Overall, most studies showed a significant association between adherence to the Mediterranean diet and HRQoL, with the most significant results retrieved for physical domains of quality of life, suggesting that diet seems to play a relevant role in both the general population and people affected by chronic conditions with an inflammatory basis. Conclusions: Adherence to the Mediterranean diet provides significant benefits in preventing and managing various chronic diseases commonly associated with aging populations. Furthermore, it enhances the overall health and quality of life of aging individuals, ultimately supporting more effective and less invasive treatment approaches for chronic diseases.

Producción Científica

Justyna Godos mail , Monica Guglielmetti mail , Cinzia Ferraris mail , Evelyn Frias-Toral mail , Irma Dominguez Azpíroz mail irma.dominguez@unini.edu.mx, Vivian Lipari mail vivian.lipari@uneatlantico.es, Andrea Di Mauro mail , Fabrizio Furnari mail , Sabrina Castellano mail , Fabio Galvano mail , Licia Iacoviello mail , Marialaura Bonaccio mail , Giuseppe Grosso mail ,

Godos

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Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images

Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields. This study presents VG-GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two-step feature extraction process. The proposed model begins by leveraging the visual geometry group (VGG-16) network to extract initial pixel-based spatial features from the crop images. These features are then further refined using the Gaussian Naive Bayes (GNB) model and feature decomposition-based matrix factorization mechanism, which generates more informative features for classification purposes. This study incorporates machine learning models to ensure a comprehensive evaluation. By comparing VG-GNBNet's performance against these models, we validate its robustness and accuracy. Integrating deep learning and machine learning techniques allows VG-GNBNet to capitalize on the strengths of both approaches, leading to superior performance. Extensive experiments demonstrate that the proposed VG-GNBNet+GNB model significantly outperforms other models, achieving an impressive accuracy score of 99.85%. This high accuracy highlights the model's potential for practical application in the agricultural sector, where the precise detection of crop health is crucial for effective disease management and yield optimization.

Producción Científica

Muhammad Usama Tanveer mail , Kashif Munir mail , Ali Raza mail , Laith Abualigah mail , Helena Garay mail helena.garay@uneatlantico.es, Luis Eduardo Prado González mail uis.prado@uneatlantico.es, Imran Ashraf mail ,

Tanveer

<a href="/16270/1/s12880-024-01546-4.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 transfer learning based bone fracture detection using radiographic images

A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients. The detection of bone fractures is crucial, and radiographic images are often relied on for accurate assessment. An efficient neural network method is essential for the early detection and timely treatment of fractures. In this study, we propose a novel transfer learning-based approach called MobLG-Net for feature engineering purposes. Initially, the spatial features are extracted from bone X-ray images using a transfer model, MobileNet, and then input into a tree-based light gradient boosting machine (LGBM) model for the generation of class probability features. Several machine learning (ML) techniques are applied to the subsets of newly generated transfer features to compare the results. K-nearest neighbor (KNN), LGBM, logistic regression (LR), and random forest (RF) are implemented using the novel features with optimized hyperparameters. The LGBM and LR models trained on proposed MobLG-Net (MobileNet-LGBM) based features outperformed others, achieving an accuracy of 99% in predicting bone fractures. A cross-validation mechanism is used to evaluate the performance of each model. The proposed study can improve the detection of bone fractures using X-ray images.

Producción Científica

Aneeza Alam mail , Ahmad Sami Al-Shamayleh mail , Nisrean Thalji mail , Ali Raza mail , Edgar Aníbal Morales Barajas mail , Ernesto Bautista Thompson mail ernesto.bautista@unini.edu.mx, Isabel de la Torre Diez mail , Imran Ashraf mail ,

Alam

<a href="/16273/1/v16p0506.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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Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification

Faced with anomalies in medical images, Deep learning is facing major challenges in detecting, diagnosing, and classifying the various pathologies that can be treated via medical imaging. The main challenges encountered are mainly due to the imbalance and variability of the data, as well as its complexity. The detection and classification of skin diseases is one such challenge that researchers are trying to overcome, as these anomalies present great variability in terms of appearance, texture, color, and localization, which sometimes makes them difficult to identify accurately and quickly, particularly by doctors, or by the various Deep Learning techniques on offer. In this study, an innovative and robust hybrid architecture is unveiled, underscoring the symbiotic potential of wavelet decomposition in conjunction with EfficientNet models. This approach integrates wavelet transformations with an EfficientNet backbone and incorporates advanced data augmentation, loss function, and optimization strategies. The model tested on the publicly accessible HAM10000 and ISIC2017 datasets has achieved an accuracy rate of 94.7%, and 92.2% respectively.

Producción Científica

Amina Aboulmira mail , Hamid Hrimech mail , Mohamed Lachgar mail , Mohamed Hanine mail , Carlos Manuel Osorio García mail carlos.osorio@uneatlantico.es, Gerardo Méndez Mezquita mail , Imran Ashraf mail ,

Aboulmira

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Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults

Background: Nut consumption has been considered a potential protective factor against cognitive decline. The aim of this study was to test whether higher total and specific nut intake was associated with better cognitive status in a sample of older Italian adults. Methods: A cross-sectional analysis on 883 older adults (>50 y) was conducted. A 110-item food frequency questionnaire was used to collect information on the consumption of various types of nuts. The Short Portable Mental Status Questionnaire was used to assess cognitive status. Multivariate logistic regression analyses were performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between nut intake and cognitive status after adjusting for potential confounding factors. Results: The median intake of total nuts was 11.7 g/day and served as a cut-off to categorize low and high consumers (mean intake 4.3 g/day vs. 39.7 g/day, respectively). Higher total nut intake was significantly associated with a lower prevalence of impaired cognitive status among older individuals (OR = 0.35, CI 95%: 0.15, 0.84) after adjusting for potential confounding factors. Notably, this association remained significant after additional adjustment for adherence to the Mediterranean dietary pattern as an indicator of diet quality, (OR = 0.32, CI 95%: 0.13, 0.77). No significant associations were found between cognitive status and specific types of nuts. Conclusions: Habitual nut intake is associated with better cognitive status in older adults.

Producción Científica

Justyna Godos mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Evelyn Frias-Toral mail , Raynier Zambrano-Villacres mail , Angel Olider Rojas Vistorte mail angel.rojas@uneatlantico.es, Vanessa Yélamos Torres mail vanessa.yelamos@funiber.org, Maurizio Battino mail maurizio.battino@uneatlantico.es, Fabio Galvano mail , Sabrina Castellano mail , Giuseppe Grosso mail ,

Godos