An induced pluripotent stem cell line (FHUSTCi002-A) derived from a patient with leucoencephalopathy with brain stem and spinal cord involvement and lactate elevation.

Leucoencephalopathy with brain stem and spinal cord involvement and lactate elevation (LBSL) is commonly induced by DARS2 abnormalities and accompanied by slowly progressing pyramidal and cerebellar dysfunction, as well as concomitant dorsal column dysfunction. In this study, an LBSL induced pluripotent stem cell (iPSC) line was generated from peripheral blood mononuclear cells of a female patient carrying biallelic mutations in DARS2. Pluripotency, differentiation potential, and karyotypic normality of this cell line were confirmed. This iPSC line offers a useful cellular model to investigate LBSL phenotypes, mechanisms, and therapy.

Real-time modeling and feature extraction method of surface electromyography signal for hand movement classification based on oscillatory theory.

Objective.Research of surface electromyography (sEMG) signal modeling and feature extraction is important in human motion intention recognition, prosthesis and exoskeleton robots. However, the existing methods mostly use the signal segmentation processing method rather than the point-to-point signal processing method, and lack physiological mechanism support.Approach. In this study, a real-time sEMG signal modeling and separation method is developed based on oscillatory theory. On this basis, an sEMG signal feature extraction method is constructed, and an ensemble learning method is combined to achieve real-time human hand motion intention recognition.Main results.The experimental results show that the average root mean square difference value of the sEMG signal modeling is 0.3838 ± 0.0591, and the average accuracy of human hand motion intention recognition is 96.03 ± 1.74%. On a computer with Intel (R) Core (TM) i5-8250U CPU running Matlab 2016Rb, the execution time for the sEMG signal with an actual duration of 2 s is 0.66 s.Significance. Compared with several existing methods, the proposed method has better modeling accuracy, motion intention recognition accuracy and real-time performance. The method developed in this study may provide a new perspective on sEMG modeling and feature extraction for hand movement classification.

Involvement of glycine receptor α1 subunits in cannabinoid-induced analgesia.

Some cannabinoids have been shown to suppress chronic pain by targeting glycine receptors (GlyRs). Although cannabinoid potentiation of α3 GlyRs is thought to contribute to cannabinoid-induced analgesia, the role of cannabinoid potentiation of α1 GlyRs in cannabinoid suppression of chronic pain remains unclear. Here we report that dehydroxylcannabidiol (DH-CBD), a nonpsychoactive cannabinoid, significantly suppresses chronic inflammatory pain caused by noxious heat stimulation. This effect may involve spinal α1 GlyRs since the expression level of α1 subunits in the spinal cord is positively correlated with CFA-induced inflammatory pain and the GlyRs antagonist strychnine blocks the DH-CBD-induced analgesia. A point-mutation of S296A in TM3 of α1 GlyRs significantly inhibits DH-CBD potentiation of glycine currents (IGly) in HEK-293 cells and neurons in lamina I-II of spinal cord slices. To explore the in vivo consequence of DH-CBD potentiation of α1 GlyRs, we generated a GlyRα1S296A knock-in mouse line. We observed that DH-CBD-induced potentiation of IGly and analgesia for inflammatory pain was absent in GlyRα1S296A knock-in mice. These findings suggest that spinal α1 GlyR is a potential target for cannabinoid analgesia in chronic inflammatory pain.