Computed tomography (CT) scans involve exposure to ionizing radiation, which can present unavoidable risks. CT is one of the largest contributors to man-made radiation dose in medical populations. Despite the clinical benefits and the increase of CT imaging usage, the concern of arising exposure of ionizing radiation associated with CT imaging may pose significant health risks. This paper investigates the arising exposure of ionizing radiation from CT and associated radiation dose in the study area. The exposure of ionizing radiation from CT scans was assessed in four hospitals using active detection methods. CT scan operators were exposed to an annual effective dose (mean ± SD) ranged from 2.83 ± 0.4 mSv/y to 12 ± 2.4 mSv/y. The public was exposed to annual effective dose ranged from 0.40 ± 0.04 mSv/y to 0.87 ± 0.04 mSv/y. Patients were exposed to minimum effective dose of 1.03 mSv (head scan) and maximum effective dose of 24.45 mSv (abdomen pelvis). The results show the occupational annual effective dose and the public annual effective dose were within the ICRP recommended dose limits. Its first kind of study in this area and the data obtained emphasizes the importance of applying strategies to optimize the radiation dose from CT scans.

Medical ionizing radiation is widely used in hospitals, in particular dental clinics, and in medical research to facilitate the diagnosis and treatment of patients. The awareness, attitude, and perception of ionizing radiation exposure among dental undergraduate students and interns in radiological investigations and dental care clinics were investigated. A cross‐sectional study was conducted; 17 questions were designed online using the software “QuestionPro,” which was licensed to the University of Imam Abdulrahman Bin Faisal. Participants included senior medical dental students from Imam Abdulrahman Bin Faisal University in their third to fifth years, as well as interns from King Fahad University Hospital and private dental care clinics. A total of 855 participants viewed, 360 started the questionnaire, and 258 (72%) completed it online. Overall, knowledge was lacking; 32% of respondents incorrectly believed that magnetic resonance imaging and ultrasound used ionizing radiation, while 38% were unsure. Dental X‐rays were deemed harmful by 40% (n = 104) of respondents. According to 33% (n = 85) of participants, there is no radiation scatter during an X‐ray or CT scan, while 30% (n = 76) are unsure. Respondents (44%; n = 104) were unaware of the radiation dose from a chest radiograph and (45%; n = 116) overestimated the radiation dose. The effects of ionizing radiation on healthy tissue are known to more than half of the participants (54%). According to 39% of respondents, digital radiography exposes them to less radiation than traditional radiography. In terms of radiation protection and hazard, 46% said personal monitoring badges should be always worn and 58% (n = 150) said lead aprons should be used on a regular basis. 63% of the subjects had received radiation protection education, such as formal lectures, tutorials, or workshops, while 37% (n = 95) had not. 53% of the respondents were not aware of the international recommendations from the International Commission on Radiological Protection. When asked if they would follow radiation protection protocols if they opened a private dental clinical practice in the future, 50% (n = 129) said they would.

The calculation of effective doses is crucial in many medical and radiation fields in order to ensure safety and compliance with regulatory limits. Traditionally, Monte Carlo codes using detailed human body computational phantoms have been used for such calculations. Monte Carlo dose calculations can be time-consuming and require expertise in different processes when building the computational phantom and dose calculations. This study employs various machine learning (ML) algorithms to predict the organ doses and effective dose conversion coefficients (DCCs) from different anthropomorphic phantoms. A comprehensive data set comprising neutron energy bins, organ labels, masses, and densities is compiled from Monte Carlo studies, and it is used to train and evaluate the supervised ML models. This study includes a broad range of phantoms, including those from the International Commission on Radiation Protection (ICRP-110, ICRP-116 phantom), the Visible-Human Project (VIP-man phantom), and the Medical Internal Radiation Dose Committee (MIRD-Phantom), with row data prepared using numerical data and organ categorical labeled data. Extreme gradient boosting (XGB), gradient boosting (GB), and the random forest-based Extra Trees regressor are employed to assess the performance of the ML models against published ICRP neutron DCC values using the mean square error, mean absolute error, and R² metrics. The results demonstrate that the ML predictions significantly vary in lower energy ranges and vary less in higher neutron energy ranges while showing good agreement with ICRP values at mid-range energies. Moreover, the categorical data models align closely with the reference doses, suggesting the potential of ML in predicting effective doses for custom phantoms based on regional populations, such as the Saudi voxel-based model. This study paves the way for efficient dose prediction using ML, particularly in scenarios requiring rapid results without extensive computational resources or expertise. The findings also indicate potential improvements in data representation and the inclusion of larger data sets to refine model accuracy and prevent overfitting. Thus, ML methods can serve as valuable techniques for the continued development of personalized dosimetry.

Severe eosinophilic asthma (SEA) is characterized by persistent airway inflammation and frequent exacerbations despite standard treatments. Mepolizumab, a monoclonal antibody that reduces eosinophil levels by targeting interleukin-5, has emerged as an add-on therapy for patients with SEA. This systematic review evaluated mepolizumab's efficacy and safety for treating SEA. A comprehensive literature search was conducted across major databases. Thirty-two studies with over 6,000 patients were included, comprising randomized controlled trials, open-label extensions, and real-world observational analyses. Study quality and risk of bias were assessed using standard tools. Meta-analysis was deemed inappropriate due to heterogeneity. Instead, a narrative synthesis was performed. Mepolizumab significantly reduced exacerbation rates by around 50% and improved symptoms and lung function compared to placebo in pivotal trials. Long-term open-label studies showed sustained reductions in exacerbations and stable lung function for up to 4.5 years. Real-world data demonstrated consistent 50%-90% exacerbation decreases across diverse patient populations over 6-24 months. Mepolizumab exhibited an acceptable safety profile, with mild injection site reactions and headaches as most common adverse events. While specific subgroups may show enhanced responses, mepolizumab displayed broad efficacy regardless of patient demographics or phenotypes. The extensive evidence provides robust support for mepolizumab as an efficacious and safe add-on treatment option for patients with severe, refractory eosinophilic asthma. Further high-quality comparative effectiveness research is warranted to optimize patient selection and positioning among emerging biologics.

This paper addresses Radhakrishnan–Kundu–Lakshmanan equation that arises in the study of soliton dynamics in optical fibers. The bifurcation analysis is carried out and the phase portraits are displayed. The complete discriminant analysis also leads to solitons and other solutions to the model.