Comorbid anxiety and depressive symptoms in chronic pain are a common health problem, but the underlying mechanisms remain unclear. Previously, we have demonstrated that sensitization of the CeA neurons via decreased GABAergic inhibition contributes to anxiety-like behaviors in neuropathic pain rats. In this study, by using male Sprague Dawley rats, we reported that the CeA plays a key role in processing both sensory and negative emotional-affective components of neuropathic pain. Bilateral electrolytic lesions of CeA, but not lateral/basolateral nucleus of the amygdala (LA/BLA), abrogated both pain hypersensitivity and aversive and depressive symptoms of neuropathic rats induced by spinal nerve ligation (SNL). Moreover, SNL rats showed structural and functional neuroplasticity manifested as reduced dendritic spines on the CeA neurons and enhanced LTD at the LA/BLA-CeA synapse. Disruption of GluA2-containing AMPAR trafficking and endocytosis from synapses using synthetic peptides, either pep2-EVKI or Tat-GluA2(3Y), restored the enhanced LTD at the LA/BLA-CeA synapse, and alleviated the mechanical allodynia and comorbid aversive and depressive symptoms in neuropathic rats, indicating that the endocytosis of GluA2-containing AMPARs from synapses is probably involved in the LTD at the LA/BLA-CeA synapse and the comorbid aversive and depressive symptoms in neuropathic pain in SNL-operated rats. These data provide a novel mechanism for elucidating comorbid aversive and depressive symptoms in neuropathic pain and highlight that structural and functional neuroplasticity in the amygdala may be important as a promising therapeutic target for comorbid negative emotional-affective disorders in chronic pain. SIGNIFICANCE STATEMENT Several studies have demonstrated the high comorbidity of negative affective disorders in patients with chronic pain. Understanding the affective aspects related to chronic pain may facilitate the development of novel therapies for more effective management. Here, we unravel that the CeA plays a key role in processing both sensory and negative emotional-affective components of neuropathic pain, and LTD at the amygdaloid LA/BLA-CeA synapse mediated by GluA2-containing AMPAR endocytosis underlies the comorbid aversive and depressive symptoms in neuropathic pain. This study provides a novel mechanism for elucidating comorbid aversive and depressive symptoms in neuropathic pain and highlights that structural and functional neuroplasticity in the amygdala may be important as a promising therapeutic target for comorbid negative emotional-affective disorders in chronic pain.
Brain-computer interface (BCI) usually suffers from the problem of low recognition accuracy and large calibration time, especially when identifying motor imagery tasks for subjects with indistinct features and classifying fine grained motion control tasks by electroencephalogram (EEG)-electromyogram (EMG) fusion analysis. To fill the research gap, this paper presents an end-to-end semi-supervised learning framework for EEG classification and EEG–EMG fusion analysis. Benefiting from the proposed metric learning based label estimation strategy, sampling criterion and progressive learning scheme, the proposed framework efficiently extracts distinctive feature embedding from the unlabeled EEG samples and achieves a 5.40% improvement on BCI Competition IV Dataset IIa with 80% unlabeled samples and an average 3.35% improvement on two public BCI datasets. By employing synchronous EMG features as pseudo labels for the unlabeled EEG samples, the proposed framework further extracts deep level features of the synergistic complementarity between the EEG signals and EMG features based on the deep encoders, which improves the performance of hybrid BCI (with a 5.53% improvement for the Upper Limb Motion Dataset and an average 4.34% improvement on two hybrid datasets). Moreover, the ablation experiments show that the proposed framework can substantially improve the performance of the deep encoders (with an average 5.53% improvement). The proposed framework not only largely improves the performance of deep networks in the BCI system, but also significantly reduces the calibration time for EEG-EMG fusion analysis, which shows great potential for building an efficient and high-performance hybrid BCI for the motor rehabilitation process.
Rhubarb was first recorded in the earliest Chinese medicine monograph Shen Nong′s Materia Medica, with its clinical application involving internal, external, women, children, and other clinical disciplines. With the wide application of rhubarb, it is getting more and more people′s attention how to better play the curative and preventive effects of rhubarb, and prevent and reduce its toxic and side effects. This article mainly reviewed the side effects of rhubarb from several aspects such as hepatotoxicity, nephrotoxicity, enterotoxicity, and the triple effects (mutagenesis, carcinogenesis, and teratogenesis) of rhubarb, and put forward the countermeasures of attenuating toxicity and increasing efficacy, especially its reasonable application in clinical kidney diseases, so as to provide a reference for the safe clinical use of rhubarb.
Key words:
Rhubarb; Toxic and side effects; Kidney disease; Reasonable use
The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals' features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study's findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.
By means of gathering statistical data of Chinese women’s volleyball team in 6 matches in the 15th World Women's Volleyball Championship in 2006,the author carried out a comprehensive analysis on the matches in such five aspects as spike strength at positions No.2 and No.4(averagely 53.4% per match,which is 13.5% lower than that of its opponents),fast break strength(averagely 36.1% per match,which is 12.2 % higher than that of its opponents),backcourt offense strength(averagely 10.6% per match,which is 1.4 % higher than that of its opponents),service scores and misses(the service scoring rate of the Chinese team is higher than that of other opponents except 1.1 % lower that of the Russian team),and active scoring capability(the active scoring capability of the Chinese team is respectively 1.8%,6.0%,10.9% and 3.8% lower than that of the Brazilian team,Japanese team,Cuban team and German team,but 6.5% higher than that of the Dutch team),and revealed the following findings: In terms of backcourt offense,Chinese women’s volleyball team performed normally,and its strength was obviously superior to that of its opponents;however,the Chinese team had too little change of offensive tactics and too many misses,which lowered its scoring rate.The author probed into the advantages and shortages of Chinese women’s volleyball team compared with world winning teams.
Objectives The pathological diagnostic criteria for primary Sjögren’s syndrome (SjS) have certain limitations. We first explored the key pathogenic pathways of SjS through a bioinformatics approach, and then evaluated the diagnostic value of the important biomarker in SjS. Methods Transcriptome data from non-SjS controls and patients with SjS were analysed using integrated bioinformatics methods. In a case–control study, phosphorylated signal transducer and activator of transcription proteins 1 (p-STAT1), a key biomarker for the activation of interferon (IFN) pathway, was selected to evaluate its diagnostic value by immunohistochemical analyses in salivary gland (SG) tissues. Results The IFN-related pathways were aberrantly activated in patients with SjS. Positive staining of p-STAT1 was detected in the SjS group, but not in non-SjS control group. There was a significant difference in the integrated optical density values of p-STAT1 expressions between the controls and the SjS groups, as well as between the controls and the SjS lymphatic foci-negative groups (p<0.05). The area under the curve of the receiver operating characteristic curve for p-STAT1 was 0.990 (95% CI 0.969 to 1.000). There was a significant difference in both accuracy and sensitivity of p-STAT1 compared with the Focus Score (p<0.05). The Jorden index for p-STAT1 was 0.968 (95% CI 0.586 to 0.999). Conclusions The IFN pathway is the key pathogenic pathway in SjS. p-STAT1 may serve as an important biomarker, in addition to lymphocytic infiltration, to diagnose SjS. Particularly in SG samples with negative lymphatic foci, p-STAT1 confers pathological diagnostic value.
Introduction Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals. Methods This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement. Results A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%. Discussion This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.
Abstract Exacerbation of pain by chronic stress and comorbidity of pain with stress-related psychiatric disorders, including anxiety and depression, represent significant clinical challenges. However, the underlying mechanisms still remain unclear. Here, we investigated whether chronic forced swim stress (CFSS)–induced exacerbation of neuropathic pain is mediated by the integration of stress-affect–related information with nociceptive information in the central nucleus of the amygdala (CeA). We first demonstrated that CFSS indeed produces both depressive-like behaviors and exacerbation of spared nerve injury (SNI)-induced mechanical allodynia in rats. Moreover, we revealed that CFSS induces both sensitization of basolateral amygdala (BLA) neurons and augmentation of long-term potentiation (LTP) at the BLA-CeA synapse and meanwhile, exaggerates both SNI-induced sensitization of CeA neurons and LTP at the parabrachial (PB)-CeA synapse. In addition, we discovered that CFSS elevates SNI-induced functional up-regulation of GluN2B-containing NMDA (GluN2B-NMDA) receptors in the CeA, which is proved to be necessary for CFSS-induced augmentation of LTP at the PB-CeA synapse and exacerbation of pain hypersensitivity in SNI rats. Suppression of CFSS-elicited depressive-like behaviors by antidepressants imipramine or ifenprodil inhibits the CFSS-induced exacerbation of neuropathic pain. Collectively, our findings suggest that CFSS potentiates synaptic efficiency of the BLA-CeA pathway, leading to the activation of GluN2B-NMDA receptors and sensitization of CeA neurons, which subsequently facilitate pain-related synaptic plasticity of the PB-CeA pathway, thereby exacerbating SNI-induced neuropathic pain. We conclude that chronic stress exacerbates neuropathic pain via the integration of stress-affect–related information with nociceptive information in the CeA.
Image forgery localization, which centers on identifying tampered pixels within an image, has seen significant advancements. Traditional approaches often model this challenge as a variant of image segmentation, treating the binary segmentation of forged areas as the end product. We argue that the basic binary forgery mask is inadequate for explaining model predictions. It doesn't clarify why the model pinpoints certain areas and treats all forged pixels the same, making it hard to spot the most fake-looking parts. In this study, we mitigate the aforementioned limitations by generating salient region-focused interpretation for the forgery images. To support this, we craft a Multi-Modal Tramper Tracing (MMTT) dataset, comprising facial images manipulated using deepfake techniques and paired with manual, interpretable textual annotations. To harvest high-quality annotation, annotators are instructed to meticulously observe the manipulated images and articulate the typical characteristics of the forgery regions. Subsequently, we collect a dataset of 128,303 image-text pairs. Leveraging the MMTT dataset, we develop ForgeryTalker, an architecture designed for concurrent forgery localization and interpretation. ForgeryTalker first trains a forgery prompter network to identify the pivotal clues within the explanatory text. Subsequently, the region prompter is incorporated into multimodal large language model for finetuning to achieve the dual goals of localization and interpretation. Extensive experiments conducted on the MMTT dataset verify the superior performance of our proposed model. The dataset, code as well as pretrained checkpoints will be made publicly available to facilitate further research and ensure the reproducibility of our results.
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