RESUMO
Background: Research advancing effective treatments for breast cancer is crucial for eradicating the disease, reducing recurrence, and improving survival rates. Nipple-sparing mastectomy (NSM), a common method for treating breast cancer, often leads to complications requiring re-operation. Despite advancements, the use of hyperbaric oxygen therapy (HBOT) for treating these complications remains underexplored. Therefore, we analyze the efficacy of HBOT in the post-operative care of patients undergoing NSM. Methods: A systematic search was conducted using PubMed, Scopus, and the Cochrane Library. Studies were assessed for eligibility using the PICO (Population, Intervention, Comparison, Outcome) framework and classified based on American Society of Plastic Surgeons (ASPS) levels of evidence. Seven studies, totaling a pool of 63 female patients, met the inclusion criteria. Among these studies, four were categorized as Level III (57.1%), one as Level IV (14.3%), and two as Level V (28.6%). These studies focused on HBOT's role in wound healing, the successful salvage of breast reconstruction, and the optimal timing for HBOT. Results: This review revealed that HBOT indeed has potential for improving tissue oxygenation, vascularization, and, consequently, wound healing. It is noted that HBOT is efficacious for mitigating post-NMS complications, including infections, re-operation, flap loss, seroma, and hematoma. Conclusions: Overall, HBOT could be beneficial in standard post-surgical care protocols for patients undergoing NSM due to its role in mitigating common adverse effects that occur after mastectomy. Despite promising outcomes, the recent literature lacks rigorous clinical trials and well-defined control groups, underscoring the need for further research to establish standardized HBOT protocols.
RESUMO
The current outbreak of monkeypox (mpox) has become a major public health concern because of the quick spread of this disease across multiple countries. Early detection and diagnosis of mpox is crucial for effective treatment and management. Considering this, the purpose of this research was to detect and validate the best performing model for detecting mpox using deep learning approaches and classification models. To achieve this goal, we evaluated the performance of five common pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, and EfficientNetB3) and compared their accuracy levels when detecting mpox. The performance of the models was assessed with metrics (i.e., the accuracy, recall, precision, and F1-score). Our experimental results demonstrate that the MobileNetV2 model had the best classification performance with an accuracy level of 98.16%, a recall of 0.96, a precision of 0.99, and an F1-score of 0.98. Additionally, validation of the model with different datasets showed that the highest accuracy of 0.94% was achieved using the MobileNetV2 model. Our findings indicate that the MobileNetV2 method outperforms previous models described in the literature in mpox image classification. These results are promising, as they show that machine learning techniques could be used for the early detection of mpox. Our algorithm was able to achieve a high level of accuracy in classifying mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.
