Anti-MME polyclonal antibody [Biotin]

Introduction: In 2013, we implemented a pill-based, multi-modal pain regimen (MMPR) in order to decrease in-hospital opioid exposure after injury at our trauma center. We hypothesized that the MMPR would decrease inpatient oral morphine milligram equivalents (MME), decrease opioid prescriptions at discharge, and result in similar Numerical Rating Scale (NRS) pain scores. Methods: Adult patients admitted to a level-1 trauma center with >= 1 rib fracture from 2010 to 2017 were included - spanning 3 years before and 4 years after MMPR implementation. MME were summarized as medians and interquartile range (IQR) by year of admission. The effect of the MMPR on daily total MME was estimated using Bayesian generalized linear model. Results: Over the 8 year study period, 6,933 patients who met study inclusion criteria were included. No significant differences between years were observed in Abbreviated Injury Scale (AIS) Chest or Injury Severity Scores (ISS). After introduction of the MMPR, there was a significant reduction in median total MME administered per patient day from 60 MME/patient day (IQR 36-91 MME/patient day) pre-MMPR implementation to 37 MME/patient day (IQR 18-61 MME/patient day) in 2017, p <0.01. Total MME administered per patient day decreased by 31% in 2017 as compared to 2010 (rate ratio 0.69, 95% CI 0.64 -0.75). Average NRS pain scores decreased by 0.8 points (95% CI -0.87, -0.81) from 2010 to 2017. Conclusion: The introduction of a multi-modal pain regimen resulted in significant reduction in inpatient opioid exposure after injury. The reduction in inpatient opioid use from 2010 to 2017 was equivalent to 11 mg less oxycodone or 17 mg less hydrocodone per patient per day. Additionally, use of the MMPR was associated with a reduction in NRS pain scores. Published by Elsevier Inc.

Cognitive radio is considered an effective solution to spectrum shortages, which has been a significant issue in the next generation of wireless communications. In this paper, we focus on spectrum sensing under a low signal-to-noise-ratio (SNR) and noise fluctuation and propose a symmetric peaks-based spectrum sensing algorithm for modulated signals. First, we analyze the characteristics of the cyclic autocorrelation function of modulated signals, and construct a detection domain for detecting primary users based on the characteristics of the cyclic autocorrelation function of primary signals. Then, we introduce the significance level factor into the spectrum sensing, and develop a symmetric peaks criterion. Following this criterion, we propose a symmetric peaks-based spectrum sensing algorithm. Finally, we give the probabilities of detection and false alarm of the spectrum sensing algorithm, discuss the effect of the significance level factor on the spectrum sensing performance, and compare the complexity of the algorithm with that of other algorithms. The spectrum sensing algorithm proposed does not require any prior knowledge of primary user signals or noise in the systems, and can sense modulated signals under very low SNR. Simulation results are provided to verify the performance of the algorithm proposed under a low SNR and noise fluctuation. Compared with the maximum and minimum eigenvalue (MME) algorithm, frequency domain autocorrelation-based (FD-AC) algorithm and statistical knowledge autocorrelation-based (SKAB) algorithm, it improves about 4 dB margin in SNR.