Hydatid cyst is a common name for the larval stage of a tapeworm species of Echinococcus granulosus, which is transmitted from animals to humans via the fecal–oral route. Hydatid cysts predominantly affect the liver (75%), followed by the lung (15%), and they can affect many organs in the human body. Medical imaging modalities are the keystone for the diagnosis of hydatid cysts with high sensitivity and specificity. Ultrasound imaging with high resolution is the first choice for diagnosis, differential diagnosis, staging, establishing a role in interventional management, and follow-up, and it can differentiate Type I hydatid cysts from simple liver cysts. Unenhanced computed tomography (CT) is indicated where or when an ultrasound is unsatisfactory, such as with chest or brain hydatid cysts, when detecting calcification, and in obese patients. Magnetic resonance imaging (MRI) is superior for demonstrating cyst wall defects, biliary communication, neural involvement, and differentiating hydatid cysts from simple cysts using diffusion-weighted imaging (DWI) sequences. According to the phase of growth, hydatid cysts occur in different sizes and shapes, which may mimic benign or malignant neoplasms and may create diagnostic challenges in some cases. Hydatid cysts can mimic simple cysts, choledochal cysts, Caroli’s disease, or mesenchymal hamartomas of the liver. They can mimic lung cystic lesions, mycetoma, blood clots, Rasmussen aneurysms, and even lung carcinomas. Differential diagnosis can be difficult for arachnoid cysts, porencephalic cysts, pyogenic abscesses, and even cystic tumors of the brain, and can create diagnostic dilemmas in the musculoskeletal system.

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.

Background/Objectives: The current literature reveals a gap in understanding how rehabilitation professionals, such as physical and occupational therapists, perceive and prepare to implement artificial intelligence (AI) in their practices. Therefore, we conducted a cross-sectional observational study to assess the perceptions, knowledge, and willingness of rehabilitation healthcare providers to implement AI in practice. Methods: This study was conducted in Saudi Arabia, with data collected from 430 physical therapy professionals via an online SurveyMonkey questionnaire between January and March 2024. The survey assessed demographics, AI knowledge and skills, and perceived challenges. Data were analyzed using Statistical Package for the Social Science (SPSS 27) and DATAtab (version 2025), with frequencies, percentages, and nonparametric tests used to examine the relationships between the variables. Results: The majority of respondents (80.9%) believed that AI would be integrated into physical therapy in future, with 78.6% seeing AI as significantly impacting their work. While 61.4% thought that AI would reduce workload and enhance productivity, only 30% expressed concerns about AI endangering their profession. A lack of formal AI training has commonly been reported, with social media platforms being respondents’ primary source of AI knowledge. Despite these challenges, 85.1% expressed an eagerness to learn and use AI. Organizational preparedness was a significant barrier, with 45.6% of respondents reporting that their organizations lacked AI strategies. There were insignificant differences in the mean rank of AI perceptions or knowledge based on the gender, years of experience, and qualification degree of the respondents. Conclusions: The results demonstrated a strong interest in AI implementation in physical therapy. The majority of respondents expressed confidence in AI’s future utility and readiness to incorporate it into their practice. However, challenges, such as a lack of formal training and organizational preparedness, were identified. Overall, the findings highlight AI’s potential to revolutionize physical therapy while underscoring the necessity to address training and readiness to fully realize this potential.

Background: There is a paucity of information about the clinical features and angiographic findings in young patients with acute myocardial infarction (MI), especially in the Arab Peninsula countries. Objective: The aim of this study was to assess the proposed risk factors, clinical presentation, and angiographic findings of acute myocardial infarction in young adults. Methods: This prospective study included young (range, 18 to 45 years) patients who presented with acute MI based on clinical evaluation, laboratory investigation, and electrocardiogram, and they underwent a coronary angiography procedure. Key findings: Data of 109 patients with a diagnosis of acute MI were collected. Patients’ mean age was 39.98 ± 7.52 years (range, 31 to 45 years), and 92.7% (101) were male. Smoking was the highest risk factor in 67% of patients, obesity and overweight in 66%, sedentary lifestyle in 64%, dyslipidaemia in 33%, and hypertension in 28%. Smoking was the most common risk factor for acute MI in males (p = 0.009), whereas sedentary lifestyle was the most common risk factor in females (p = 0.028). Chest pain typical of acute MI was the most common presenting symptom in 96% of patients (p < 0.001). On admission, 96% of patients were conscious, and 95% were oriented. On angiography, the left anterior descending artery (LAD) was affected in 57%, the right coronary artery (RCA) was affected in 42%, and the left circumflex artery (LCX) was affected in 32% of patients. The LAD was severely affected in 44%, the RCA was severely affected in 25.7%, and the LCX was severely affected in 19.26% of patients (p < 0.001). Conclusion: Smoking, obesity, sedentary lifestyle, dyslipidaemia, and hypertension were the most common risk factors for acute MI. Smoking was the most common risk factor in males and sedentary lifestyle in females. The LAD was the most commonly affected coronary artery, followed by the RCA and LCX arteries, with the same order for severity of stenosis.

Khat is an evergreen plant and its fresh green leaves and buds are chewed for several hours a day for its psychostimulant response. This study aimed to review the effects of khat chewing on the body. PubMed was searched for literature on the different aspects of khat chewing to summarize its effects on different body systems. The major effects of khat chewing are those on the cardiovascular system including increased blood pressure, increased heart rate, and increased risk of myocardial infarction. It causes insomnia, stress, depression, hallucination, and increased risk of brain stroke. It causes dental caries, bad oral hygiene, periodontitis, increased oral mucosal ulcers, and increased gingival bleeding and recession. Khat chewing causes loss of appetite, gastritis, constipation, and hemorrhoids, and increased risk of hepatotoxicity and liver cirrhosis. Ultimately, it causes weak micturition, decreased sperm motility and count, and low birth-weight offspring in khat chewing mothers. The published articles about khat chewing in journals indexed in the PubMed was reviewed. Inclusion criteria involved each article available with English language and have a reported new effect of khat chewing.

Background: Artificial Intelligence (AI) is becoming integral to the health sector, particularly radiology, because it enhances diagnostic accuracy and optimizes patient care. This study aims to assess the awareness and acceptance of AI among radiology professionals in Saudi Arabia, identifying the educational and training needs to bridge knowledge gaps and enhance AI-related competencies. Methods: This cross-sectional observational study surveyed radiology professionals across various hospitals in Saudi Arabia. Participants were recruited through multiple channels, including direct invitations, emails, social media, and professional societies. The survey comprised four sections: demographic details, perceptions of AI, knowledge about AI, and willingness to adopt AI in clinical practice. Results: Out of 374 radiology professionals surveyed, 45.2% acknowledged AI’s significant impact on their field. Approximately 44% showed enthusiasm for AI adoption. However, 58.6% reported limited AI knowledge and inadequate training, with 43.6% identifying skill development and the complexity of AI educational programs as major barriers to implementation. Conclusion: While radiology professionals in Saudi Arabia are generally positive about integrating AI into clinical practice, significant gaps in knowledge and training need to be addressed. Tailored educational programs are essential to fully leverage AI’s potential in improving medical imaging practices and patient care outcomes.