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Post-marketing surveillance of drugs is a cornerstone of pharmacovigilance. This study was conducted to characterize patterns of adverse drug reactions (ADRs) reported in Jordan. ADR reports submitted to the pharmacovigilance database of the Jordan Food and Drug Administration during 2015–2021 were retrospectively analyzed. The most commonly reported drugs, drug classes, ADRs, and ADRs consequences were explored. Logistic regression identified possible predictors of reporting serious ADRs. A total of 2744 ADR reports were included, among which 28.4% were classified as serious. An annual increase in ADR reporting was observed. The most commonly implicated drug classes were antineoplastic and immunomodulating agents (24.0%), anti-infectives for systemic use (14.2%), and alimentary tract and metabolism (12.1%). Covid−19 vaccination was the most reported drug (22.8%). Fatigue (6.3%), injection site pain (6.1%), and headache (6.0%) were the top three common ADRs. Among ADRs with outcome information, 4.7% were fatal. Patient’s age and intravenous medication use largely predicted reporting serious ADRs. This study provides contemporary insights into the post-marketing surveillance of drugs in Jordan. The findings are foundational for future studies exploring drug-ADRs causality relationships. Efforts that promote pharmacovigilance concepts should be sustained and enhanced at the national level.

BACKGROUND Achieving widespread coronavirus disease 2019 (COVID-19) vaccination is crucial in controlling the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This cross-sectional study aimed to identify factors associated with the willingness of dental medicine students to receive the COVID-19 vaccine. OBJECTIVES The study sought to assess the knowledge, attitudes, and behaviors of undergraduate dental students toward COVID-19 vaccines and to identify determinants, motivators, and barriers to vaccine uptake and booster receipt. METHODS A web-based survey was distributed to all 882 undergraduate dental surgery students in January 2022, and 70.7% of the students responded. The survey used χ2 tests and logistic regression analysis to examine the association among the variables. The significance level was set at α = 0.05. RESULTS Most participants (72.4%) reported having adequate knowledge of COVID-19. The vaccine acceptance rate was higher among male and older trainees, with no significant difference compared to women and younger trainees with no significant difference (p = 0.849). Acceptance of the vaccine varied according to study level (5-year program), ranging from 44.8% to 73.0%, in the following order 4th > 1st > 3rd > 5th > 2nd year. Social media (76.8%), government websites (66.5%), and family and friends (57.2%) were the main sources of COVID-19-related information. Among hesitant and unwilling participants, the main concerns were side effects (34.0%) and lack of understanding about the vaccine's mechanism (67.3%). CONCLUSIONS Ajman dental students had moderate knowledge of COVID-19 and obtained information mainly from social media, government websites, and family and friends. Age, sex, and study year influenced vaccine acceptance. The main reasons for refusal were lack of knowledge, fear of side effects, and complications. Education campaigns are needed to increase vaccine acceptance among dental students.

BackgroundDysregulated systemic inflammation is the primary driver of mortality in severe coronavirus disease 2019 (COVID-19) pneumonia. Current guidelines favour a 7–10-day course of any glucocorticoid equivalent to dexamethasone 6 mg daily. A comparative randomised controlled trial (RCT) with a higher dose and a longer duration of intervention was lacking.MethodsWe conducted a multicentre, open-label RCT to investigate methylprednisolone 80 mg as a continuous daily infusion for 8 days followed by slow taperingversusdexamethasone 6 mg once daily for up to 10 days in adult patients with COVID-19 pneumonia requiring oxygen or noninvasive respiratory support. The primary outcome was reduction in 28-day mortality. Secondary outcomes were mechanical ventilation-free days at 28 days, need for intensive care unit (ICU) referral, length of hospitalisation, need for tracheostomy, and changes in C-reactive protein (CRP) levels, arterial oxygen tension/inspiratory oxygen fraction (PaO2/FIO2) ratio and World Health Organization Clinical Progression Scale at days 3, 7 and 14.Results677 randomised patients were included. Findings are reported as methylprednisolone (n=337)versusdexamethasone (n=340). By day 28, there were no significant differences in mortality (35 (10.4%)versus41 (12.1%); p=0.49) nor in median mechanical ventilation-free days (median (interquartile range (IQR)) 23 (14)versus24 (16) days; p=0.49). ICU referral was necessary in 41 (12.2%)versus45 (13.2%) (p=0.68) and tracheostomy in 8 (2.4%)versus9 (2.6%) (p=0.82). Survivors in the methylprednisolone group required a longer median (IQR) hospitalisation (15 (11)versus14 (11) days; p=0.005) and experienced an improvement in CRP levels, but not inPaO2/FIO2ratio, at days 7 and 14. There were no differences in disease progression at the prespecified time-points.ConclusionProlonged, higher dose methylprednisolone did not reduce mortality at 28 days compared with conventional dexamethasone in COVID-19 pneumonia.

Abdulrahman Al Shudifat,1 Hala Al Suqi,2 Kutada Soub,1 Leen Al Nemrawi,2 Moa’tasem Abu Jaber,2 Mohammad Al Barbarawi,3 Nour Shewaikani,2 Yazan El Adwan,4 Assem Al Refaei2 1Department of Neurosurgery, University of Jordan, Amman, Jordan; 2School of Medicine, University of Jordan, Amman, Jordan; 3Department of Neurosurgery, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan; 4Jordan Hospital, Amman, JordanCorrespondence: Assem Al Refaei Jubaiha, Amman, 11942, JordanTel +962796553288Email aas0170234@ju.edu.joPurpose: Our current study investigates the relationship between ABO blood groups and brain tumor incidence in the Jordanian pediatric population in a case-controlled manner.Patients and Methods: This case-control study targeted pediatric primary brain tumor patients and tumor-free controls. Cases included patients younger than 18 when given a histologically confirmed diagnosis with a primary brain tumor, ascertained from two tertiary hospitals in Jordan. Controls were age- and gender-matched to cases and acquired from JUH pediatric clinics, with an exclusion for all patients with a personal history of tumors. Through using available records and calling guardians, our team obtained patients’ and controls’ blood groups.Results: Our case control included 81 (35.4%) pediatric primary brain tumor patients and age- and gender-matched tumor-free controls 148 (64.6%). When compared to O blood group, patients with A and B blood groups were not at higher risk of developing pediatric primary brain tumors (P=0.742, P=1.000, respectively). However, Chi-square analysis revealed a 2.79-fold higher risk for pediatric primary brain tumors in AB blood group patients (P=0.024). Gender-specific analysis revealed a 3.42-fold higher risk for pediatric brain tumors in AB blood group males when compared to O blood group males.Conclusion: This work represents the first published study on the association between blood groups and pediatric brain tumors. With future research with larger samples and control of confounding factors, AB blood group may become a more established risk factor for pediatric brain tumors, aiding in screening.Keywords: pediatrics, children, brain tumors, ABO blood groups

Several studies discussed the predictive modeling of deep learning in different applications such as classifying tissue features from microstructural data, Crude Oil Prices, mechanical constitutive behavior of materials, microbiome data, and mineral prospectively. Commercial navigation includes a wealth of trip-related data, including distance, expected journey time, and tolls that may be encountered along the way. Using a classification algorithm, it is possible to extract drop-off and pickup locations from taxi trip data and estimate if the tour would incur tolls. In this work, let’s use the classification learner to create classification models, compare their performance, and export the findings for additional study. The workflow for the classification learner is the same as for the regression learner. The purpose is to make predictions based on fresh data in order to see how well the model performs with new data. To train the model, it’s critical to separate the data set. The combined training and validation data is next pre-processed, which involves tasks such as cleaning and developing new features skills. Once the data has been prepared, it’s time to begin the supervised machine learning process and test a number of ways to identify the best model, such as the type of model that should be used, the important features, and the best parameters of the model to find the best fit for the considered data. The results of analyzing different predictive multiclass classification models with taxi trip tolls show that it is possible to use a machine learning-based model when we like to avoid road tolls depending on historical data on taxi trip tolls. The outcome of this study can help to expect road tolls from the drop-off and pickup locations of a taxi data, У кількох дослідженнях обговорювалося прогностичне моделювання глибокого навчання у різних додатках, таких як класифікація характеристик тканин на основі мікроструктурних даних, ціни на сиру нафту, механічну конститутивну поведінку матеріалів, дані мікробіомів та перспективи мінералів. Комерційна навігація включає безліч даних, пов’язаних з поїздкою, включаючи відстань, очікуваний час у дорозі і дорожні збори, які можуть виникнути в дорозі. Використовуючи алгоритм класифікації, можна отримати місця висадки та посадки з даних про поїздки на таксі та оцінити, чи будуть дорожні збори за поїздку. У цій роботі давайте використовуємо засіб навчання класифікації для створення моделей класифікації, порівняння їхньої продуктивності та експорту результатів для додаткового вивчення. Робочий процес для учня класифікації такий самий, як і для учня регресії. Ціль полягає в тому, щоб робити прогнози на основі актуальних даних, щоб побачити, наскільки добре модель працює з новими даними. Для навчання моделі важливо розділити набір даних. Потім об’єднані дані навчання та перевірки проходять попередню обробку, яка включає такі завдання, як очищення та розвиток навичок роботи з новими функціями. Після того, як дані підготовлені, настає час розпочати контрольований процес машинного навчання та протестувати кілька способів визначення кращої моделі, наприклад, тип моделі, яку слід використовувати, важливі функції та кращі параметри моделі, знайти найкращу відповідність для даних, що розглядаються. Результати аналізу різних моделей прогнозної мультикласової класифікації з урахуванням плати за проїзд у таксі показують, що можна використовувати модель, що базується на машинному навчанні, коли ми хочемо уникнути дорожніх зборів залежно від історії даних про плату за поїздки в таксі. Результати цього дослідження можуть допомогти розрахувати дорожні збори у місцях висадки та посадки таксі

All individuals have a fundamental human right to nutrition. However, it is well known that undernourishment is a common and major health issue among elderly across the globe. Nutritional documentation helps to guarantee correct nutritional care and therapy. Research on medical and nursing home staff's documentation of nutritional care in hospitals and between medical facilities and nursing homes is scarce. The evidence that is presently offered points to unsatisfactory documenting practices. The nursing workforce lacks the necessary skills to appropriately account for the relationship between patients' medical conditions and their nutritional status, as well as the substantial risk of heightened patient suffering brought on by undernutrition. Unsystematic and unorganized recording processes may contribute to the lack of nutritional information document management and sharing. Another challenge is brought on by the inadequate proper nutrition information transfer when elderly patients are transferring between hospitals and nursing facilities. Clarifying roles in nutrition therapy and treatment is part of interprofessional responsibility. The haziness and ambiguity around accountability may be one factor contributing to the lack of attention given to nutritional documentation. Reassessment of the prevailing systems and development an alternate solution way of tracking food consumption among medical and nursing home residents given the present documentation issues, elevated staff turnover rate, and the inability of training and managing interventions to generate long-term behavioral modifications among nursing home staff may aid in reducing the level of undernutrition and lack of nutrition-related documentation among geriatric individuals.

In the advent of ultra-dense networks with unprecedented complex and heterogeneous infrastructure, the role of automation in network optimization becomes vital for sustaining the target performance. In this work, we address the challenge of identifying and classifying sub-par performing nodes in near-real time through a machine-learning inspection of streaming performance indicators from multiple probe points. We present a novel K-means-based solution for classifying node performance over a sliding time segment and further categorizing the type of failure. The K-means solution first identifies the performance instances of interest. These are then inspected in a second clustering round for automated performance labeling. Next, the labeled data-set is employed to train a Support Vector Machine based classifier that is continuously classifying incoming performance instances from the network. The method is tested using a real network data set comprising call detail records. The results advocate the potential of our method for effectively and accurately identifying and classifying performance degradation in any node in the network.

Background Magnetic resonance imaging (MRI) is potentially considered as the greatest invention in the world. In addition, all the people in the medical field have been surprised by the capability of MRI in the past 25 years. There has been a massive increase in the use of MRI in the clinical field. More than 80 million MRI procedures occur every year around the globe. Patients who are undergoing MRI examinations infrequently experience anxiety as a consequence of the procedure or the environment. Reducing patient anxiety is considered as one of the most common concerns affecting the outcome of the MRI scan. The purpose of this study was to investigate the effectiveness of procedure explanation on controlling anxiety levels in adolescent patients undergoing MRI. Methods The Institutional Review Board (IRB) ethics committee approved this cross-sectional study. Convenient sampling techniques were used to select 37 adolescent patients (19 females and 18 male) who attended the MRI appointment unit at the Medical Imaging Department of King Abdulaziz Medical City, Riyadh, Saudi Arabia. Three tools were used to collect data: the sociodemographic data sheet, patient assessment knowledge sheet, and the State-Trait Anxiety Inventory (STAI) questionnaire. STAI is a self-report test designed to measure patient anxiety level; 40 questions are divided into two subcategories; 20 state (defined as fear, nervousness, discomfort, etc, and how the person feels right now), and 20 traits (defined as stress, worry, discomfort, etc, that the person experiences on a daily basis). Results The state anxiety level showed a statistically significant difference in patients’ knowledge between the pre-test and post-test with or without instruction. For the group with instruction, their anxiety level significantly reduced. The trait anxiety level showed no significant difference between pre-test and post-test with or without instruction. Conclusion Because of lack of awareness about the procedural instructions for the patient before undergoing MRI, it may affect the procedure prognosis and outcome. Our suggestion for the future is to increase awareness about MRI and to improve communication skills of MRI staff to educate the patient in a good way to reduce patient anxiety.