Using DeepLab v3 + -based semantic segmentation to evaluate platelet activation.

This research used DeepLab v3 + -based semantic segmentation to automatically evaluate the platelet activation process and count the number of platelets from scanning electron microscopy (SEM) images. Current activated platelet recognition and counting methods include (a) using optical microscopy or SEM images to identify and manually count platelets at different stages, or (b) using flow cytometry to automatically recognize and count platelets. However, the former is time- and labor-consuming, while the latter cannot be employed due to the complicated morphology of platelet transformation during activation. Additionally, because of how complicated the transformation of platelets is, current blood-cell image analysis methods, such as logistic regression or convolution neural networks, cannot precisely recognize transformed platelets. Therefore, this study used DeepLab v3 + , a powerful learning model for semantic segmentation of image analysis, to automatically recognize and count platelets at different activation stages from SEM images. Deformable convolution, a pretrained model, and deep supervision were added to obtain additional platelet transformation features and higher accuracy. The number of activated platelets was predicted by dividing the segmentation predicted platelet area by the average platelet area. The results showed that the model counted the activated platelets at different stages from the SEM images, achieving an error rate within 20%. The error rate was approximately 10% for stages 2 and 4. The proposed approach can thus save labor and time for evaluating platelet activation and facilitate related research.

Author Correction: Wound Healing in Streptozotocin-Induced Diabetic Rats Using Atmospheric-Pressure Argon Plasma Jet.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Wound Healing in Streptozotocin-Induced Diabetic Rats Using Atmospheric-Pressure Argon Plasma Jet.

In this study, we used an argon-based round atmospheric-pressure plasma jet (APPJ) for enhancing wound healing in streptozotocin (STZ) induced diabetic rats. The APPJ was characterized by optical emission spectroscopy. We induced Type 1 and Type 2 diabetes in rats with different amounts of STZ combined with normal and high-fat diets, respectively. The wound area ratio of all the plasma-treated normal and diabetic groups was greatly reduced (up to 30%) compared with that of the untreated groups during healing. Histological analysis revealed faster re-epithelialization, collagen deposition, less inflammation, and a complete skin structure in the plasma-treated groups was found as compared with the untreated control groups. In addition, the new blood vessels of plasma-treated tissues decreased more than untreated tissues in the middle (Day 14) and late (Day 21) stages of wound healing. The plasma-treated wounds demonstrated more transforming growth factor beta (TGF-ß) expression in the early stage (Day 7), whereas they decreased in the middle and late stages of wound healing. The levels of superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase (CAT) increased after plasma treatment. In addition, plasma-treated water had a higher concentration of hydrogen peroxide, nitrite and nitrate when the plasma treatment time was longer. In summary, the proposed argon APPJ based on the current study could be a potential tool for treating diabetic wounds.