Tyrosine phosphorylation of the N-Methyl-D-Aspartate receptor 2B subunit in spinal cord contributes to remifentanil-induced postoperative hyperalgesia: the preventive effect of ketamine

<p>Abstract</p> <p>Background</p> <p>Experimental and clinical studies showed that intraoperative infusionof remifentanil has been associated with postoperative hyperalgesia. Previous reports suggested that spinal N-methyl-D-aspartate (NMDA) receptors may contribute to the development and maintenance of opioid-induced hyperalgesia. In the present study, we used a rat model of postoperative pain to investigate the role of tyrosine phosphorylation of NMDA receptor 2B (NR2B) subunit in spinal cord in the postoperative hyperalgesia induced by remifentanil and the intervention of pretreatment with ketamine.</p> <p>Results</p> <p>Intraoperative infusion of remifentanil (0.04 mg/kg, subcutaneous) significantly enhanced mechanical allodynia and thermal hyperalgesia induced by the plantar incision during the postoperative period (each lasting between 2 h and 48 h), which was attenuated by pretreatment with ketamine (10 mg/kg, subcutaneous). Correlated with the pain behavior changes, immunocytochemical and western blotting experiments in our study revealed that there was a marked increase in NR2B phosphorylation at Tyr1472 in the superficial dorsal horn after intraoperative infusion of remifentanil, which was attenuated by pretreatment with ketamine.</p> <p>Conclusions</p> <p>This study provides direct evidence that tyrosine phosphorylation of the NR2B at Tyr1472 in spinal dosal horn contributes to postoperative hyperalgesia induced by remifentanil and supports the potential therapeutic value of ketamine for improving postoperative hyperalgesia induced by remifentanil.</p

Intraperitoneal injection of thalidomide attenuates bone cancer pain and decreases spinal tumor necrosis factor-α expression in a mouse model

<p>Abstract</p> <p>Background</p> <p>Tumor necrosis factor α (TNF-α) may have a pivotal role in the genesis of mechanical allodynia and thermal hyperalgesia during inflammatory and neuropathic pain. Thalidomide has been shown to selectively inhibit TNF-α production. Previous studies have suggested that thalidomide exerts anti-nociceptive effects in various pain models, but its effects on bone cancer pain have not previously been studied. Therefore, in the present study, we investigated the effect of thalidomide on bone cancer-induced hyperalgesia and up-regulated expression of spinal TNF-α in a mouse model.</p> <p>Results</p> <p>Osteosarcoma NCTC 2472 cells were implanted into the intramedullary space of the right femurs of C3H/HeJ mice to induce ongoing bone cancer related pain behaviors. At day 5, 7, 10 and 14 after operation, the expression of TNF-α in the spinal cord was higher in tumor-bearing mice compared to the sham mice. Intraperitoneal injection of thalidomide (50 mg/kg), started at day 1 after surgery and once daily thereafter until day 7, attenuated bone cancer-evoked mechanical allodynia and thermal hyperalgesia as well as the up-regulation of TNF-α in the spinal cord.</p> <p>Conclusions</p> <p>These results suggest that thalidomide can efficiently alleviate bone cancer pain and it may be a useful alternative or adjunct therapy for bone cancer pain. Our data also suggest a role of spinal TNF-α in the development of bone cancer pain.</p

Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps

Over decades, neuroscience has accumulated a wealth of research results in the text modality that can be used to explore cognitive processes. Meta-analysis is a typical method that successfully establishes a link from text queries to brain activation maps using these research results, but it still relies on an ideal query environment. In practical applications, text queries used for meta-analyses may encounter issues such as semantic redundancy and ambiguity, resulting in an inaccurate mapping to brain images. On the other hand, large language models (LLMs) like ChatGPT have shown great potential in tasks such as context understanding and reasoning, displaying a high degree of consistency with human natural language. Hence, LLMs could improve the connection between text modality and neuroscience, resolving existing challenges of meta-analyses. In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments. By utilizing the understanding and reasoning capabilities of LLMs, the performance of the mapping model is optimized by transferring text queries to semantic queries. We demonstrate that Chat2Brain can synthesize anatomically plausible neural activation patterns for more complex tasks of text queries.Comment: 8 pages, 4 figure

SAMAug: Point Prompt Augmentation for Segment Anything Model

This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information about the user's intention to SAM. Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain-specific. To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4's reasoning ability by introducing varying levels of inference difficulty. Our results show that 1) GPT-4 outperforms ChatGPT in the radiology NLI task; 2) other specifically fine-tuned models require significant amounts of data samples to achieve comparable performance to ChatGPT/GPT-4. These findings demonstrate that constructing a generic model that is capable of solving various tasks across different domains is feasible

ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT

Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT

Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models

This paper presents a comprehensive survey of ChatGPT and GPT-4, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT/GPT-4 research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.Comment: 35 pages, 3 figure

Intrathecal Injection of Spironolactone Attenuates Radicular Pain by Inhibition of Spinal Microglia Activation in a Rat Model

Microglia might play an important role in nociceptive processing and hyperalgesia by neuroinflammatory process. Mineralocorticoid receptor (MR) expressed on microglia might play a central role in the modulation of microglia activity. However the roles of microglia and MR in radicular pain were not well understood. This study sought to investigate whether selective MR antagonist spironolactone develop antinociceptive effects on radicular pain by inhibition neuroinflammation induced by spinal microglia activation.Radicular pain was produced by chronic compression of the dorsal root ganglia with SURGIFLO™. The expression of microglia, interleukin beta (IL-1β), interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), NR1 subunit of the NMDA receptor (t-NR1), and NR1 subunit phosphorylated at Ser896 (p-NR1) were also markedly up-regulated. Intrathecal injection of spironolactone significantly attenuated pain behaviors as well as the expression of microglia, IL-1β, TNF-α, t-NR1, and p-NR1, whereas the production of IL-6 wasn't affected.These results suggest that intrathecal delivery spironolactone has therapeutic effects on radicular pain in rats. Decreasing the activation of glial cells, the production of proinflammatory cytokines and down-regulating the expression and phosphorylation of NMDA receptors in the spinal dorsal horn and dorsal root ganglia are the main mechanisms contributing to its beneficial effects

Logarithmic Sobolev inequalities for harmonic measures on spheres

International audienc

Gravid females of Cephalcia chuxiongica (Hymenoptera, Pamphiliidae) are attracted to egg-carrying needles of Pinus yunnanensis

Cephalcia chuxiongica Xiao is one of the most dangerous defoliators of Pinus yunnanensis and other pine species in Yunnan province, resulting in serious losses. Its distinguishing characteristics are the females’ aggregation oviposition and larvae’s aggregation feeding. In order to explore the mechanism of aggregation oviposition in this sawfly, preliminary olfactory bioassay was conducted in laboratory. In in-cage choice tests, on average vast majority gravid females selected the shoots that had been loaded and oviposited by a ‘pioneer’ female. In one-choice tests in laboratory by a Y-tube olfactometer, the gravid females were attracted by the odors of eggs-carrying shoots (PE), shoots with one delivering female and her eggs (PGE), needles’ extract (NE), and fresh eggs’ eluent (EL); the virgin females were attracted by odors of fresh needles (P), PE, PGE, and NE, but repelled by odors of virgin and gravid females. In two-choice tests, the odors were tested in pairs for gravid females. When compared with odors of gravid females (G) or P, gravid females showed significantly more tendency to odors of PE or PGE. When given odors EL vs. NE, gravid females preferred the odors of NE, but they did not make obvious selection between G vs. P, and PE vs. PGE. Based on the results, our conjectures were: (1) Delivery female, as a pioneer, can summon her conspecific gravid females to aggregate in the same pine shoot; (2) Pine needles’ odors were attractive for both the virgin and gravid females; (3) Gravid females could be attracted by odors released by the pioneer gravid females; (4) The olfactory sensation of the females may be changed by mating