Benefits of using intrathecal buprenorphine

Background: General anesthesia draws attention to the most commonly used modalities for post cesarean delivery pain relief in systemic administration of opioids, while the administration of small dose of intrathecal opioid during spinal anesthesia can be a possible alternative. The aim of this study was to evaluate the effects of buprenorphine on cesarean section prescribed intrathecally. Methods: This double blind randomized clinical trial study was conducted in patients for cesarean section under spinal anesthesia. The patients were randomly divided into case and control groups. Case group (208 patients) received 65-70 mg of 5% lidocaine plus 0.2 ml of buprenorphine while the same amount of 5% lidocaine diluted with 0.2 ml of normal saline was given to 234 cases in the control group. Hemodynamic changes and neonatal APGAR scores (Appearance, Pulse, Grimace, Activity, Respiration) were recorded. Pain score was recorded according to the visual analog scale. This study was registered in the Iranian Registry of clinical Trials; IRCT2013022112552N1. Results: The mean age of case and control groups was 24.4±5.38 and 26.84±5.42 years, respectively. Systolic blood pressure was not significantly different until the 45th minute but diastolic blood pressure showed a significant difference at the 15th and the 60th minutes (P<0.001). Heart rate changes were significantly different between cases and controls at the initial 5th, 15th and after 60th minutes (P<0.001). Pain-free period was significantly different between two groups (1.25 h versus 18.73 h) (P<0.001). Conclusion: The results show that prescription of intratechal buprenorphine prolongs the duration of analgesia without any significant considerable side effects

Distinct Role of IL-27 in Immature and LPS-Induced Mature Dendritic Cell-Mediated Development of CD4

Interleukin-27 (IL-27) plays an important role in regulation of anti-inflammatory responses and autoimmunity; however, the molecular mechanisms of IL-27 in modulation of immune tolerance and autoimmunity have not been fully elucidated. Dendritic cells (DCs) play a central role in regulating immune responses mediated by innate and adaptive immune systems, but regulatory mechanisms of DCs in CD4+ T cell-mediated immune responses have not yet been elucidated. Here we show that IL-27 treated mature DCs induced by LPS inhibit immune tolerance mediated by LPS-stimulated DCs. IL-27 treatment facilitates development of the CD4+ CD127+3G11+ regulatory T cell subset in vitro and in vivo. By contrast, IL-27 treated immature DCs fail to modulate development of the CD4+CD127+3G11+ regulatory T cell sub-population in vitro and in vivo. Our results suggest that IL-27 may break immune tolerance induced by LPS-stimulated mature DCs through modulating development of a specific CD4+ regulatory T cell subset mediated by 3G11 and CD127. Our data reveal a new cellular regulatory mechanism of IL-27 that targets DC-mediated immune responses in autoimmune diseases such as multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). © 2018 Zhou, Zhang and Rostami

Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance

This paper proposes a novel multi-objective optimisation approach to solving both the problem of finding good structural and parametric choices in an ANN and the problem of training a classifier with a heavily skewed data set. The state-of-the-art CMA-PAES-HAGA multi-objective evolutionary algorithm [41] is used to simultaneously optimise the structure, weights, and biases of a population of ANNs with respect to not only the overall classification accuracy, but the classification accuracies of each individual target class. The effectiveness of this approach is then demonstrated on a real-world multi-class problem in medical diagnosis (classification of fetal cardiotocograms) where more than 75% of the data belongs to the majority class and the rest to two other minority classes. The optimised ANN is shown to significantly outperform a standard feed-forward ANN with respect to minority class recognition at the cost of slightly worse performance in terms of overall classification accuracy