Use of functional magnetic resonance imaging to identify cortical loci for lower limb movements and their efficacy for individuals after stroke.

BACKGROUND: Identification of cortical loci for lower limb movements for stroke rehabilitation is crucial for better rehabilitation outcomes via noninvasive brain stimulation by targeting the fine-grained cortical loci of the movements. However, identification of the cortical loci for lower limb movements using functional MRI (fMRI) is challenging due to head motion and difficulty in isolating different types of movement. Therefore, we developed a custom-made MR-compatible footplate and leg cushion to identify the cortical loci for lower limb movements and conducted multivariate analysis on the fMRI data. We evaluated the validity of the identified loci using both fMRI and behavioral data, obtained from healthy participants as well as individuals after stroke. METHODS: We recruited 33 healthy participants who performed four different lower limb movements (ankle dorsiflexion, ankle rotation, knee extension, and toe flexion) using our custom-built equipment while fMRI data were acquired. A subgroup of these participants (Dataset 1; n = 21) was used to identify the cortical loci associated with each lower limb movement in the paracentral lobule (PCL) using multivoxel pattern analysis and representational similarity analysis. The identified cortical loci were then evaluated using the remaining healthy participants (Dataset 2; n = 11), for whom the laterality index (LI) was calculated for each lower limb movement using the cortical loci identified for the left and right lower limbs. In addition, we acquired a dataset from 15 individuals with chronic stroke for regression analysis using the LI and the Fugl-Meyer Assessment (FMA) scale. RESULTS: The cortical loci associated with the lower limb movements were hierarchically organized in the medial wall of the PCL following the cortical homunculus. The LI was clearer using the identified cortical loci than using the PCL. The healthy participants (mean ± standard deviation: 0.12 ± 0.30; range: - 0.63 to 0.91) exhibited a higher contralateral LI than the individuals after stroke (0.07 ± 0.47; - 0.83 to 0.97). The corresponding LI scores for individuals after stroke showed a significant positive correlation with the FMA scale for paretic side movement in ankle dorsiflexion (R2 = 0.33, p = 0.025) and toe flexion (R2 = 0.37, p = 0.016). CONCLUSIONS: The cortical loci associated with lower limb movements in the PCL identified in healthy participants were validated using independent groups of healthy participants and individuals after stroke. Our findings suggest that these cortical loci may be beneficial for the neurorehabilitation of lower limb movement in individuals after stroke, such as in developing effective rehabilitation interventions guided by the LI scores obtained for neuronal activations calculated from the identified cortical loci across the paretic and non-paretic sides of the brain.

Heart Rate Variability as a Potential Indicator of Cancer Pain in a Mouse Model of Peritoneal Metastasis.

Heart rate variability (HRV) is closely related to changes in the autonomic nervous system (ANS) associated with stress and pain. In this study, we investigated whether HRV could be used to assess cancer pain in mice with peritoneal metastases. At 12 days after cancer induction, positive indicators of pain such as physiological characteristics, appearance, posture, and activity were observed, and time- and frequency-domain HRV parameters such as mean R-R interval, square root of the mean squared differences of successive R-R intervals, and percentage of successive R-R interval differences greater than 5 ms, low frequency (LF), high frequency (HF), and ratio of LF and HF power, were found to be significantly decreased. These parameters returned to normal after analgesic administration. Our results indicate that overall ANS activity was decreased by cancer pain and that HRV could be a useful tool for assessing pain.

Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network.

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

Electrical stimulation accelerates neurite regeneration in axotomized dorsal root ganglion neurons by increasing MMP-2 expression.

Electrical stimulation (ES) can be useful for promoting the regeneration of injured axons, but the mechanism underlying its positive effects is largely unknown. The current study aimed to investigate whether ES could enhance the regeneration of injured neurites in dorsal root ganglion explants and regulate the MMP-2 expression level, which is correlated with regeneration. Significantly increased neurite regeneration and MMP-2 expression was observed in the ES group compared with the sham group. However, an MMP inhibitor significantly decreased this ES-induced neurite regeneration. Our data suggest that the positive effect of ES on neurite regeneration could likely be mediated by an increase in MMP-2 expression, thereby promoting the regeneration of injured neurites.

Linear feature projection-based real-time decoding of limb state from dorsal root ganglion recordings.

Proprioceptive afferent activities recorded by a multichannel microelectrode have been used to decode limb movements to provide sensory feedback signals for closed-loop control in a functional electrical stimulation (FES) system. However, analyzing the high dimensionality of neural activity is one of the major challenges in real-time applications. This paper proposes a linear feature projection method for the real-time decoding of ankle and knee joint angles. Single-unit activity was extracted as a feature vector from proprioceptive afferent signals that were recorded from the L7 dorsal root ganglion during passive movements of ankle and knee joints. The dimensionality of this feature vector was then reduced using a linear feature projection composed of projection pursuit and negentropy maximization (PP/NEM). Finally, a time-delayed Kalman filter was used to estimate the ankle and knee joint angles. The PP/NEM approach had a better decoding performance than did other feature projection methods, and all processes were completed within the real-time constraints. These results suggested that the proposed method could be a useful decoding method to provide real-time feedback signals in closed-loop FES systems.

Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network.

The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network (DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life.

Implantation of a Matrigel-loaded agarose scaffold promotes functional regeneration of axons after spinal cord injury in rat.

An agarose scaffold can be useful for supporting and guiding injured axons after spinal cord injury (SCI), but the electrophysiological signal of regenerated axon in scaffolds has not yet been determined. The current study investigated whether a Matrigel-loaded agarose scaffold would enhance the regeneration of axons after SCI. Moreover, the functional connectivity of regenerated axons within the channels of the scaffold was evaluated by directly recording motor evoked potentials. Our data showed that the agarose scaffold containing Matrigel can support and enhance linearly organized axon regeneration after SCI. Additionally, motor evoked potentials were successfully recorded from regenerated axons. These results demonstrate that an agarose scaffold loaded with Matrigel could promote the regeneration of axons and guide the reconnection of functional axons after SCI.

The Effect of Triangular Fibrocartilage Complex Tear on Wrist Proprioception.

PURPOSE: This study examined the influence of triangular fibrocartilage complex (TFCC) deep fiber tears on wrist proprioception. METHODS: The study involved 48 subjects: 24 with deep fiber TFCC tears and 24 with healthy wrists. A specially created sensor measured wrist proprioception in 3 axes of movement. Absolute differences between target and subject-reproduced angles were compared in injured and healthy wrists and in injured and contralateral patient wrists. A greater difference in reproduced angles was deemed to reflect a lesser ability to approximate a target angle. RESULTS: In wrists with TFCC injuries, 40° pronation and 60° pronation showed significantly greater differences between target and subject-reproduced angles compared with those in the control wrists. In wrists with TFCC injuries, 40° pronation demonstrated significantly greater differences between target and subject-reproduced angles than did those in patients' contralateral wrists. Proportions of outliers with absolute differences greater than 6° were significantly higher in 60° supination and 40° pronation in wrists with TFCC injuries. CONCLUSIONS: Deep TFCC fiber detachment may lead to decreased wrist proprioception in 60° and 40° forearm rotation. CLINICAL RELEVANCE: Deep TFCC fiber tear may contribute to decreased wrist rotational positioning sense and may have biomechanical importance in distal radioulnar joint stability.

Linear Feature Projection-Based Sensory Event Detection from the Multiunit Activity of Dorsal Root Ganglion Recordings.

Afferent signals recorded from the dorsal root ganglion can be used to extract sensory information to provide feedback signals in a functional electrical stimulation (FES) system. The goal of this study was to propose an efficient feature projection method for detecting sensory events from multiunit activity-based feature vectors of tactile afferent activity. Tactile afferent signals were recorded from the L4 dorsal root ganglion using a multichannel microelectrode for three types of sensory events generated by mechanical stimulation on the rat hind paw. The multiunit spikes (MUSs) were extracted as multiunit activity-based feature vectors and projected using a linear feature projection method which consisted of projection pursuit and negentropy maximization (PP/NEM). Finally, a multilayer perceptron classifier was used to detect sensory events. The proposed method showed a detection accuracy superior to those of other linear and nonlinear feature projection methods and all processes were completed within real-time constraints. Results suggest that the proposed method could be useful to detect sensory events in real time. We have demonstrated the methodology for an efficient feature projection method to detect real-time sensory events from the multiunit activity of dorsal root ganglion recordings. The proposed method could be applied to provide real-time sensory feedback signals in closed-loop FES systems.

Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor.

Routine stress monitoring in daily life can predict potentially serious health impacts. Effective stress monitoring in medical and healthcare fields is dependent upon accurate determination of stress-related features. In this study, we determined the optimal stress-related features for effective monitoring of cumulative stress. We first investigated the effects of short- and long-term stress on various heart rate variability (HRV) features using a rodent model. Subsequently, we determined an optimal HRV feature set using support vector machine-recursive feature elimination (SVM-RFE). Experimental results indicate that the HRV time domain features generally decrease under long-term stress, and the HRV frequency domain features have substantially significant differences under short-term stress. Further, an SVM classifier with a radial basis function kernel proved most accurate (93.11%) when using an optimal HRV feature set comprising the mean of R-R intervals (mRR), the standard deviation of R-R intervals (SDRR), and the coefficient of variance of R-R intervals (CVRR) as time domain features, and the normalized low frequency (nLF) and the normalized high frequency (nHF) as frequency domain features. Our findings indicate that the optimal HRV features identified in this study can effectively and efficiently detect stress. This knowledge facilitates development of in-facility and mobile healthcare system designs to support stress monitoring in daily life.

The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors.

Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system.

An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode.

Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

TNF-α gene silencing using polymerized siRNA/thiolated glycol chitosan nanoparticles for rheumatoid arthritis.

Among various proinflammatory cytokines involved in the pathogenesis of rheumatoid arthritis (RA), tumor necrosis factor (TNF)-α plays a pivotal role in the release of other cytokines and induction of chronic inflammation. Even though siRNA has the therapeutic potential, they have a challenge to be delivered into the target cells because of their poor stability in physiological fluids. Herein, we design a nanocomplex of polymerized siRNA (poly-siRNA) targeting TNF-α with thiolated glycol chitosan (tGC) polymers for the treatment of RA. Poly-siRNA is prepared through self-polymerization of thiol groups at the 5' end of sense and antisense strand of siRNA and encapsulated into tGC polymers, resulting in poly-siRNA-tGC nanoparticles (psi-tGC-NPs) with an average diameter of 370 nm. In the macrophage culture system, psi-tGC-NPs exhibit rapid cellular uptake and excellent in vitro TNF-α gene silencing efficacy. Importantly, psi-tGC-NPs show the high accumulation at the arthritic joint sites in collagen-induced arthritis (CIA) mice. Treatment monitoring data obtained by the matrix metalloproteinase 3-specific nanoprobe and microcomputed tomography show that intravenous injection of psi-tGC-NPs significantly inhibits inflammation and bone erosion in CIA mice, comparable to methotrexate (5 mg/kg). Therefore, the availability of psi-tGC-NP therapy that target specific cytokines may herald new era in the treatment of RA.

Prediction of antiarthritic drug efficacies by monitoring active matrix metalloproteinase-3 (MMP-3) levels in collagen-induced arthritic mice using the MMP-3 probe.

Active matrix metalloproteinase-3 (MMP-3) is a prognostic marker of rheumatoid arthritis (RA). We recently developed an MMP-3 probe that can specifically detect the active form of MMP-3. The aim of this study was to investigate whether detection and monitoring of active MMP-3 could be useful to predict therapeutic drug responses in a collagen-induced arthritis (CIA) model. During the period of treatment with drugs such as methotrexate (MTX) or infliximab (IFX), MMP-3 mRNA and protein levels were correlated with fluorescence signals in arthritic joint tissues and in the serum of CIA mice. Also, bone volume density and erosion in the knee joints and the paws of CIA mice were measured with microcomputed tomography (micro-CT), X-ray, and histology to confirm drug responses. In joint tissues and serum of CIA mice, strong fluorescence signals induced by the action of active MMP-3 were significantly decreased when drugs were applied. The decrease in RA scores in drug-treated CIA mice led to fluorescence reductions, mainly as a result of down-regulation of MMP-3 mRNA or protein. The micro-CT, X-ray, and histology results clearly showed marked decreases in bone and cartilage destruction, which were consistent with the reduction of fluorescence by down-regulation of active MMP-3 in drug-treated CIA mice. We suggest that the MMP-3 diagnostic kit could be used to detect and monitor the active form of MMP-3 in CIA mice serum during a treatment course and thereby used to predict the drug response or resistance to RA therapies at an earlier stage. We hope that monitoring of active MMP-3 levels in arthritis patients using the MMP-3 diagnostic kit will be a promising tool for drug discovery, drug development, and monitoring of drug responses in RA therapy.

The effects of a minimally invasive laser needle system on complete Freund's adjuvant-induced arthritis.

The present study aimed to investigate the effects of a minimally invasive laser needle system (MILNS) on the acute progression of arthritis. Previous studies showed controversial clinical results regarding the effects of low-level laser therapy on arthritis, with the outcomes depending upon stimulation parameters such as laser wavelength and dosage. Based on the positive effects of MILNS on osteoporotic mice, we hypothesized that MILNS could potentially suppress the progression of arthritis owing to its biostimulation effects. Eight C57BL/6 mice with complete Freund's adjuvant (CFA)-induced arthritis were used as acute progression arthritis models and divided into the laser and control groups (n = 4 each). In the laser group, after minimally invasive laser stimulation, laser speckle contrast images (LSCIs) were obtained every 6 h for a total of 108 h. The LSCIs in the control group were obtained without laser stimulation. The effects of MILNS on the acute progression of arthritis were indirectly evaluated by calculating the paw area and the average laser speckle index (LSI) at the arthritis-induced area. Moreover, the macrophage population was estimated in the arthritis-induced area. Compared to the control group, the laser group showed (1) lower relative variations of the paw area, (2) lower average LSI in the arthritis-induced area, and (3) lower macrophage population in the arthritis-induced area. These results indicate that MILNS may suppress the acute progression of CFA-induced arthritis in mice and may thus be used as a potential treatment modality of arthritis in clinics.

Deep learning-based dynamic ventilatory threshold estimation from electrocardiograms.

BACKGROUND AND OBJECTIVE: The ventilatory threshold (VT) marks the transition from aerobic to anaerobic metabolism and is used to assess cardiorespiratory endurance. A conventional way to assess VT is cardiopulmonary exercise testing, which requires a gas analyzer. Another method for measuring VT involves calculating the heart rate variability (HRV) from an electrocardiogram (ECG) by computing the variability of heartbeats. However, the HRV method has some limitations. ECGs should be recorded for at least 5 minutes to calculate the HRV, and the result may depend on the utilized ECG preprocessing algorithms. METHODS: To overcome these problems, we developed a deep learning-based model consisting of long short-term memory (LSTM) and convolutional neural network (CNN) for a lead II ECG. Variables reflecting subjects' physical characteristics, as well as ECG signals, were input into the model to estimate VT. We applied joint optimization to the CNN layers to generate an informative latent space, which was fed to the LSTM layers. The model was trained and evaluated on two datasets, one from the Bruce protocol and the other from a protocol including multiple tasks (MT). RESULTS: Acceptable performances (mean and 95% CI) were obtained on the datasets from the Bruce protocol (-0.28[-1.91,1.34] ml/min/kg) and the MT protocol (0.07[-3.14,3.28] ml/min/kg) regarding the differences between the predictions and labels. The coefficient of determination, Pearson correlation coefficient, and root mean square error were 0.84, 0.93, and 0.868 for the Bruce protocol and 0.73, 0.97, and 3.373 for the MT protocol, respectively. CONCLUSIONS: The results indicated that it is possible for the proposed model to simultaneously assess VT with the inputs of successive ECGs. In addition, from ablation studies concerning the physical variables and the joint optimization process, it was demonstrated that their use could boost the VT assessment performance of the model. The proposed model enables dynamic VT estimation with ECGs, which could help with managing cardiorespiratory fitness in daily life and cardiovascular rehabilitation in patients.

Detection of active matrix metalloproteinase-3 in serum and fibroblast-like synoviocytes of collagen-induced arthritis mice.

The activity of rheumatoid arthritis (RA) correlates with the expression of proteases. Among several proteases, matrix metalloproteinase-3 (MMP-3) is one of the biological markers used to diagnose RA. The active form of MMP-3 is a key enzyme involved in RA-associated destruction of cartilage and bone. Thus, detection of active MMP-3 in serum or in vivo is very important for early diagnosis of RA. In this study, a soluble MMP-3 probe was prepared to monitor RA progression by detecting expression of active MMP-3 in collagen-induced arthritis (CIA) mice in vivo in both serum and fibroblast-like synoviocytes (FLSs). The MMP-3 probe exhibited strong sensitivity to MMP-3 and moderate sensitivity to MMP-7 at nanomolecular concentrations, but was not sensitive to other MMPs such as MMP-2, MMP-9, and MMP-13. In an optical imaging study, the MMP-3 probe produced early and strong NIR fluorescence signals prior to observation of erythema and swelling in CIA mice. The MMP-3 probe was able to rapidly and selectively detect and monitor active MMP-3 in diluted serum from CIA mice. Furthermore, histological data demonstrated that activated FLSs in arthritic knee joints expressed active MMP-3. Together, our results demonstrated that the MMP-3 probe may be useful for detecting active MMP-3 for diagnosis of RA. More importantly, the MMP-3 probe was able to detect active MMP-3 in diluted serum with high sensitivity. Therefore, the MMP-3 probe developed in this study may be a very promising probe, useful as a biomarker for early detection and diagnosis of RA.

Classification of gait phases based on a machine learning approach using muscle synergy.

The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.

Venous congestion affects neuromuscular changes in pigs in terms of muscle electrical activity and muscle stiffness.

Early detection of venous congestion (VC)-related diseases such as deep vein thrombosis (DVT) is important to prevent irreversible or serious pathological conditions. However, the current way of diagnosing DVT is only possible after recognizing advanced DVT symptoms such as swelling, pain, and tightness in affected extremities, which may be due to the lack of information on neuromechanical changes following VC. Thus, the goal of this study was to investigate acute neuromechanical changes in muscle electrical activity and muscle stiffness when VC was induced. The eight pigs were selected and the change of muscle stiffness from the acceleration and muscle activity in terms of integral electromyography (IEMG) was investigated in three VC stages. Consequently, we discovered a significant increase in the change in muscle stiffness and IEMG from the baseline to the VC stages (p < 0.05). Our results and approach can enable early detection of pathological conditions associated with VC, which can be a basis for further developing early diagnostic tools for detecting VC-related diseases.

IL-6/JAK2/STAT3 axis mediates neuropathic pain by regulating astrocyte and microglia activation after spinal cord injury.

After spinal cord injury (SCI), the control of activated glial cells such as microglia and astrocytes has emerged as a promising strategy for neuropathic pain management. However, signaling mechanism involved in glial activation in the process of neuropathic pain development and maintenance after SCI is not well elucidated. In this study, we investigated the potential role and mechanism of the JAK2/STAT3 pathway associated with glial cell activation in chronic neuropathic pain development and maintenance after SCI. One month after contusive SCI, the activation of JAK2/STAT3 pathway was markedly upregulated in both microglia and astrocyte in nociceptive processing regions of the lumbar spinal cord. In addition, both mechanical allodynia and thermal hyperalgesia was significantly inhibited by a JAK2 inhibitor, AG490. In particular, AG490 treatment inhibited both microglial and astrocyte activation in the lumbar (L) 4-5 dorsal horn and significantly decreased levels of p-p38MAPK, p-ERK and p-JNK, which are known to be activated in microglia (p-p38MAPK and p-ERK) and astrocyte (p-JNK). Experiments using primary cell cultures also revealed that the JAK2/STAT3 pathway promoted microglia and astrocyte activation after lipopolysaccharide stimulation. Furthermore, JAK2/STAT3 signaling and pain behaviors were significantly attenuated when the rats were treated with anti-IL-6 antibody. Finally, minocycline, a tetracycline antibiotic, inhibited IL-6/JAK2/STAT3 signaling pathway in activated glial cells and restored nociceptive thresholds and the hyperresponsiveness of dorsal neurons. These results suggest an important role of the IL-6/JAK2/STAT3 pathway in the activation of microglia and astrocytes and in the maintenance of chronic below-level pain after SCI.