The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations.

Background: Human emotions are subjective psychophysiological processes that play an important role in the daily interactions of human life. Emotions often do not manifest themselves in isolation; people can experience a mixture of them and may not express them in a visible or perceptible way; Methods: This study seeks to uncover EEG patterns linked to emotions, as well as to examine brain activity across emotional states and optimise machine learning techniques for accurate emotion classification. For these purposes, the DEAP dataset was used to comprehensively analyse electroencephalogram (EEG) data and understand how emotional patterns can be observed. Machine learning algorithms, such as SVM, MLP, and RF, were implemented to predict valence and arousal classifications for different combinations of frequency bands and brain regions; Results: The analysis reaffirms the value of EEG as a tool for objective emotion detection, demonstrating its potential in both clinical and technological contexts. By highlighting the benefits of using fewer electrodes, this study emphasises the feasibility of creating more accessible and user-friendly emotion recognition systems; Conclusions: Further improvements in feature extraction and model generalisation are necessary for clinical applications. This study highlights not only the potential of emotion classification to develop biomedical applications, but also to enhance human–machine interaction systems.

Oral mucositis is an inflammatory and ulcerative disease that affects patients undergoing cancer treatment, namely chemotherapy and radiotherapy, significantly compromising their quality of life. Despite its high incidence and clinical impact, current therapeutic options remain limited, reinforcing the need for new treatment approaches. In this context, the Instituto Português de Oncologia do Porto FG, EPE (IPO-Porto) developed an innovative mouthwash containing dexamethasone sodium phosphate (DSP), ascorbic acid, and sodium hyaluronate. In this work the physicochemical stability of this formulation was evaluated to ensure its quality and safety as compounded medicine. Exploratory studies were performed under accelerated aging conditions (40 ± 2 °C), followed by stability studies under real-time conditions (20 ± 2 °C) and refrigeration conditions (4 ± 2 °C), as well as in-use stability studies simulating daily patient handling. At each sampling time, physicochemical characterization was carried out (pH, color, odor, and precipitate formation) as well as the DSP quantification using high-performance liquid chromatography. The acceptable range of 90.0–110.0%, compared to the initial concentration of DSP, was used as the primary stability criterion. Results showed that the formulation remained stable at high temperatures (40 °C), on short exposures times (≤6 h), and for 30 days at 4 °C in closed amber glass bottles. In-use stability studies performed during the 7 days after opening (considering three daily applications) showed that the DSP concentration remained above 90%. The results obtained enabled us to establish a definition of storage conditions and beyond-use recommendations, since they showed that the prepared formulation is stable when stored under refrigeration. Further studies are currently being developed with a view to its clinical use as a compounded mouthwash in a hospital setting for the treatment of oral mucositis.