One of the most common health concerns disturbing people within working years globally is diabetes mellitus (DM). One well-known consequence of DM is vascular damage, which can manifest as macro- and microangiopathy affecting the ocular retina. Therefore, Diabetic macular edema (DME) is a major sight-threatening complication of diabetic retinopathy (DR) worldwide. It is the most prevalent cause of significant vision impairment in diabetic patients. Long-term vision loss can be avoided by following early DME treatment guidelines in everyday life. Hence, there are various therapeutic approaches for DME management. Currently, the first-line treatment for DME is anti-VEGF family drugs, such as ranibizumab, brolucizumab, bevacizumab, and aflibercept. Nevertheless, relapses of the disease, inadequate response, and resistance during anti-VEGF therapy are still seen because of the intricate pathophysiological foundation of the disease. Consequently, there is an excellent requirement for therapeutic approaches to advance and become better at controlling diseases more satisfactorily and require fewer treatments overall. We conducted a thorough literature search in the current review to present a comprehensive overview of the primary data about the current DME therapeutic agents. We also covered the novel advances in DME management and probable future treatments being investigated and developed. This review recommended that Large clinical trials should afford sufficient evidence to support these innovative treatment modalities.

ChatGPT has shown significant promise in transforming medical education by streamlining research and improving teaching methods. However, its adoption in Middle Eastern medical education has remained underexplored. This study investigated the factors influencing the adoption of ChatGPT in Jordanian medical education using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A cross-sectional survey was conducted with medical students and faculty members at the University of Jordan. A validated 33-item questionnaire distributed online and on campus targeted individuals familiar with the ChatGPT. Structural equation modeling (SEM) assessed the relationships between key constructs, including Performance Expectancy (PE), Effort Expectancy (EE), Perceived Risk (PR), Facilitating Conditions (FC), and attitude (ATT). Among 127 participants (53% male, mean age 23.2 ± 7.6), ATT was significantly influenced by PE and EE, explaining 37% of its variance. Behavioral Intention (BI) was predicted by ATT and had a significant positive effect on actual usage. FC did not significantly influence EE or BI, suggesting a limited reliance on external support. Contrary to expectations, PR did not negatively affect ATT, indicating that utility outweighed concerns about misinformation or privacy. Overall, the model explained 53% of the variance in BI and 36.5% of the variance in actual usage. The adoption of ChatGPT in Jordanian medical education is driven by perceived utility and ease of use, with attitudes playing a pivotal role. Addressing misinformation risks and improving trust through tailored strategies can foster broader integration of AI tools, such as ChatGPT, in medical training.

Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of symptoms and has a complicated pattern of occurrence, leading to a significant number of deaths. IE poses significant diagnostic and treatment difficulties. This evaluation examines the utilization of artificial intelligence (AI) and machine learning (ML) models in addressing information extraction (IE) management. It focuses on the most recent advancements and possible applications. Through this paper, we observe that AI/ML can significantly enhance and outperform traditional diagnostic methods leading to more accurate risk stratification, personalized therapies as well and real-time monitoring facilities. For example, early postsurgical mortality prediction models like SYSUPMIE achieved ‘very good’ area under the curve (AUROC) values exceeding 0.81. Additionally, AI/ML has improved diagnostic accuracy for prosthetic valve endocarditis, with PET-ML models increasing sensitivity from 59% to 72% when integrated into ESC criteria and reaching a high specificity of 83%. Furthermore, inflammatory biomarkers such as IL-15 and CCL4 have been identified as predictive markers, showing 91% accuracy in forecasting mortality, and identifying high-risk patients with specific CRP, IL-15, and CCL4 levels. Even simpler ML models, like Naïve Bayes, demonstrated an excellent accuracy of 92.30% in death rate prediction following valvular surgery for IE patients. Furthermore, this review provides a vital assessment of the advantages and disadvantages of such AI/ML models, such as better-quality decision support approaches like adaptive response systems on one hand, and data privacy threats or ethical concerns on the other hand. In conclusion, Al and ML must continue, through multi-centric and validated research, to advance cardiovascular medicine, and overcome implementation challenges to boost patient outcomes and healthcare delivery. Export

Neuro-Behçet’s Disease (NBD) is a serious neurological complication of Behçet’s Disease, but there’s still a lack of research on its early-stage symptoms and imaging findings. While previous research highlights MRI abnormalities and cerebrospinal fluid changes, there hasn’t been a systematic effort to identify patterns specific to newly diagnosed patients. Our study aimed to investigate the clinical and imaging characteristics of newly diagnosed parenchymal and non-parenchymal NBD based on case reports and case series studies. We synthesized case reports and case series from MEDLINE, Scopus, EMBASE, PubMed, Web of Science, and Cochrane Library until October 17th,2023. Multiple statistical tests, such as the chi-square and Mann-Whitney tests, were used, with the corresponding 95% confidence interval (CI). The study protocol was registered with PROSPERO (ID: CRD42023472436). We included case reports and case series of adult patients (≥ 18 years) with a confirmed diagnosis of parenchymal or non-parenchymal Neuro-Behçet’s disease (NBD), based on neurological symptoms attributed to Behçet’s disease. Studies were required to report MRI findings demonstrating central or peripheral nervous system involvement. Our analysis of 107 case reports and a case series (n = 174 patients) revealed that parenchymal NBD patients (n = 141; mean age 35.54 ± 13.33 years) frequently presented with ocular manifestations (80.9%), pyramidal signs (57.5%), cranial nerve palsies (50.4%), and headache (32.6%) while non-parenchymal NBD patients (n = 19; mean age 28.68 ± 10.67 years) commonly experienced headache (86.4%), papilledema (47.4%), cranial nerve palsies (42.1%), and nausea (31.6%). For the mixed-type group (n = 14; mean age 27.5 ± 11.57 years), commonly presented with headache (64.3%), pyramidal sign (50%), fever (50%), and diplopia (35.7%). Brainstem involvement for parenchymal NBD predominated in acute and chronic cases. Contrast enhancement was significantly more prevalent in acute NBD compared to chronic progressive NBD. Neuro-Behçet’s disease is an uncommon but serious medical condition with varying symptoms based on its type. Parenchymal and non-parenchymal NBD have unique patterns, with parenchymal cases exhibiting primarily ocular symptoms and pyramidal signs, while non-parenchymal cases present with headache and papilledema. Patients with both types of features were more likely to experience headaches, pyramidal symptoms, fever, and diplopia. Recognizing these patterns can help diagnose and treat the disease earlier.

Patients with congenital heart disease (CHD) are more likely to experience ischemic and hemorrhagic stroke due to factors such as arrhythmias, residual shunts and related cardiovascular complications. However, guidelines for identifying CHD patients at the highest risk of stroke remain unclear. In this study, we aimed to evaluate the risk of developing stroke in patients with CHD. A systematic literature search was performed on PubMed, Scopus, Cochrane, and Embase databases to retrieve studies that evaluated stroke risk in patients with CHD. Random effects model was used to pool the hazard ratios (HR) with 95% confidence intervals (CI). Subgroup analysis was conducted on age, type of stroke, type of study and region. Publication bias was assessed by Egger’s regression test. Statistical significance was set at p < 0.05. All the analysis was performed using R studio V4.3.1. Eleven studies (5,490,412 participants) were included in this systematic review and meta-analysis. Patients with CHD were at a higher risk of stroke [Pooled HR: 3.25; 95% CI: 2.25, 4.68; p < 0.01; I2: 100%] than the control group. In subgroup analysis, patients with CHD were at a higher risk of ischemic stroke [Pooled HR: 4.45; 95% CI: 2.24, 8.85; p < 0.01; I2: 100%] and hemorrhagic stroke [Pooled HR: 4.70; 95% CI: 1.70, 12.96; p < 0.01; I2: 99%] than the control group. Our meta-analysis indicates a significantly increased stroke risk in patients with CHD. Subgroup analyses showed higher stroke risk in European regions compared to Asia and USA, and among adults compared to pediatric populations. Future studies should focus on addressing regional and data limitations to better inform clinical strategies for managing stroke risk in CHD patients.

Despite the development of diverse treatment options, there has been an increase in mortality rates for laryngeal squamous cell carcinoma (LSCC). Our research employed survival analysis and machine learning (ML) techniques to evaluate the impact of different therapeutic options on survival and to build a prognostic model for individualized clinical decisions in advanced LSCC. The Surveillance, Epidemiology and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables for patients with LSCC, we conducted Cox regression analysis and constructed prognostic models using five machine learning (ML) algorithms to predict 5-year survival. A method of validation that incorporated the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using Kaplan Meier (K-M) survival analysis. The study included 7350 patients, of whom 2689 were diagnosed with glottic cancer (GC), 4349 with supraglottic (SuGC) and 312 with subglottic (SC). ML models identified age, sex, and stage as the most important factors that affect survival. In terms of treatment, bets survival therapeutic options for all anatomical sites was surgery and radiotherapy (RT). Employing multimodal therapies such as surgery and radiotherapy is crucial for managing advanced-stage LSCC. Tailored approaches that consider prognostic factors such as age, sex, and tumor stage are necessary. Additionally, chemotherapy did not significantly impact overall survival, suggesting potential areas for improvement in LSCC management.