Does electroacupuncture have different effects on peripheral and central sensitization in humans : a randomized controlled study

Background: Acupuncture is used to reduce chronic musculoskeletal pain. The common mechanism underlying these types of pain are peripheral and/or central sensitization. In the clinical setting, it is difficult to separate the peripheral from the central component of pain. Heat/capsaicin 45°C/0.075%-induced hyperalgesia provides a stable, human central sensitization model in which the peripheral component is also assessed. Aim: This randomized, sham-controlled study aimed to investigate the effect of electroacupuncture (EA) on the severity of heat (peripheral sensitization) and mechanical hyperalgesia (central sensitization) in a heat/capsaicin pain model in humans. Methods: Twenty-six healthy young participants (24 ± 3.9 years) were recruited. After baseline assessment, heat/capsaicin 45°C/0.075% was applied to the non-dominant forearm to induce hyperalgesia. The primary outcome measures were the size of the area of mechanical hyperalgesia, intensity of pain to heat stimulation and heat pain thresholds. The intensity of pain was recorded using modified 10-cm visual analogues scales (VAS). Participants were assessed at 70 min after the initial application of capsaicin then randomly allocated to receive either real electroacupuncture (REA, n = 14) or sham non-invasive EA (SEA, n = 12) for 30 min. The main outcome measures were assessed again immediately and then 90 min following EA. Credibility of blinding was assessed. Data were analyzed with t-tests or analysis of variance (ANOVA) where appropriate. Results: After the model was established, the area of mechanical hyperalgesia was formed (55.64 cm2), as was heat hyperalgesia, as the rating to heat stimulation, increased from 2/10 to 6/10. The REA and SEA groups were comparable. Immediately after the allocated acupuncture treatment, the rating to heat stimulation was statistically significantly lower in the REA group (2.94 ± 1.64) than in the SEA group (4.62 ± 2.26) (p < 0.05). The area of mechanical hyperalgesia reduced significantly without any group difference. No group difference was detected in heat pain threshold. Blinding of the participants was successful. Conclusion: Peripheral and central sensitization in the heat/capsaicin 45°C/0.075% model responded to EA differently, suggesting that acupuncture analgesia could vary, depending on the types of pain. This observation may explain some inconsistent findings from clinical trials of acupuncture

MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation

Ovarian cancer is one of the most harmful gynecological diseases. Detecting ovarian tumors in early stage with computer-aided techniques can efficiently decrease the mortality rate. With the improvement of medical treatment standard, ultrasound images are widely applied in clinical treatment. However, recent notable methods mainly focus on single-modality ultrasound ovarian tumor segmentation or recognition, which means there still lacks researches on exploring the representation capability of multi-modality ultrasound ovarian tumor images. To solve this problem, we propose a Multi-Modality Ovarian Tumor Ultrasound (MMOTU) image dataset containing 1469 2d ultrasound images and 170 contrast enhanced ultrasonography (CEUS) images with pixel-wise and global-wise annotations. Based on MMOTU, we mainly focus on unsupervised cross-domain semantic segmentation task. To solve the domain shift problem, we propose a feature alignment based architecture named Dual-Scheme Domain-Selected Network (DS2Net). Specifically, we first design source-encoder and target-encoder to extract two-style features of source and target images. Then, we propose Domain-Distinct Selected Module (DDSM) and Domain-Universal Selected Module (DUSM) to extract the distinct and universal features in two styles (source-style or target-style). Finally, we fuse these two kinds of features and feed them into the source-decoder and target-decoder to generate final predictions. Extensive comparison experiments and analysis on MMOTU image dataset show that DS2Net can boost the segmentation performance for bidirectional cross-domain adaptation of 2d ultrasound images and CEUS images. Our proposed dataset and code are all available at https://github.com/cv516Buaa/MMOTU_DS2Net.Comment: code: https://github.com/cv516Buaa/MMOTU_DS2Net paper:18 pages, 12 figures, 11 tables, 16 formula

Improving the Segmentation Accuracy of Ovarian-Tumor Ultrasound Images Using Image Inpainting

Diagnostic results can be radically influenced by the quality of 2D ovarian-tumor ultrasound images. However, clinically processed 2D ovarian-tumor ultrasound images contain many artificially recognized symbols, such as fingers, crosses, dashed lines, and letters which assist artificial intelligence (AI) in image recognition. These symbols are widely distributed within the lesion’s boundary, which can also affect the useful feature-extraction-utilizing networks and thus decrease the accuracy of lesion classification and segmentation. Image inpainting techniques are used for noise and object elimination from images. To solve this problem, we observed the MMOTU dataset and built a 2D ovarian-tumor ultrasound image inpainting dataset by finely annotating the various symbols in the images. A novel framework called mask-guided generative adversarial network (MGGAN) is presented in this paper for 2D ovarian-tumor ultrasound images to remove various symbols from the images. The MGGAN performs to a high standard in corrupted regions by using an attention mechanism in the generator to pay more attention to valid information and ignore symbol information, making lesion boundaries more realistic. Moreover, fast Fourier convolutions (FFCs) and residual networks are used to increase the global field of perception; thus, our model can be applied to high-resolution ultrasound images. The greatest benefit of this algorithm is that it achieves pixel-level inpainting of distorted regions without clean images. Compared with other models, our model achieveed better results with only one stage in terms of objective and subjective evaluations. Our model obtained the best results for 256 × 256 and 512 × 512 resolutions. At a resolution of 256 × 256, our model achieved 0.9246 for SSIM, 22.66 for FID, and 0.07806 for LPIPS. At a resolution of 512 × 512, our model achieved 0.9208 for SSIM, 25.52 for FID, and 0.08300 for LPIPS. Our method can considerably improve the accuracy of computerized ovarian tumor diagnosis. The segmentation accuracy was improved from 71.51% to 76.06% for the Unet model and from 61.13% to 66.65% for the PSPnet model in clean images

Resolving Ultraviolet–Visible Spectra for Complex Dissolved Mixtures of Multitudinous Organic Matters in Aerosols

Light-absorbing organic aerosols, referred to as brown carbon (BrC), play a vital role in the global climate and air quality. Due to the complexity of BrC chromophores, the identified absorbing substances in the ambient atmosphere are very limited. However, without comprehensive knowledge of the complex absorbing compounds in BrC, our understanding of its sources, formation, and evolution mechanisms remains superficial, leading to great uncertainty in climatic and atmospheric models. To address this gap, we developed a constrained non-negative matrix factorization (NMF) model to resolve the individual ultraviolet–visible spectrum for each substance in dissolved organic aerosols, with the power of ultrahigh-performance liquid chromatography-diode array detector-ultrahigh-resolution mass spectrometry (UHPLC-DAD-UHRMS). The resolved spectra were validated by selected standard substances and validation samples. Approximately 40,000 light-absorbing substances were recognized at the MS1 level. It turns out that BrC is composed of a vast number of substances rather than a few prominent chromophores in the urban atmosphere. Previous understanding of the absorbing feature of BrC based on a few identified compounds could be biased. Weak-absorbing substances missed previously play an important role in BrC absorption when they are integrated due to their overwhelming number. This model brings the property exploration of complex dissolved organic mixtures to a molecular level, laying a foundation for identifying potentially significant compositions and obtaining a comprehensive chemical picture