BackgroundBone marrow stromal cells (BMSCs) have shown potential to treat chronic pain, although much still needs to be learned about their efficacy and mechanisms of action under different pain conditions. Here, we provide further convergent evidence on the effects of BMSCs in rodent pain models.ResultsIn an orofacial pain model involving injury of a tendon of the masseter muscle, BMSCs attenuated behavioral pain conditions assessed by von Frey filaments and a conditioned place avoidance test in female Sprague-Dawley rats. The antihyperalgesia of BMSCs in females lasted for <8 weeks, which is shorter than that seen in males. To relate preclinical findings to human clinical conditions, we used human BMSCs. Human BMSCs (1.5 M cells, i.v.) attenuated mechanical and thermal hyperalgesia induced by spinal nerve ligation and suppressed spinal nerve ligation-induced aversive behavior, and the effect persisted through the 8-week observation period. In a trigeminal slice preparation, BMSC-treated and nerve-injured C57B/L mice showed reduced amplitude and frequency of spontaneous excitatory postsynaptic currents, as well as excitatory synaptic currents evoked by electrical stimulation of the trigeminal nerve root, suggesting inhibition of trigeminal neuronal hyperexcitability and primary afferent input by BMSCs. Finally, we observed that GluN2A (N-methyl-D-aspartate receptor subunit 2A) tyrosine phosphorylation and protein kinase Cgamma (PKCγ) immunoreactivity in rostral ventromedial medulla was suppressed at 8 weeks after BMSC in tendon-injured rats.ConclusionsCollectively, the present work adds convergent evidence supporting the use of BMSCs in pain control. As PKCγ activity related to N-methyl-D-aspartate receptor activation is critical in opioid tolerance, these results help to understand the mechanisms of BMSC-produced long-term antihyperalgesia, which requires opioid receptors in rostral ventromedial medulla and apparently lacks the development of tolerance.

SUMMARY1. Spinal glial cells play a key role in developing and maintaining allodynia and hyperalgesia following tissue inflammation. Dexmedetomidine, a highly selective a 2 -adrenoceptor (a 2 -AR) agonist, has exhibited a significant analgesic effect in various rodent models of chronic pain. However, whether spinal glial activation is involved in the analgesic effect of dexmedetomidine remains unknown. The present study investigated whether spinal administration of dexmedetomidine could antagonize glial activation in the spinal dorsal horn and attenuate thermal hyperalgesia in complete Freund's adjuvant (CFA)-induced ankle joint monoarthritic (MA) rats.2. Unilateral intra-articular injection of CFA produced a robust activation of microglia and astrocytes in the spinal cord, which was associated with the development and maintenance of thermal hyperalgesia. Repeated lumbar puncture (LP) administration of dexmedetomidine (10 lg) significantly attenuated MA-induced thermal hyperalgesia in a cumulative manner. Monoarthritis-induced spinal glial activation was also suppressed following dexmedetomidine application. The a 2A -AR, essential for the antinociceptive effects of a 2 -AR agonists, was detected in spinal neurons and glia, as well as in dorsal root ganglion primary afferent neurons, which may be implicated in dexmedetomidine-induced suppression of spinal glial activation and antihyperalgesic effects.3. These data provide the first evidence that blocking spinal glial activation is involved in the analgesic action of dexmedetomidine.

In order to solve the problem of low face recognition accuracy of traditional algorithms and excessive long training time in deep learning methods, a novel lightweight and short training time multiface recognition method is proposed in this paper. Firstly, an extraction model of facial feature vectors is established based on local binary mode and principal component analysis. Secondly, the beetle antennae search algorithm (BAS) is optimized using adaptive factors, and the ABAS algorithm is proposed. This paper uses the ABAS algorithm to optimize the initial threshold of the neural network and proposes the ABASNet method. Thirdly, ABASNet method is combined with the face features extracted by the face feature vector extraction model to be used for multi-face classification tasks. Finally, H-softmax (hierarchical softmax) is used to replace softmax in neural networks. By reducing the amount of calculation in the process of multi-category face classification, the training time of the ABASNet network is reduced. Through ablation experiments and comparative experiments with various methods such as IKDA + PNN algorithm and PCANet algorithm, to verify the accuracy, robustness and short training time of the ABASNet method. Among them, the maximum accuracy of the method in the ORL face dataset, ExtYaleB face dataset and FERET face dataset is 99.35%, 99.54% and 99.18% respectively. In addition, actual test results indicate that the training time and recognition time of the method in this paper are 21 seconds and 0.06 seconds respectively, which has demonstrated the lightweight and real-time performance of the proposed method. At the same time, more test results show that the proposed ABASNet method can also be implemented in embedded devices and other classification tasks. INDEX TERMS Beetle antennae search, neural network, hierarchical softmax, multiface recognization.