This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

Background: The Pfizer-BioNTech (BNT162b2) and the Oxford-AstraZeneca (ChAdOx1 nCoV-19) COVID-19 vaccines have shown promising safety and acceptability. However, COVID-19 vaccine side effects play an essential role in public vaccine confidence. We aimed to study the side effects of these COVID-19 vaccines. Methods: A randomized, cross-sectional descriptive study was conducted between March and May of 2021. In total, 330 participants among the King Khalid University community in the Aseer region of the Kingdom of Saudi Arabia reported their side effects following the COVID-19 vaccine. A questionnaire was designed and validated to collect the participants’ demographic data and COVID-19-related symptoms after COVID-19 vaccine injection. Results: Symptoms associated with COVID-19 were reported by 226 participants (68.5%). The most common side effects reported by the participants were fever (n = 136, 41.2%), fatigue (n = 119, 36.1%), headache (n = 86, 24.2%), malaise (n = 121, 36.7%), myalgia (n = 121, 36.7%), and muscle and joint pain (n = 76, 23%). Of the participants, 5.1% became infected with COVID-19 after vaccination. Symptoms were significantly more common in males than in females (p = 0.006). Conclusion: The incidence of COVID-19 vaccination side effects in the Aseer region, Kingdom of Saudi Arabia, was consistent with the manufacturers’ data. The most common post-vaccination symptoms reported by the participants were fever, myalgia, malaise, fatigue, muscle and joint pain, and headache. The results of this study showed significant variation in adverse events between Pfizer-BioNTech and Oxford-AstraZeneca COVID-19 vaccines. Healthcare providers and recipients of vaccines can be more confident about the safety of Pfizer-BioNTech and Oxford-AstraZeneca COVID-19 vaccines.

Considering the large spectrum of side effects caused by synthetic drugs and the development of natural alternatives utilizing Algerian flora, this study aimed to place a spotlight on the chemical profile and antioxidant and anti-inflammatory activities of Myrtus communis L. essential oils (MCEOs). In this study, essential oils (EOs) were collected via hydro-distillation of the plant’s leaves, and a chemical constituent analysis was performed using gas chromatography–mass spectrophotometry (GC–MS). The in vitro antioxidant activity was evaluated using DPPH, ABTS, and hydroxyl radical scavenging tests. The in vitro anti-inflammatory capacity was estimated by studying the antidenaturation effect using bovine serum albumin (BSA) as a protein model. The in vivo anti-inflammatory activity was carried out by utilizing the classical model of carrageenan-induced paw edema in rats, using diclofenac (DCF) as the reference drug. Moreover, the molecular interaction of the compounds obtained from the GC–MS analysis was studied within the binding site of cyclooxygenase-2 (COX-2) using an in silico approach as the confirmatory tool of the in vitro and in vivo experiments. The GC–MS analysis revealed that MCEOs were mainly composed of oxygenated monoterpenes (70.56%), oxygenated sesquiterpenes (3.1%), sesquiterpenes (4.17%), and monoterpenes (8.75%). Furthermore, 1,8-cineole was the major compound (19.05%), followed by cis-geranyl acetate (11.74%), methyl eugenol (5.58%), α-terpineol (4.62%), and β-myrcene (4.40%). MCEOs exhibited remarkable concentration-dependent free radical scavenging activity, with an IC₅₀ of 15.317 ± 0.340 µg/mL, 18.890 ± 2.190 µg/mL, and 31.877 ± 0.742 µg/mL for DPPH, ABTS, and hydroxyl radical, respectively. The significant in vitro anti-inflammatory activity due to the inhibition of BSA denaturation was proportional to the EO concentration, where the highest value was recorded at 100 μg/mL with an approximately 63.35% percentage inhibition and an IC₅₀ of 60.351 ± 5.832 μg/mL. MCEOs showed a good in vivo anti-inflammatory effect by limiting the development of carrageenan-induced paw thickness. The in silico study indicated that, among the 60 compounds identified by the GC–MS analysis, 9 compounds were observed to have a high binding energy to cyclooxygenase-2 as compared to diclofenac. Our study revealed that EOs from Algerian Myrtus communis L. can be considered to be a promising candidate for alleviating many debilitating health problems and may provide new insights in the fields of drug design, agriculture, and the food industry.