Objective To explore the effects of basic fibroblast growth factor(bFGF) pre-inducing on marrow stroma cells(MSCs) which were induced into dopaminergic neurons.Methods The rat MSCs were isolated primarily from the femurs and tibias of the Wistar rats.MSCs were cultured,proliferated and purified by passage culture.After being induced by bFGF,cultuered MSCs were divided into experimental groups(GM1 group,GDNF group and GDNF+ GM1 group) and control group.The surface markers of the differentiated neuron,such as neurone specific enolase(NSE),glial fibrillary acidic protein(GFAP) and TH were detected by immunocytochemistry after MSCs were cultured in induction media for 3 days and 7 days.Results In control group,the NSE expression of MSCs was very low.Many NSE-positive cells and TH-positive cells were found in the experimental groups at 3 or 7 days after induction.The percentage of NSE-positive and TH-positive cells of GDNF+GM1 group was significantly more than the other experimental groups(all P

Objective To establish a method of quick three-dimensional (3D) reconstruction of pubic symphysis based on magnetic resonance imaging. Methods The pelvis images of adult male were generated on a 3.0 T scanner using a T1 Gradient Echo FLASH-3D (T1- FL3D) sequence and imported the images into medical image control system. Segmentation of binaryzation threshold was conducted and pelvic soft tissue image was extracted by regional growth, 3D structure model of pubic symphysis was obtained by Boolean operation. The 3D structure model of pubic symphysis was established by the noise reduction of reverse engineering software. And compared with the 3D reconstruction model pubic bone CT scan. Results The morphological characters of the MRI pubic symphysis 3D model, such as the ridges and furrows on the symphysial surface, lower extremity, dorsal margin (beveling), margin (beveling) and pubic tubercle, were highly consistent with the morphological characters of the 3D model established by CT scan. Conclusion MRI scan can be used to reconstruct the 3D structure of pubic symphysis quickly and effectively, and it can provide a safe radiation-free 3D visualization imaging technique for forensic age estimation for the living.

OBJECTIVE: To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice. METHODS: A comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally. RESULTS: The rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations. CONCLUSION The expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.
Artificial Intelligence ; Orthopedics ; Machine Learning ; Algorithms