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Background/Objectives: This study explores the application of vision transformers to predict early responses to stereotactic radiosurgery in patients with brain metastases using minimally pre-processed magnetic resonance imaging scans. The objective is to assess the potential of vision transformers as a predictive tool for clinical decision-making, particularly in the context of imbalanced datasets. Methods: We analyzed magnetic resonance imaging scans from 19 brain metastases patients, focusing on axial fluid-attenuated inversion recovery and high-resolution contrast-enhanced T1-weighted sequences. Patients were categorized into responders (complete or partial response) and non-responders (stable or progressive disease). Results: Despite the imbalanced nature of the dataset, our results demonstrate that vision transformers can predict early treatment responses with an overall accuracy of 99%. The model exhibited high precision (99% for progression and 100% for regression) and recall (99% for progression and 100% for regression). The use of the attention mechanism in the vision transformers allowed the model to focus on relevant features in the magnetic resonance imaging images, ensuring an unbiased performance even with the imbalanced data. Confusion matrix analysis further confirmed the model’s reliability, with minimal misclassifications. Additionally, the model achieved a perfect area under the receiver operator characteristic curve (AUC = 1.00), effectively distinguishing between responders and non-responders. Conclusions: These findings highlight the potential of vision transformers, aided by the attention mechanism, as a non-invasive, predictive tool for early response assessment in clinical oncology. The vision transformer (ViT) model employed in this study processes MRIs as sequences of patches, enabling the capture of localized tumor features critical for early response prediction. By leveraging patch-based feature learning, this approach enhances robustness, interpretability, and clinical applicability, addressing key challenges in tumor progression prediction following stereotactic radiosurgery (SRS). The model’s robust performance, despite the dataset imbalance, underscores its ability to provide unbiased predictions. This approach could significantly enhance clinical decision-making and support personalized treatment strategies for brain metastases. Future research should validate these findings in larger, more diverse cohorts and explore the integration of additional data types to further optimize the model’s clinical utility.

Healthcare workers are on the front lines of COVID-19 pandemic. Nurses and medical doctors have been the most exposed population. From all specialties involved in dealing with SARS-CoV2 disease, some of them seem to be more exposed than the others. From all specialties, infectious diseases, anesthesiology, emergency medicine, dentists and ophthalmology doctors, were at greatest risk of contracting COVID-19. Dentists have lived with the fear of self-contamination and transmission of the virus, as well as taking the risk of neglecting emergencies and chronic pathologies, or prosthetic work postponed, due to the pandemic, which drastically reduced the number of interventions. The SARS-Cov2 pandemic generated a new problem within the first three months of 2021, apart from the one created by the disease itself: allergic reactions, even fatal, that occured after the COVID-19 vaccination. Could dental practices add, under these circumstances, a supplemental risk for the newly vaccinated COVID-19, this is the main question we need to answer.

Background and Objectives: Different types of food introduced gradually in the diet will expose children to different food allergens, increasing the chance of developing allergic diseases. The aim of our study was to determine if allergen-specific IgE values can influence, depending on the diet, the prediction of remission of urticaria in children. Materials and Methods: This prospective study was conducted in 132 patients diagnosed over two years with urticaria, admitted to “Sf. Maria” Clinical Pediatric Hospital Iaşi. Total IgE assay was performed by ELISA, and determination of specific serum IgE by the CLA System Quanti Scan method (Innogenetics, Heiden, Germany). Data were gathered and statistical analysis was performed using statistical software SPSS, using descriptive and inferential statistics. Results: The determination of specific IgE to food allergens was performed on a total of 132 cases. The values of specific IgE were positive for one or more food allergens in 84 patients (63.64%). The most common allergens involved were: cow’s milk in 33.3% cases, egg white in 22.6% cases, and hazelnuts in 11.9% cases. The specific IgE values for the different types of food included in our study had a predictive value for disease remission. Conclusions: The determination of specific IgE confirms the presence of a particular food allergen and may have predictive value for the future development of an allergic manifestation.