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.

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.

Predicting response to anti-EGFR antibody, cetuximab, therapy by monitoring receptor internalization and degradation.

Anti-epidermal growth factor receptor (EGFR) antibody, cetuximab, therapy has significantly improved the clinical outcomes of patients with colorectal cancer, but the response to cetuximab can vary widely among individuals. We thus need strategies for predicting the response to this therapy. However, the current methods are unsatisfactory in their predictive power. Cetuximab can promote the internalization and degradation of EGFR, and its therapeutic efficacy is significantly correlated with the degree of EGFR degradation. Here, we present a new approach to predict the response to anti-EGFR therapy, cetuximab by evaluating the degree of EGFR internalization and degradation of colorectal cancer cells in vitro and in vivo. Our newly developed fluorogenic cetuximab-conjugated probe (Cetux-probe) was confirmed to undergo EGFR binding, internalization, and lysosomal degradation to yield fluorescence activation; it thus shares the action mechanism by which cetuximab exerts its anti-tumor effects. Cetux-probe-activated fluorescence could be used to gauge EGFR degradation and showed a strong linear correlation with the cytotoxicity of cetuximab in colorectal cancer cells and tumor-bearing mice. The predictive ability of Cetux-probe-activated fluorescence was much higher than those of EGFR expression or KRAS mutation status. The Cetux-probes may become useful tools for predicting the response to cetuximab therapy by assessing EGFR degradation.

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.

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.

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.

Single-lead ECG based autonomic nervous system assessment for meditation monitoring.

We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by breathing is a dominant component of HRV, but its frequency depends on an individual's breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p < 0.000005) and decreased (p < 0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity.

Targeted thrombolysis by magnetoacoustic particles in photothrombotic stroke model.

BACKGROUND: Recombinant tissue plasminogen activator (rtPA) has a short half-life, and additional hemorrhagic transformation (HT) can occur when treatment is delayed. Here, we report the design and thrombolytic performance of 3 [Formula: see text]m discoidal polymeric particles loaded with rtPA and superparamagnetic iron oxide nanoparticles (SPIONs), referred to as rmDPPs, to address the HT issues of rtPA. METHODS: The rmDPPs consisted of a biodegradable polymeric matrix, rtPA, and SPIONs and were synthesized via a top-down fabrication. RESULTS: The rmDPPs could be concentrated at the target site with magnetic attraction, and then the rtPA could be released under acoustic stimulus. Therefore, we named that the particles had magnetoacoustic properties. For the in vitro blood clot lysis, the rmDPPs with magnetoacoustic stimuli could not enhance the lytic potential compared to the rmDPPs without stimulation. Furthermore, although the reduction of the infarcts in vivo was observed along with the magnetoacoustic stimuli in the rmDPPs, more enhancement was not achieved in comparison with the rtPA. A notable advantage of rmDPPs was shown in delayed administration of rmDPPs at poststroke. The late treatment of rmDPPs with magnetoacoustic stimuli could reduce the infarcts and lead to no additional HT issues, while rtPA alone could not show any favorable prognosis. CONCLUSION: The rmDPPs may be advantageous in delayed treatment of thrombotic patients.

Cavitation-assisted sonothrombolysis by asymmetrical nanostars for accelerated thrombolysis.

Sonothrombolysis with recombinant tissue plasminogen activator (rtPA) and microbubbles has been widely studied to enhance thrombolytic potential. Here, we report different sonothrombolysis strategy in nanoparticles using microbubbles cavitation. We found that different particles in shape exhibited different reactivity toward the cavitation, leading to a distinct sonothrombolytic potential. Two different gold nanoparticles in shape were functionalized with the rtPA: rtPA-functionalized gold nanospheres (NPt) and gold nanostars (NSt). NPt could not accelerate the thrombolytic potential with a sole acoustic stimulus. Importantly, NSt enhanced the potential with acoustic stimulus and microbubble-mediated cavitation, while NPt were not reactive to cavitation. Coadministration of NSt and microbubbles resulted in a dramatic reduction of the infarcts in a photothrombotic model and recovery in the cerebral blood flow. Given the synergistic effect and in vivo feasibility of this strategy, cavitation-assisted sonothrombolysis by asymmetrical NSt might be useful for treating acute ischemic stroke.

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.

Adaptive self-healing electronic epineurium for chronic bidirectional neural interfaces.

Realizing a clinical-grade electronic medicine for peripheral nerve disorders is challenging owing to the lack of rational material design that mimics the dynamic mechanical nature of peripheral nerves. Electronic medicine should be soft and stretchable, to feasibly allow autonomous mechanical nerve adaptation. Herein, we report a new type of neural interface platform, an adaptive self-healing electronic epineurium (A-SEE), which can form compressive stress-free and strain-insensitive electronics-nerve interfaces and enable facile biofluid-resistant self-locking owing to dynamic stress relaxation and water-proof self-bonding properties of intrinsically stretchable and self-healable insulating/conducting materials, respectively. Specifically, the A-SEE does not need to be sutured or glued when implanted, thereby significantly reducing complexity and the operation time of microneurosurgery. In addition, the autonomous mechanical adaptability of the A-SEE to peripheral nerves can significantly reduce the mechanical mismatch at electronics-nerve interfaces, which minimizes nerve compression-induced immune responses and device failure. Though a small amount of Ag leaked from the A-SEE is observed in vivo (17.03 ppm after 32 weeks of implantation), we successfully achieved a bidirectional neural signal recording and stimulation in a rat sciatic nerve model for 14 weeks. In view of our materials strategy and in vivo feasibility, the mechanically adaptive self-healing neural interface would be considered a new implantable platform for a wide range application of electronic medicine for neurological disorders in the human nervous system.

Ultrasound Stimulation Increases Neurite Regeneration in Injured Dorsal Root Ganglion Neurons through Mammalian Target of Rapamycin Activation.

Ultrasound stimulation (US) is reported to be a safe and useful technology for improving injured nerve regeneration. However, the intracellular mechanisms underlying its stimulatory effects are only partially understood. Mammalian target of rapamycin (mTOR) signaling is involved in neuronal survival and axonal outgrowth. In this study, we investigated the effect of US on regeneration of injured dorsal root ganglion (DRG) neurons and activation of the mTOR pathway. We showed that US significantly increased neurite regeneration and enhanced mTOR activation. Moreover, the expression of growth-associated protein-43 (GAP-43), a crucial factor for axonal outgrowth and regeneration in neurons, was significantly increased by US. These data suggest that US-induced neurite regeneration is mediated by upregulation of mTOR activity, which promotes the regeneration of injured DRG neurons.

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.

Ultrasonic Neuromodulation via Astrocytic TRPA1.

Low-intensity, low-frequency ultrasound (LILFU) is the next-generation, non-invasive brain stimulation technology for treating various neurological and psychiatric disorders. However, the underlying cellular and molecular mechanism of LILFU-induced neuromodulation has remained unknown. Here, we report that LILFU-induced neuromodulation is initiated by opening of TRPA1 channels in astrocytes. The Ca2+ entry through TRPA1 causes a release of gliotransmitters including glutamate through Best1 channels in astrocytes. The released glutamate activates NMDA receptors in neighboring neurons to elicit action potential firing. Our results reveal an unprecedented mechanism of LILFU-induced neuromodulation, involving TRPA1 as a unique sensor for LILFU and glutamate-releasing Best1 as a mediator of glia-neuron interaction. These discoveries should prove to be useful for optimization of human brain stimulation and ultrasonogenetic manipulations of TRPA1.

Inhibition of COX-2 alleviates lumbar spinal stenosis-induced chronic mechanical allodynia in rats.

Chronic low back pain due to lumbar spinal stenosis (LSS) is common, costly, mechanistically complex, and clinically challenging. However, the factors and mechanisms causing and mediating chronic pain induced by cauda equina compression remain unclear. Here, we examined the role of cyclooxygenase (COX)-2 in infiltrated macrophages, a key mediator of inflammation, in chronic neuropathic pain by LSS using an animal model. LSS was induced in adult male rats by cauda equina compression procedure using a silicone block within the epidural spaces of L5-L6 vertebrae. Locomotor deficit was observed after compression and mechanical allodynia was developed progressively for 4 weeks after injury. A number of macrophage were also infiltrated into the spinal parenchyma and cauda equina and COX-2 was expressed in infiltrated macrophages at 28 days after cauda equina compression. The administration of COX-2 inhibitors, celecoxib and MPO-0029, significantly alleviated LSS-induced chronic mechanical allodynia and inhibited the mRNA expression of inflammatory mediators such as tnf-α, Il-1ß, il-6, and inos. Furthermore, COX-2 inhibitors significantly reduced prostaglandin E2 production. These results demonstrated the role of COX-2 in LSS-induced chronic neuropathic pain and suggest that the regulation of COX-2 can be considered as a therapeutic target to relive neuropathic pain.

An Ultrastretchable and Self-Healable Nanocomposite Conductor Enabled by Autonomously Percolative Electrical Pathways.

Both self-healable conductors and stretchable conductors have been previously reported. However, it is still difficult to simultaneously achieve high stretchability, high conductivity, and self-healability. Here, we observed an intriguing phenomenon, termed "electrical self-boosting", which enables reconstructing of electrically percolative pathways in an ultrastretchable and self-healable nanocomposite conductor (over 1700% strain). The autonomously reconstructed percolative pathways were directly verified by using microcomputed tomography and in situ scanning electron microscopy. The encapsulated nanocomposite conductor shows exceptional conductivity (average value: 2578 S cm-1; highest value: 3086 S cm-1) at 3500% tensile strain by virtue of efficient strain energy dissipation of the self-healing polymer and self-alignment and rearrangement of silver flakes surrounded by spontaneously formed silver nanoparticles and their self-assembly in the strained self-healing polymer matrix. In addition, the conductor maintains high conductivity and stretchability even after recovered from a complete cut. Besides, a design of double-layered conductor enabled by the self-bonding assembly allowed a conducting interface to be located on the neutral mechanical plane, showing extremely durable operations in a cyclic stretching test. Finally, we successfully demonstrated that electromyogram signals can be monitored by our self-healable interconnects. Such information was transmitted to a prosthetic robot to control various hand motions for robust interactive human-robot interfaces.

Hemodynamic correlation imaging of the mouse brain for application in unilateral neurodegenerative diseases.

We developed a single-camera two-channel hemodynamic imaging system that uses near-infrared light to monitor the mouse brain in vivo with an exposed, un-thinned, and intact skull to explore the effect of Parkinson's disease on the resting state functional connectivity of the brain. To demonstrate our system's ability to monitor cerebral hemodynamics, we first performed direct electrical stimulation of an anesthetized healthy mouse brain and detected hemodynamic changes localized to the stimulated area. Subsequently, we developed a unilaterally lesioned 6-hydroxydopamine (hemi-parkinsonian) mouse model and detected the differences in functional connectivity between the normal and hemi-parkinsonian mouse brains by comparing the hemispheric hemodynamic correlations during the resting state. Seed-based correlation for the oxy-hemoglobin channel from the left and right hemispheres of healthy mice was much higher and more symmetric than in hemi-parkinsonian mice. Through a k-means clustering of the hemodynamic signals, the healthy mouse brains were segmented according to brain region, but the hemi-parkinsonian mice did not show a similar segmentation. Overall, this study highlights the development of a spatial multiplexing hemodynamic imaging system that reveals the resting state hemodynamic connectivity in healthy and hemi-parkinsonian mice.

Protocatechuic acid improves functional recovery after spinal cord injury by attenuating blood-spinal cord barrier disruption and hemorrhage in rats.

After spinal cord injury (SCI), blood-spinal cord barrier (BSCB) disruption and hemorrhage lead to blood cell infiltration and progressive secondary injuries including inflammation. Inflammatory response is one of the major events resulting in apoptosis, scar formation and neuronal dysfunction after SCI. Here, we investigated whether protocatechuic acid (PCA), a natural phenolic compound, would attenuate BSCB disruption and hemorrhage, leading to functional improvement after SCI. After a moderate contusion injury at T9, PCA (50 mg/kg) was administrated via intraperitoneal injection immediately, 6 h, and 12 h after SCI, and the same dose of PCA once a day until 7 d after injury. Our data show that PCA inhibited apoptotic cell death of neurons and oligodendrocytes and improved functional recovery after injury. PCA also attenuated BSCB disruption and hemorrhage and reduced the infiltration of neutrophils and macrophages compared to vehicle control. Moreover, PCA inhibited the expression and activation of matrix metalloprotease-9, which is well known to disrupt BSCB after SCI. Furthermore, PCA treatment significantly inhibited the expression of sulfonylurea receptor 1 and transient receptor potential melastatin 4, which are known to mediate hemorrhage at an early stage after SCI. Consistent with these findings, the mRNA and protein expression of inflammatory mediators such as tumor necrosis factor alpha, interleukin 1 beta, cyclooxygenase-2, inducible nitric oxide synthase, and chemokines was significantly alleviated by PCA treatment. Thus, our results suggest that PCA improved functional recovery after SCI in part by inhibiting BSCB disruption and hemorrhage through the down-regulation of sulfonylurea receptor 1/transient receptor potential melastatin 4 and matrix metalloprotease-9.