Suppression of MyD88-dependent signaling alleviates neuropathic pain induced by peripheral nerve injury in the rat

Abstract Background MyD88 is the adaptor protein of MyD88-dependent signaling pathway of TLRs and IL-1 receptor and regulates innate immune response. However, it was not clear whether and how MyD88 and related signaling pathways in the dorsal root ganglion (DRG) and spinal dorsal horn (SDH) are involved in neuropathic pain. Methods Chronic constriction injury (CCI) was used to induce neuropathic pain in the rat. The expression of MyD88, TRIF, IBA1, and GFAP was detected with immunofluorescent staining and Western blot. The expression of interleukin-1 beta (IL-1β), high mobility group box 1 (HMGB1), NF-κB-p65, phosphorylated NF-κB-p65, ERK, phosphorylated ERK, and tumor necrosis factor-alpha (TNF-α) was detected with Western blot. Pain-related behavioral effects of MyD88 homodimerization inhibitory peptide (MIP) were accessed up to 3 weeks after intrathecal administration. Results Peripheral nerve injury significantly increased the protein level of MyD88 in the DRG and SDH, but had no effect on TRIF. MyD88 was found partly distributed in the nociceptive neurons in the DRGs and the astrocytes and microglia in the SDH. HMGB1 and IL-1β were also found upregulated in nociceptive pathways of CCI rats. Intrathecal application of MIP significantly alleviated mechanical and thermal hyperalgesia in the CCI rats and also reversed CCI-induced upregulation of MyD88 in both DRG and SDH. Further investigation revealed that suppression of MyD88 protein reduced the release of TNF-α and glial activation in the SDH in the CCI rats. Conclusions MyD88-dependent TIR pathway in the DRG and SDH may play a role in CCI-induced neuropathic pain. MyD88 might serve as a potential therapeutic target for neuropathic pain

The fast light of CsI(Na) crystals

The responds of different common alkali halide crystals to alpha-rays and gamma-rays are tested in our research. It is found that only CsI(Na) crystals have significantly different waveforms between alpha and gamma scintillations, while others have not this phenomena. It is suggested that the fast light of CsI(Na) crystals arises from the recombination of free electrons with self-trapped holes of the host crystal CsI. Self-absorption limits the emission of fast light of CsI(Tl) and NaI(Tl) crystals.Comment: 5 pages, 11 figures Submit to Chinese Physics

PromptVC: Flexible Stylistic Voice Conversion in Latent Space Driven by Natural Language Prompts

Style voice conversion aims to transform the style of source speech to a desired style according to real-world application demands. However, the current style voice conversion approach relies on pre-defined labels or reference speech to control the conversion process, which leads to limitations in style diversity or falls short in terms of the intuitive and interpretability of style representation. In this study, we propose PromptVC, a novel style voice conversion approach that employs a latent diffusion model to generate a style vector driven by natural language prompts. Specifically, the style vector is extracted by a style encoder during training, and then the latent diffusion model is trained independently to sample the style vector from noise, with this process being conditioned on natural language prompts. To improve style expressiveness, we leverage HuBERT to extract discrete tokens and replace them with the K-Means center embedding to serve as the linguistic content, which minimizes residual style information. Additionally, we deduplicate the same discrete token and employ a differentiable duration predictor to re-predict the duration of each token, which can adapt the duration of the same linguistic content to different styles. The subjective and objective evaluation results demonstrate the effectiveness of our proposed system.Comment: Submitted to ICASSP 202

Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases. Therefore, it is important to develop an efficient heart segmentation algorithm.Methods: A total of 275 nuclear magnetic resonance imaging (MRI) heart scans were collected, analyzed, and preprocessed from Huaqiao University Affiliated Strait Hospital, and the data were used in our improved deep learning model, which was designed based on the U-net network. The training set included 80% of the images, and the remaining 20% was the test set. Based on five time phases from end-diastole (ED) to end-systole (ES), the segmentation findings showed that it is possible to achieve improved segmentation accuracy and computational complexity by segmenting the left ventricle (LV), right ventricle (RV), and myocardium (myo).Results: We improved the Dice index of the LV to 0.965 and 0.921, and the Hausdorff index decreased to 5.4 and 6.9 in the ED and ES phases, respectively; RV Dice increased to 0.938 and 0.860, and the Hausdorff index decreased to 11.7 and 12.6 in the ED and ES, respectively; myo Dice increased to 0.889 and 0.901, and the Hausdorff index decreased to 8.3 and 9.2 in the ED and ES, respectively.Conclusion: The model obtained in the final experiment provided more accurate segmentation of the left and right ventricles, as well as the myocardium, from cardiac MRI. The data from this model facilitate the prediction of cardiovascular disease in real-time, thereby providing potential clinical utility