The effects of gabapentin and pregabalin on fracture healing: A histological, radiological, and biomechanical analysis.

Objectives: This study evaluated the impact of different doses of gabapentin and pregabalin on fracture healing in a rat femoral shaft model, with histological, radiological, and biomechanical assessments.

Materials And Methods: Seventy male Wistar albino rats were divided into five groups: control, low-dose gabapentin (GBP-L, 300 mg/day), high-dose gabapentin (GBP-H, 3600 mg/day), low-dose pregabalin (PRG-L, 150 mg/day), and high-dose pregabalin (PRG-H, 600 mg/day), based on human equivalent doses. Bilateral femoral fractures were induced; the right femurs were prepared for radiological examination using microtomography, followed by histological analysis, whereas the left femurs were allocated for biomechanical testing.

High Genetic Diagnostic Yield of Whole Exome Sequencing in Children with Epilepsy and Neurodevelopmental Disorders.

Nowadays, the diagnostic rate of childhood epilepsies is increasing rapidly in parallel with the advances in genetic technology. In this study, it was aimed to reveal the diagnostic yield of whole exome sequencing (WES) in children with epilepsy and neurodevelopmental disorders (NDDs). Children aged 1 to 17 years with epilepsy and NDD who underwent WES were included in this retrospective study.

Invasive Management of Iatrogenic Left Internal Mammary Artery-Great Cardiac Vein Anastomosis: A Case Report.

Coronary artery bypass graft (CABG) surgery is one of the main treatment procedures for revascularization. Diagnosing postoperative complications can be difficult. One rare complication is the iatrogenic connection of the graft to the venous system, causing distal ischemia due to an arteriovenous shunt.

Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.

This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as , , , , and were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images.