SUMMARY Why do opiates make human beings itch ? Spinal opioid-induced itch, a prevalent side effect of pain management, has been considered to occur as a result of pain inhibition. We report that morphine-induced scratching (MIS) is abolished in mice lacking either gastrin-releasing peptide receptor (GRPR) or the μ opioid receptor (MOR). Using exon-specific knockdown, we identified the MOR1D isoform as essential for MIS, whereas MOR1 is important for morphine-induced analgesia (MIA) with no cross activity present. MOR1D and GRPR form constitutive heterodimers in the spinal cord and relay itch information upon morphine activation. Morphine induces internalization of both GRPR and MOR1D, whereas GRP induces that of GRPR but not MOR1D, when co-expressed. Moreover, GRP-induced scratching (GIS) is independent of MOR activation. These results suggest a unidirectional cross-activation of GRPR signaling by MOR1D via heterodimerization, and that opioid-induced itch is an active process concomitant with but independent of opioid analgesia.

Empirical mode decomposition (EMD) is an adaptive filter bank for processing nonlinear and non-stationary signals, such as electroencephalographic (EEG) signals. EMD works well to decompose a time series into a set of intrinsic mode functions with specific frequency bands. An IMF therefore represents an intrinsic component on its correspondingly intrinsic frequency band. The word of ‘intrinsic’ means the frequency is totally adaptive to the nature of a signal. In this study, power density and nonlinearity are two critical parameters for characterizing the amplitude and frequency modulations in IMFs. In this study, a nonlinearity level is quantified using degree of waveform distortion (DWD), which represents the characteristic of waveform distortion as an assessment of the intra-wave modulation of an IMF. In the application of anesthesia EEG analysis, the assessments of power density and DWD for a set of IMFs represent dynamic responses in EEG caused by two different anesthesia agents, Ketamine and Alfentanil, on different frequency bands. Ketamine causes the increase of power density and the decrease of nonlinearity on γ-band neuronal oscillation, which cannot be found EEG responses of group B using Alfentanil. Both agents cause an increase of power density and a decrease of nonlinearity on β-band neuronal oscillation accompany with a loss of consciousness. Moreover, anesthesia agents cause the decreases of power density and nonlinearity (i.e. DWD) for the low-frequency IMFs.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.