Publications by authors named "D Nehme Nasr"
Objectives: The growing trend of minimally invasive approaches has encouraged the application of partial coverage designs in restorative dentistry. This study aimed to evaluate the cyclic fatigue performance of different CAD-CAM materials used in various partial coverage designs for premolar restorations.
Methods: A freshly extracted upper premolar was prepared using a high-speed handpiece to create mesio-occluso-distal (MOD) cavities with standardized dimensions.
Ultra-translucent zirconia has gained popularity in restorative dentistry due to its excellent esthetic properties and high strength. Surface treatment methods play a crucial role in optimizing the bond between zirconia and enamel. This laboratory study aimed to investigate the influence of acid-etching solution on the surface properties of ultra-translucent zirconia and its subsequent shear bond strength to enamel.
View Article and Find Full Text PDFObjectives: To describe the characteristics and causes of non-aneurysmal thunderclap headache (TCH) and compare serious from benign underlying causes.
Methods: Retrospective cohort study of consecutive adult patients with TCH presenting to a tertiary care academic medical center between 2010 and 2020. Aneurysmal subarachnoid hemorrhage cases were excluded.
Article Synopsis
- Ischemic stroke, a major cause of death, highlights the need for effective detection methods like echocardiography and emerging alternatives such as cardiac CT (CCT) to identify potential cardiac sources of stroke.
- A systematic review and meta-analysis of 43 studies, involving 9,552 patients, was conducted to compare the effectiveness of CCT against the traditional echocardiography (specifically TEE) in detecting intracardiac thrombus.
- The analysis found that CCT demonstrated high sensitivity (98.38%) and specificity (96.0%) for thrombus detection, with the best results coming from delayed-phase, electrocardiogram-gated CCT, while early-phase, nongated CCT had slightly
Article Synopsis
- Dedicated brain imaging for cancer patients is rarely recommended unless symptoms arise, and brain metastases are often difficult to identify on non-enhanced CT (NE-CT).
- This research aims to create a 3D Convolutional Neural Network (3D-CNN) using NE-CT scans to effectively distinguish between cancer patients with and without brain metastases.
- The study utilized a dataset of 200 patients (100 with and 100 without brain abnormalities), achieving high accuracy (ACC of 0.983) and sensitivity (0.983) in detecting brain metastases, indicating the potential for a reliable diagnostic tool with further validation needed.