Scalp electroacupuncture targeting the VTADA neurons to relieve negative emotions and promote the alleviation of chronic pain

ObjectChronic pain and negative emotions are often linked, and both can impact the reward circuit. The use of electroacupuncture (EA) has been found to regulate and improve these conditions. This study explores the potential mechanism of chronic pain relief by adding acupoints with emotional regulation effect to the basis of routine EA analgesia, to optimize the acupoint compatibility scheme of EA in the treatment of analgesia.MethodFor this study, 42 male Wistar rats were used. Recombinant adeno-associated viruses were used to label and regulate the activity of dopamine (DA) neurons. The rat model was established by complete Freund’s adjuvant (CFA). Lower limb electroacupuncture (LEA) was applied to the ST36 and BL60 acupoints. In addition, LEA + scalp EA (SEA) was given using the GV20 and GV24+ acupoints besides ST36 and BL60. To evaluate the pain threshold, we measured 50% paw withdrawal thresholds and thermal paw withdrawal latencies. Negative emotions were evaluated through the open field test, marble-burying test, sucrose preference test, and forced swimming test. Moreover, the conditional place preference test was conducted to measure the reward behavior in response to pain relief. Immunofluorescence staining, Western blotting, and qPCR were used to detect the activity of the VTADA-NAc reward circuit.ResultThe injection of CFA significantly lowered the pain threshold. As the pain persisted, the anxiety and depression-like behaviors escalated while the response to reward reduced. Meanwhile, the VTADA-NAc pathway was suppressed with pain chronification. However, activating DA neurons in VTA attenuated the effects induced by CFA. LEA could relieve chronic pain, negative emotions, and reward disorders, while also activating the VTADA-NAc pathway. In addition, LEA + SEA exhibited a more pronounced effect compared with LEA alone. Nevertheless, chemogenetic inhibition of DA neurons decreased the efficacy of LEA + SEA in the treatment of chronic pain and associated comorbidities.ConclusionAdding SEA to conventional LEA effectively alleviates negative emotions and chronic pain, potentially due to the activation of the VTADA-NAc reward neural circuit. Thus, LEA + SEA is a more effective treatment for hyperalgesia and associated negative emotions compared with LEA alone

Prepreg and Core Dielectric Permittivity (ϵr) Extraction for Fabricated Striplines\u27 Far-End Crosstalk Modeling

As the data rate and density of digital high-speed systems are getting higher, far-end crosstalk (FEXT) noise becomes one of the major issues that limit signal integrity performance. It was commonly believed that FEXT would be eliminated for strip lines routed in a homogeneous dielectric, but in reality, FEXT can always be measured in strip lines on the fabricated printed circuit boards. A slightly different dielectric permittivity (ϵr) of prepreg and core may be one of the major contributors to the FEXT. This article is focusing on providing a practical FEXT modeling methodology for strip lines by introducing an approach to extract ϵr of prepreg and core. Using the known cross-sectional geometry and measured S-parameters of the coupled strip line, the capacitance components in prepreg and core are separated using a two-dimensional solver, and the ϵr of prepreg and core is determined. A more comprehensive FEXT modeling approach is proposed by applying extracted inhomogeneous dielectric material information

The Role of IL-17 Promotes Spinal Cord Neuroinflammation via Activation of the Transcription Factor STAT3 after Spinal Cord Injury in the Rat

Study Design. In this study, we investigated the role of IL-17 via activation of STAT3 in the pathophysiology of SCI. Objective. The purpose of the experiments is to study the expression of IL-17 and related cytokines via STAT3 signaling pathways, which is caused by the acute inflammatory response following SCI in different periods via establishing an acute SCI model in rat. Methods. Basso, Beattie, and Bresnahan hind limb locomotor rating scale was used to assess the rat hind limb motor function. Immunohistochemistry was used to determine the expression levels of IL-17 and p-STAT3 in spinal cord tissues. Western blotting analysis was used to determine the protein expression of p-STAT3 in spinal cord tissue. RT-PCR was used to analyze the mRNA expression of IL-17 and IL-23p19 in the spleen tissue. ELISA was used to determine the peripheral blood serum levels of IL-6, IL-21, and IL-23. Results. Compared to the sham-operated group, the expression levels of IL-17, p-STAT3, IL-6, IL-21, and IL-23 were significantly increased and peaked at 24 h after SCI. The increased levels of cytokines were correlated with the SCI disease stages. Conclusion. IL-17 may play an important role in promoting spinal cord neuroinflammation after SCI via activation of STAT3

A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise

The surge of interest towards Multi-modal Large Language Models (MLLMs), e.g., GPT-4V(ision) from OpenAI, has marked a significant trend in both academia and industry. They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks. Very recently, Google released Gemini, its newest and most capable MLLM built from the ground up for multi-modality. In light of the superior reasoning capabilities, can Gemini challenge GPT-4V's leading position in multi-modal learning? In this paper, we present a preliminary exploration of Gemini Pro's visual understanding proficiency, which comprehensively covers four domains: fundamental perception, advanced cognition, challenging vision tasks, and various expert capacities. We compare Gemini Pro with the state-of-the-art GPT-4V to evaluate its upper limits, along with the latest open-sourced MLLM, Sphinx, which reveals the gap between manual efforts and black-box systems. The qualitative samples indicate that, while GPT-4V and Gemini showcase different answering styles and preferences, they can exhibit comparable visual reasoning capabilities, and Sphinx still trails behind them concerning domain generalizability. Specifically, GPT-4V tends to elaborate detailed explanations and intermediate steps, and Gemini prefers to output a direct and concise answer. The quantitative evaluation on the popular MME benchmark also demonstrates the potential of Gemini to be a strong challenger to GPT-4V. Our early investigation of Gemini also observes some common issues of MLLMs, indicating that there still remains a considerable distance towards artificial general intelligence. Our project for tracking the progress of MLLM is released at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.Comment: Total 120 pages. See our project at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model

T cell-related ubiquitination genes as prognostic indicators in hepatocellular carcinoma

BackgroundT lymphocytes, integral to the adaptive immune system, wield pivotal influence in bolstering anti-tumor responses, and are strictly regulated by ubiquitination modification. The objective of this investigation was to devise a novel prognostic and immunotherapeutic efficacy predictor for hepatocellular carcinoma patients utilizing T cell-related ubiquitination genes (TCRUG).MethodThe single-cell RNA sequencing (scRNA-seq) data and bulk RNA data of HCC patients are derived from the GEO database and TCGA database. Based on the processing of scRNA-seq, T cell marker genes are obtained and TCRUG is obtained. Further combined with WGCNA, differential analysis, univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis to filter and screen TCRUG. Finally construct a riskscore for predicting the prognosis of HCC patients, the predictive effect of which is validated in the GEO dataset. In addition, we also studied the correlation between riskscore and immunotherapy efficacy. Finally, the oncogenic role of UBE2E1 in HCC was explored through various in vitro experiments.ResultBased on patients’ scRNA-seq data, we finally obtained 3050 T cell marker genes. Combined with bulk RNA data and clinical data from the TCGA database, we constructed a riskscore that accurately predicts the prognosis of HCC patients. This riskscore is an independent prognostic factor for HCC and is used to construct a convenient column chart. In addition, we found that the high-risk group is more suitable for immunotherapy. Finally, the proliferation, migration, and invasion abilities of HCC cells significantly decreased after UBE2E1 expression reduction.ConclusionThis study developed a riskscore based on TCRUG that can accurately and stably predict the prognosis of HCC patients. This riskscore is also effective in predicting the immune therapy response of HCC patients

A Spectral–Spatial Transformer Fusion Method for Hyperspectral Video Tracking

Hyperspectral videos (HSVs) can record more adequate detail clues than other videos, which is especially beneficial in cases of abundant spectral information. Although traditional methods based on correlation filters (CFs) employed to explore spectral information locally achieve promising results, their performances are limited by ignoring global information. In this paper, a joint spectral–spatial information method, named spectral–spatial transformer-based feature fusion tracker (SSTFT), is proposed for hyperspectral video tracking, which is capable of utilizing spectral–spatial features and considering global interactions. Specifically, the feature extraction module employs two parallel branches to extract multiple-level coarse-grained and fine-grained spectral–spatial features, which are fused with adaptive weights. The extracted features are further fused with the context fusion module based on a transformer with the hyperspectral self-attention (HSA) and hyperspectral cross-attention (HCA), which are designed to capture the self-context feature interaction and the cross-context feature interaction, respectively. Furthermore, an adaptive dynamic template updating strategy is used to update the template bounding box based on the prediction score. The extensive experimental results on benchmark hyperspectral video tracking datasets demonstrated that the proposed SSTFT outperforms the state-of-the-art methods in both precision and speed

BiTSRS: A Bi-Decoder Transformer Segmentor for High-Spatial-Resolution Remote Sensing Images

Semantic segmentation of high-spatial-resolution (HSR) remote sensing (RS) images has been extensively studied, and most of the existing methods are based on convolutional neural network (CNN) models. However, the CNN is regarded to have less power in global representation modeling. In the past few years, methods using transformer have attracted increasing attention and generate improved results in semantic segmentation of natural images, owing to their powerful ability in global information acquisition. Nevertheless, these transformer-based methods exhibit limited performance in semantic segmentation of RS images, probably because of the lack of comprehensive understanding in the feature decoding process. In this paper, a novel transformer-based model named the bi-decoder transformer segmentor for remote sensing (BiTSRS) is proposed, aiming at alleviating the problem of flexible feature decoding, through a bi-decoder design for semantic segmentation of RS images. In the proposed BiTSRS, the Swin transformer is adopted as encoder to take both global and local representations into consideration, and a unique design module (ITM) is designed to deal with the limitation of input size for Swin transformer. Furthermore, BiTSRS adopts a bi-decoder structure consisting of a Dilated-Uper decoder and a fully deformable convolutional network (FDCN) module embedded with focal loss, with which it is capable of decoding a wide range of features and local detail deformations. Both ablation experiments and comparison experiments were conducted on three representative RS images datasets. The ablation analysis demonstrates the contributions of specifically designed modules in the proposed BiTSRS to performance improvement. The comparison experimental results illustrate that the proposed BiTSRS clearly outperforms some state-of-the-art semantic segmentation methods

The Effects of Interleukin-17 (IL-17)-Related Inflammatory Cytokines and A20 Regulatory Proteins on Astrocytes in Spinal Cord Cultured In Vitro

Background/Aims: This study focused on investigating the expression of several inflammatory cytokines and chemokines, including regulatory proteins in the astrocytes of mice stimulated with IL-17. Materials and Methods: The cultured astrocytes from the spinal cords of mice were stimulated with IL-17. The expression of interleukin-6 (IL-6), tumor necrosis factor (TNF), monocyte chemotactic protein-1/5 (MCP-1/5) and macrophage inflammatory protein-2 (MIP-2) stimulated with IL-17 (50 ng/ml) at different time points (3 h, 6 h, 12 h, 24 h and 48 h) were determined using real-time PCR and ELISA. The expressions of A20 (tumor necrosis factor a inducible protein 3, TNFAIP3) and NF-κB were examined using real-time PCR and western blotting. Results: Compared with the control group, the mRNA expression levels of IL-6, TNF, MCP-1/5 and MIP-2 increased significantly at 6 h after IL-17 stimulation, while the protein expression levels also increased significantly and peaked at 12 h. The mRNA expression level of NF-κB increased and peaked at 6 h before gradually declining, while the expression of A20 decreased. The protein expression level of NF-κB increased and peaked at 12 h, while the expression A20 had an opposite response. Conclusion: The study showed that NF-κB may have an effect on the cytokines secreted by astrocytes after IL-17 stimulation. Moreover, both A20 and NF-κB could regulate the expression and secretion of inflammatory mediators