Finite element analysis of a low modulus Ti-20Zr-3Mo-3Sn alloy designed to reduce the stress shielding effect of a hip prosthesis.

After total hip arthroplasty, the stress shielding effect can occur due to the difference of stiffness between the metallic alloy of the stems and the host bone, which may cause a proximal bone loss. To overcome this problem, a low-modulus metastable ß Ti-20Zr-3Mo-3Sn alloy composition has recently been designed to be potentially used for the cementless femoral hip stems. After having verified experimentally that the ß alloy has a low modulus of around 50 GPa, a finite element analysis was performed on a Ti-20Zr-3Mo-3Sn alloy hip prosthesis model to evaluate the influence of a reduced modulus on stress shielding and stress fields in both stem and bone compared with the medical grade Ti-6Al-4V alloy whose elastic modulus reached 110 GPa. Our results show that the Ti-20Zr-3Mo-3Sn stem with low elastic modulus can effectively reduce the total stress shielding by 45.5% compared to the common Ti-6Al-4V prosthesis. Moreover, it is highlighted that the material elasticity affects the stress distribution in the implant, especially near the bone-stem interfaces.

Enhancing Brain-Computer Interface Performance by Incorporating Brain-to-Brain Coupling.

Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human-robot interaction may benefit from integration with human-human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain-computer interface (BCI) training using eye-contact and hand-touch interaction. Twelve pairs of friends (experimental group) and 10 pairs of strangers (control group) were recruited for MI-based BCI tests concurrent with electroencephalography (EEG) hyperscanning. Event-related desynchronization (ERD) was estimated to measure cortical activation, and interbrain functional connectivity was assessed using multilevel statistical analysis. Furthermore, we compared BCI classification performance under different social interaction conditions. In the experimental group, greater ERD was found around the contralateral sensorimotor cortex under social interaction conditions compared with MI without any social interaction. Notably, EEG channels with decreased power were mainly distributed around the frontal, central, and occipital regions. A significant increase in interbrain coupling was also found under social interaction conditions. BCI decoding accuracies were significantly improved in the eye contact condition and eye and hand contact condition compared with the no-interaction condition. However, for the strangers' group, no positive effects were observed in comparisons of cortical activations between interaction and no-interaction conditions. These findings indicate that social interaction can improve the neural synchronization between familiar partners with enhanced brain activations and brain-to-brain coupling. This study may provide a novel method for enhancing MI-based BCI performance in conjunction with neural synchronization between users.

Finite element analysis of the mechanical performance of self-expanding endovascular stents made with new nickel-free superelastic ß-titanium alloys.

New Ni-free superelastic ß-titanium alloys from the Ti-Zr-Nb-Sn system have been designed in this study to replace the NiTi alloy currently used for self-expanding endovascular stents. The simulation results, carried out by finite element analysis (FEA) on two ß-type Ti-Zr-Nb-Sn alloys using a commonly used superelastic constitutive model, were in good agreement with the experimental uniaxial tension data. An ad-hoc self-expanding coronary stent was specifically designed for the present study. To assess the mechanical performance of the endovascular stents, a FEA framework of the stent deployed in the arterial system was established, and a simply cyclic bending loading was proposed. Six comparative simulations of three superelastic materials (including NiTi for comparison) and two arterial configurations were successfully conducted. The mechanical behaviours of the stents were analysed through stress localization, the increase in artery diameter, contact results, and distributions of mean and alternating strain. The simulation results show that the Ti-22Zr-11Nb-2Sn (at. %) alloy composition for the stent produces the largest contact area (9.92 mm2) and radial contact force (49.5 mN) on the inner surface of the plaque and a higher increase in the stenotic artery diameter (70 %) after three vascular bending cycles. Furthermore, the Ti-22Zr-11Nb-2Sn stent exhibited sufficient crimping capacity and reliable mechanical performance during deployment and cyclic bending, which could make it a suitable choice for self-expanding coronary stents. In this work, the implementation of finite element analysis has thus made it possible to propose a solid basis for the mechanical evaluation of these stents fabricated in new Ni-free superelastic ß-Ti alloys.

Perfluorooctane sulfonate (PFOS) induces apoptosis and autophagy by inhibition of PI3K/AKT/mTOR pathway in human granulosa cell line KGN.

Perfluorooctane sulfonate (PFOS) is recognized as an environmental endocrine disruptor with widespread use in industrial manufacturing and daily life, contributing to various public health concerns. However, the precise impacts of PFOS on the ovary and its regulatory mechanisms remain unclear. This study aims to delineate the ovarian toxicity of PFOS and scrutinize its effects on apoptosis and autophagy through modulation of the PI3K/AKT/mTOR pathway in the human granulosa cell line (KGN). Cell viability, assessed via the Cell Counting Kit-8 (CCK8), revealed a dose-dependent reduction in cell viability upon PFOS exposure. Flow cytometry analysis demonstrated an elevated proportion of apoptotic cells following PFOS treatment. Western blot analyses unveiled increased expression of Bax, Cyt c, cleaved caspase-9, and LC3-II/I, coupled with decreased expression of Bcl-2 and p62. Transmission electron microscopy (TEM) observations illustrated a heightened number of autophagosomes induced by PFOS. Molecular docking investigations, in conjunction with Western blot experiments, substantiated PFOS's significant inhibition of the PI3K/AKT/mTOR signaling pathway. These findings collectively underscore that PFOS induces apoptosis and autophagy in KGN cells through modulation of the PI3K/AKT/mTOR pathway, providing experimental evidence for PFOS-induced ovarian toxicity and elucidating the underlying regulatory mechanisms in KGN cells.

Characterization and drivers of water and carbon fluxes dynamics in dune ecosystems of the Horqin Sandy Land.

Sandy regions constitute pivotal components of terrestrial ecosystems, exerting significant influences on global ecological equilibrium and security. This study meticulously explored water and carbon fluxes dynamics within a dune ecosystem in the Horqin Sandy Land throughout the growing seasons from 2013 to 2022 by employing an advanced eddy covariance system. The dynamic characteristics of these fluxes and their underlying driving forces were extensively analyzed, with a particular focus on the impact of precipitation. The main results are as follows: (1) During the growing seasons of 2015 and 2016, the dune ecosystem acted as a modest carbon source, while in 2013, 2014, and 2017- 2022, it transformed into a net carbon sink. Notably, the annual mean values of water use efficiency (WUE) and evapotranspiration (ET) were 5.16 gC·kg-1H2O and 255.4 mm, respectively. (2) The intensity, frequency, and temporal distribution of precipitation were found to significantly influence the carbon and water fluxes dynamics. Isolated minor precipitation events did not trigger substantial fluctuations, but substantial and prolonged precipitation events spanning multiple days or consecutive minor precipitation events resulted in notable assimilation delays. (3) Air temperature, soil temperature, and fractional vegetation cover (FVC) were found to be key factors influencing the carbon and water fluxes. Specifically, FVC exhibited a negative logarithmic correlation with net ecosystem CO2 exchange (NEE) and a power function relationship with WUE. (4) The interaction between carbon and water fluxes is exhibited by exponential increases in ecosystem respiration (Reco) and gross primary productivity (GPP) with WUE, while NEE displayed an exponential decrease in relation to WUE. These findings are of high significance in predicting the potential ramifications of climate change on the intricate carbon and water cycles, and enhance our understanding of ecosystem dynamics in sandy environments.

Multiscale Canonical Coherence for Functional Corticomuscular Coupling Analysis.

Functional corticomuscular coupling (FCMC) probes multi-level information communication in the sensorimotor system. The canonical Coherence (caCOH) method has been leveraged to measure the FCMC between two multivariate signals within the single scale. In this paper, we propose the concept of multiscale canonical Coherence (MS-caCOH) to disentangle complex multi-layer information and extract coupling features in multivariate spaces from multiple scales. Then, we verified the reliability and effectiveness of MS-caCOH on two types of data sets, i.e., a synthetic multivariate data set and a real-world multivariate data set. Our simulation results showed that compared with caCOH, MS-caCOH enhanced coupling detection and achieved lower pattern recovery errors at multiple frequency scales. Further analysis on experimental data demonstrated that the proposed MS-caCOH method could also capture detailed multiscale spatial-frequency characteristics. This study leverages the multiscale analysis framework and multivariate method to give a new insight into corticomuscular coupling analysis.

Motor-Respiratory Coupling Improves Endurance Performance during Rhythmic Isometric Handgrip Exercise.

PURPOSE: This study aimed to evaluate whether motor-respiratory coupling exists in rhythmic isometric handgrip exercises and its effect on endurance performance. In addition, the mechanism underlying observed effects was to be investigated if higher motor-respiratory coupling rate could enhance endurance performance. METHODS: Eleven subjects completed three rhythmic isometric handgrip trials to task failure in a randomized manner. After one pretraining session to determine personal grip frequency, one trial was performed without respiration requirement (CON), and two trials were performed with inspiration-motor coupling (IMC) or expiration-motor coupling. Changes in maximal voluntary contraction (MVC) and EMG were used to measure neuromuscular fatigue. Force data during test were used to assess exercise intensity. Another 10 subjects completed electrical stimulation-induced finger flexion and extension during normal inspiration, normal expiration, fast inspiration, fast expiration, and breath holding. Force changes of different breathing conditions were compared. RESULTS: Normalized exercise time to exhaustion was significantly longer in IMC (1.27 ± 0.23) compared with expiration-motor coupling (0.82 ± 0.18) and CON (0.91 ± 0.18, P < 0.001). ΔMVC, grip frequency, force, and EMG indices were not different among conditions (all P > 0.05). Electrical stimulation-induced finger extensor force was significant higher during fast inspiration (1.11 ± 0.09) than normal respiration (1.00 ± 0.05) and fast expiration (0.94 ± 0.08, P < 0.05). CONCLUSIONS: IMC is an effective way to improve endurance performance of rhythmic handgrip exercise. This is likely due to a reduction in the energy consumption of motion control, as evidenced by similar peripheral fatigue in different conditions and modulation of corticospinal excitability by respiration.

Enhancement of EEG-EMG coupling detection using corticomuscular coherence with spatial-temporal optimization.

Objective. Corticomuscular coherence (CMC) is widely used to detect and quantify the coupling between motor cortex and effector muscles. It is promisingly used in human-machine interaction (HMI) supported rehabilitation training to promote the closed-loop motor control for stroke patients. However, suffering from weak coherence features and low accuracy in contingent neurofeedback, its application to HMI rehabilitation robots is currently limited. In this paper, we propose the concept of spatial-temporal CMC (STCMC), which is the coherence by refining CMC with delay compensation and spatial optimization.Approach. The proposed STCMC method measures the coherence between electroencephalogram (EEG) and electromyogram (EMG) in the multivariate spaces. Specifically, we combined delay compensation and spatial optimization to maximize the absolute value of the coherence. Then, we tested the reliability and effectiveness of STCMC on neurophysiological data of force tracking tasks.Main results. Compared with CMC, STCMC not only enhanced the coherence significantly between brain and muscle signals, but also produced higher classification accuracy. Further analysis showed that temporal and spatial parameters estimated by the STCMC reflected more detailed brain topographical patterns, which emphasized the different roles between the contralateral and ipsilateral hemisphere.Significance. This study integrates delay compensation and spatial optimization to give a new perspective for corticomuscular coupling analysis. It is also feasible to design robotic neurorehabilitation paradigms by the proposed method.

Effect of PFOA exposure on diminished ovarian reserve and its metabolism.

Owing to its difficulty in degrading and ease of accumulation in the body, perfluorooctanoic acid (PFOA) has a detrimental effect on reproduction. This study aimed to examine the effect of PFOA concentration in follicular fluid during ovulation stimulation on embryo quality and the impact of PFOA exposure on the metabolic components of follicular fluid. This was a single-center prospective study that included 25 patients with diminished ovarian reserve (DOR), 25 with normal ovarian reserve (NOR), and 25 with polycystic ovary syndrome (PCOS). Follicular fluid samples were analyzed using ultra-high performance liquid chromatography-tandem mass spectrometry. We demonstrated that the PFOA levels of follicular fluid in the DOR group were higher than those in the NOR group and PCOS group (P < 0.05). PFOA concentration in the PCOS group was negatively correlated with high-quality embryos (P < 0.05). To gain more insight into the impact of PFOA on the metabolic composition of follicular fluid, we classified the DOR group based on the PFOA concentration, for which metabolomic analysis was performed. In the high-concentration PFOA group, there was an increase and a decrease in three and nine metabolites, respectively, compared to that in the low-concentration group. These results suggest that PFOA may alter the metabolic composition of follicular fluid, thus, affecting ovarian reserve function.

Tailoring brain-machine interface rehabilitation training based on neural reorganization: towards personalized treatment for stroke patients.

Electroencephalogram (EEG)-based brain-machine interface (BMI) has the potential to enhance rehabilitation training efficiency, but it still remains elusive regarding how to design BMI training for heterogeneous stroke patients with varied neural reorganization. Here, we hypothesize that tailoring BMI training according to different patterns of neural reorganization can contribute to a personalized rehabilitation trajectory. Thirteen stroke patients were recruited in a 2-week personalized BMI training experiment. Clinical and behavioral measurements, as well as cortical and muscular activities, were assessed before and after training. Following treatment, significant improvements were found in motor function assessment. Three types of brain activation patterns were identified during BMI tasks, namely, bilateral widespread activation, ipsilesional focusing activation, and contralesional recruitment activation. Patients with either ipsilesional dominance or contralesional dominance can achieve recovery through personalized BMI training. Results indicate that personalized BMI training tends to connect the potentially reorganized brain areas with event-contingent proprioceptive feedback. It can also be inferred that personalization plays an important role in establishing the sensorimotor loop in BMI training. With further understanding of neural rehabilitation mechanisms, personalized treatment strategy is a promising way to improve the rehabilitation efficacy and promote the clinical use of rehabilitation robots and other neurotechnologies.

The follicular fluid metabolome in infertile individuals between polycystic ovary syndrome and diminished ovarian reserve.

Follicular fluid is the microenvironment of oocytes that plays a crucial role in oocyte development. This study intended to explore the follicular fluid metabolomics in diminished ovarian reserve (DOR), polycystic ovarian syndrome (PCOS), and normal ovary response (NOR) groups. For metabolomic analysis, we collected the follicular fluid samples from 28 patients with DOR, 28 patients with NOR, and 28 patients with PCOS. The identified metabolites were annotated using KEGG to determine the metabolic pathway disturbances in PCOS and DOR. Based on the regression model, we conducted ROC analysis to identify PCOS and DOR biomarkers in the follicular fluid. The present results identified that the DOR and NOR groups' differential metabolites were primarily enriched in the choline pathway. The concentrations of pregnanediol-3-glucuronide and 2-hydroxyestrone sulfate in the DOR and NOR groups were substantially different. The metabolites in the purine metabolism pathway were mainly enriched in the PCOS and NOR groups. N-Acetyl-S-(N-methylcarbamoyl) cysteine and 3,4-dehydrothiomorpholine in the PCOS and NOR groups were substantially different. We also identified metabolic alterations in PCOS and DOR follicular fluid, which provides novel ways for PCOS and DOR diagnosis and therapy.

Recognizing the individualized sensorimotor loop of stroke patients during BMI-supported rehabilitation training based on brain functional connectivity analysis.

BACKGROUND: Electroencephalogram (EEG) based brain-machine interaction training can facilitate rehabilitation by closing the sensorimotor loop. However, it remains unclear how to evaluate whether the loop is closed, especially for stroke patients whose brain regions of motor control and sensorimotor feedback could be altered. Our hypothesis is that motor recovery depends on whether sensorimotor loop is established poststroke. This study aims to explore how to evaluate the establishment of sensorimotor loop based on the evolving neural reorganization patterns after stroke. NEW METHOD: 14 stroke patients participated in the experiment and EEG were recorded during three specific tasks: Movement Imagery (MI), Passive Movement (PM) and Movement Execution (ME). Activated brain regions correlated with movement intention expression and sensorimotor feedback were detected respectively during MI and PM. In ME, local-averaged Phase Lag Index (PLI) was analyzed to represent the functional connectivity between activated brain regions of MI and PM. RESULTS: Individualized cortical activation was found both in MI and PM. The overlapping brain activation during PM and MI did not correlate with patient's Fugl-Meyer Upper Extremity Motor Score (FMU) . However, we found that FMU of the group with higher local-averaged PLI was statistically higher than FMU of the group with lower local-averaged PLI compared with global-averaged PLI (p < 0.05). CONCLUSIONS: The findings demonstrate functional connectivity between activated brain regions of motor control and sensorimotor feedback may imply if the individualized sensorimotor loop is established poststroke. The successful formation of the closed loop can indicate stroke patients' motor recovery.

5 Hz rTMS improves motor-imagery based BCI classification performance.

Brain-computer interface (BCI) based rehabilitation has been proven a promising method facilitating motor recovery. Recognizing motor intention is crucial for realizing BCI rehabilitation training. Event-related desynchronization (ERD) is a kind of electroencephalogram (EEG) inherent characteristics associated with motor intention. However, due to brain deficits poststroke, some patients are not able to generate ERD, which discourages them to be involved in BCI rehabilitation training. To boost ERD during motor imagery (MI), this paper investigates the effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) on BCI classification performance. Eleven subjects participated in this study. The experiment consisted of two conditions: rTMS + MI versus sham rTMS + MI, which were arranged on different days. MI tests with 64-channel EEG recording were arranged immediately before and after rTMS and sham rTMS. Time-frequency analysis were utilized to measure ERD changes. Common spatial pattern was used to extract features and linear discriminant analysis was used to calculate offline classification accuracies. Paired-sample t-test and Wilcoxon signed rank tests with post-hoc analysis were used to compare performance before and after stimulation. Statistically stronger ERD (-13.93±12.99%) was found after real rTMS compared with ERD (-5.71±21.25%) before real rTMS (p<0.05). Classification accuracy after real rTMS (70.71±10.32%) tended to be higher than that before real rTMS (66.50±8.48%) (p<0.1). However, no statistical differences were found after sham stimulation. This research provides an effective method in improving BCI performance by utilizing neural modulation.Clinical Relevance- This study offers a promising treatment for patients who cannot be recruited in BCI rehabilitation training due to poor BCI classification performance.

CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training.

Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient's neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of prescribing BCI training accordingly. A Convolution Neural Network (CNN) based prognosis model was developed to predict the outcome of 11 stroke patients' recovery following a 2-week rehabilitation training with BCI. In our study, functional connectivity and power spectrum have been evaluated and applied as the inputs of CNN to regress patients' recovery rate. A saliency map was used to identify the correlation between EEG channels with the recovery outcome. The performance of our model was assessed using the leave-one-out cross-validation. Overall, the proposed model predicted patients' recovery with R2 0.98 and MSE 0.89. According to the saliency map, the highest functional connectivity occurred in Fp2/Fpz-AF8, Fp2/F4/F8-P3, P1/PO7-PO5 and AF3-AF4. Our results demonstrated that deep learning method has the potential to predict the recovery rate of BCI training, which contributes to guiding individualized prescription in the early stage of clinical rehabilitation.

Development and Preliminary Validation of a Pneumatic Focal Vibration System to the Mitigation of Post-Stroke Spasticity.

Some evidence has demonstrated that focal vibration (FV) plays an important role in the mitigation of spasticity. However, the research on developing the FV system to mitigate the spasticity effectively has been seldom reported. To relieve post-stroke spasticity, a new pneumatic FV system has been proposed in this paper. An image processing approach, in which the edge of vibration actuator was identified by the Canny edge detector, was utilized to quantify this system's parameters: the frequency ranging from 44 Hz to 128 Hz and the corresponding amplitude. Taking one FV protocol with the frequency of 87 Hz and the amplitude 0.28 mm of this system as an example, a clinical experiment was carried out. In the clinical experiment, FV was applied over the muscle belly of the antagonist of spastic muscle for twelve chronic spastic stroke patients. Spasticity was quantified by the muscle compliance and area under the curve for muscle (AUC_muscle). The result has demonstrated that, in the state of flexion of spastic muscle, the AUC_muscle and muscle compliance of the spastic muscle significantly increased immediately after FV compared with before-FV, illustrating the mitigation of the spasticity. This study will not only provide a potential tool to relieve post-stroke spasticity, but also contribute to improving the sensory and motor function of patients with other neurological diseases, e.g. spinal cord injury, multiple sclerosis, Parkinson and dystonia, etc.

Denoising Algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction.

BACKGROUND: Rehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients' recovery by closing the loop between motor intention and actual movement. The main challenge is to identify the patient's motor intention based on large training datasets with noise contamination in the Electroencephalogram (EEG) signal. NEW METHOD: To address this problem, this paper proposed a self-adaptively denoised Event-Related Desynchronization (ERD)-based motor intention recognition algorithm (DeERD) in order to enable BMI training with a small sample of calibration data. This study recruited 8 stroke patients. Each patient was required to execute paralyzed upper-limb motor attempt for 20 trials and remain in resting state for 20 trials randomly. ERD-based motor intention recognition algorithm, Common spatial filter algorithm (CSP) and Directed Transfer Function analysis (DTF) were used to extract features for classification respectively and compared with the proposed DeERD analysis. RESULTS: DeERD can filter the noise and extract the average lines as the principal trends. With denoising processing, Accuracy (ACC) was up to 70% for all 8 patients and they could be included in this BMI system effectively. COMPARISON WITH EXISTING METHODS: The proposed DeERD model generated statistically significant increase in True Positive Rate (TPR) and in ACC than the DTF model. TPR and ACC standard deviation of DeERD was smaller than that of CSP. CONCLUSIONS: The proposed DeERD model can eliminate the principal noise and extract the principal trend of the time-frequency analysis. It provides a practical method to recruit more stroke patients into BMI training with fewer calibration trainings.

Impact of smart force feedback rehabilitation robot training on upper limb motor function in the subacute stage of stroke.

OBJECTIVE: To explore the impact of rehabilitation robot training (RRT) on upper limb motor function and daily activity ability in patients with stroke. METHODS: Forty patients meeting the inclusion criteria were randomly divided into the treatment group (TRE) and the control group (CON). Group TRE was trained with an upper limb rehabilitation robot and group CON was trained with traditional occupational therapy. The training time was six weeks, and the upper limb function and daily activities were then assessed. RESULTS: (1) There was no statistical significance in the Fugl-Meyer (FM) score, Wolf Motor Function Test (WMFT) score, and Modified Barthel Index (MBI) score between the two groups before treatment (P > 0.05). (2) After treatment, the FM score, WMFT score, and MBI score were significantly higher than before treatment (P < 0.01). (3) There was no significant significance between the two groups after treatment (P > 0.05). CONCLUSIONS: Both RRT and traditional occupational therapy training are useful for the recovery of upper limb motor function and daily life ability in the sub-acute stage of stroke.

Brain-Computer Interface Channel-Selection Strategy Based on Analysis of Event-Related Desynchronization Topography in Stroke Patients.

In the last decade, technology-assisted stroke rehabilitation has been the focus of research. Electroencephalogram- (EEG-) based brain-computer interface (BCI) has a great potential for motor rehabilitation in stroke patients since the closed loop between motor intention and the actual movement established by BCI can stimulate the neural pathways of motor control. Due to the deficits in the brain, motor intention expression may shift to other brain regions during and even after neural reorganization. The objective of this paper was to study the event-related desynchronization (ERD) topography during motor attempt tasks of the paretic hand in stroke patients and compare the classification performance using different channel-selection strategies in EEG-based BCI. Fifteen stroke patients were recruited in this study. A cue-based experimental paradigm was applied in the experiment, in which each patient was required to open the palm of the paretic or the unaffected hand. EEG was recorded and analyzed to measure the motor intention and indicate the activated brain regions. Support vector machine (SVM) combined with common spatial pattern (CSP) algorithm was used to calculate the offline classification accuracy between the motor attempt of the paretic hand and the resting state applying different channel-selection strategies. Results showed individualized ERD topography during the motor attempt of the paretic hand due to the deficits caused by stroke. Statistical analysis showed a significant increase in the classification accuracy by analyzing the channels showing ERD than analyzing the channels from the contralateral sensorimotor cortex (SM1). The results indicated that for stroke patients whose affected motor cortex is extensively damaged, the compensated brain regions should be considered for implementing EEG-based BCI for motor rehabilitation as the closed loop between the altered activated brain regions and the paretic hand can be stimulated more accurately using the individualized channel-selection strategy.

Quercetin relieved diabetic neuropathic pain by inhibiting upregulated P2X4 receptor in dorsal root ganglia.

The upregulation of nociceptive ion channels expressed in dorsal root ganglia (DRG) contributes to the development and retaining of diabetic pain symptoms. The flavonoid quercetin (3,3',4',5,7-pentahydroxyflavone) is a component extracted from various fruits and vegetables and exerts anti-inflammatory, analgesic, anticarcinogenic, antiulcer, and antihypertensive effects. However, the exact mechanism underlying quercetin's analgesic action remains poorly understood. The aim of this study was to investigate the effects of quercetin on diabetic neuropathic pain related to the P2X4 receptor in the DRG of type 2 diabetic rat model. Our data showed that both mechanical withdrawal threshold and thermal withdrawal latency in diabetic rats treated with quercetin were higher compared with those in untreated diabetic rats. The expression levels of P2X4 messenger RNA and protein in the DRG of diabetic rats were increased compared with the control rats, while quercetin treatment significantly inhibited such enhanced P2X4 expression in diabetic rats. The satellite glial cells (SGCs) enwrap the neuronal soma in the DRG. Quercetin treatment also lowered the elevated coexpression of P2X4 and glial fibrillary acidic protein (a marker of SGCs) and decreased the upregulation of phosphorylated p38 mitogen-activated protein kinase (p38MAPK) in the DRG of diabetic rats. Quercetin significantly reduced the P2X4 agonist adenosine triphosphate-activated currents in HEK293 cells transfected with P2X4 receptors. Thus, our data demonstrate that quercetin may decrease the upregulation of the P2X4 receptor in DRG SGCs, and consequently inhibit P2X4 receptor-mediated p38MAPK activation to relieve the mechanical and thermal hyperalgesia in diabetic rats.