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. Rats underwent contusion with three different heights to simulate various severities of SCI, and their locomotive functions were evaluated by the grid-walking test, Louisville swim scale (LSS), especially catwalk gait analysis system and basic testing, and Basso, Beattie, Bresnahan (BBB) score. The results showed that the lesion area (LA) is a better indicator for damage assessment compared with other parameters in sagittal T2-weighted MRI (T2WI). Although two samples are marked as outliers by the box plot analysis, LA correlated closely with all of the behavioral testing without ceiling effect and floor effect. Moreover, with a moderate severity of SCI in a contusion height of 25 mm, the smaller the LA of the spinal cord measured on sagittal T2WI the better the functional performance, the smaller the cavity region and glial scar, the more spared the myelin, the higher the volatility, and the thicker the bladder wall. We found that LA significantly related with behavior outcomes, which indicated that LA could be a proxy of damage assessment. The combination of sagittal T2WI and four types of behavioral testing can be used as a reliable scheme to evaluate the prognosis for preclinical studies of SCI.

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. METHOD: Based on the admission and perioperative data of the patients, linear regression was used to analyze the predictive factors of the LOS. Multiple machine learning models were developed, and the performance of different models was compared. RESULT: Stepwise linear regression showed that preoperative calcium level (P = 0.017) and preoperative lymphocyte percentage (P = 0.007), in addition to intraoperative bleeding (p = 0.041), glucose and sodium chloride infusion after surgery (P = 0.019), Charlson Comorbidity Index (p = 0.007) and BMI (P = 0.031), were significant predictors of LOS. The best performing model was the principal component regression (PCR) with an optimal MAE (1.525) and a proportion of prediction error within 3 days of 90.91%. CONCLUSION: Excessive intravenous glucose and sodium chloride infusion after surgery, preoperative hypocalcemia, preoperative high percentages of lymphocytes, excessive intraoperative bleeding, lower BMI and higher CCI scores were related to prolonged LOS by using linear regression. Machine learning could accurately predict the postoperative LOS. This information allows hospital administrators to plan reasonable resource allocation to fulfill demand, leading to direct care quality improvement and more reasonable use of scarce resources.

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. NSCs were obtained and identified by nestin immunostaining. The proliferation of NSCs were measured by using Cell Counting Kit-8 (CCK-8). The expressions of nutritional factors (NTFs) were detected with immunoassay (ELISA). NSCs differentiation were detected by immunofluorescence and immunoblotting analysis. The expression level of proteins involved in the Notch signaling pathway was also measured by immunoblotting assay. Our results showed the intensity of 69.3 mW/cm2 (1 MHz, 8 V) was applicable for LIPUS stimulation. ELISA analysis demonstrated that LIPUS treatment promoted the expression of nutritional factors of NSCs in vitro. Immunofluorescence and immunoblotting analyses suggested that the LIPUS not only reduced the astrocyte differentiation, but also stimulated the differentiation to neurons. Additionally, LIPUS stimulation significantly upregulated expression level of Notch1 and Hes1. Results from our study suggest that LIPUS triggers NSCs proliferation and differentiation by modulating the Notch signaling pathway. This study implies LIPUS as a potential and promising therapeutic platform for the optimization of stem cells and enable noninvasive neuromodulation for central nervous system diseases.