Correlation Analysis Between Magnetic Resonance Imaging-Based Anatomical Assessment and Behavioral Outcome in a Rat Contusion Model of Chronic Thoracic Spinal Cord Injury.

Although plenty of evidences from preclinical studies have led to potential treatments for patients with spinal cord injury (SCI), the failure to translate promising preclinical findings into clinical advances has long puzzled researchers. Thus, a more reliable combination of anatomical assessment and behavioral testing is urgently needed to improve the translational worth of preclinical studies. To address this issue, the present study was designed to relate magnetic resonance imaging (MRI)-based anatomical assessment to behavioral outcome in a rat contusion model.

The application of machine learning algorithms in predicting the length of stay following femoral neck fracture.

Purpose: Femoral neck fracture is a frequent cause of hospitalization, and length of stay is an important marker of hospital cost and quality of care provided. As an extension of traditional statistical methods, machine learning provides the possibility of accurately predicting the length of hospital stay. The aim of this paper is to retrospectively identify predictive factors of the length of hospital stay (LOS) and predict the postoperative LOS by using machine learning algorithms.

Low-intensity pulsed ultrasound regulates proliferation and differentiation of neural stem cells through notch signaling pathway.

Low-intensity pulsed ultrasound (LIPUS) is widely used to regulate stem cell proliferation and differentiation. However, the effect of LIPUS stimulation on neural stem cells (NSCs) is not well documented. In this study, we have identified the optimal parameters, and investigated the cellular mechanisms of LIPUS to regulate the proliferation and differentiation of NSCs in vitro.