Investigation of the In Vivo, In Vitro, and In Silico Wound Healing Potential of Pinctada martensii Purified Peptides.

Previous studies found that both oral and topical administration of enzymatic digestion products < 3 K Da ultrafiltration fractions of Pinctada martensii mantle (PMPs) had pro-healing effects. Thus, we further purified them by Sephadex-G25 and screened them by cellular assays to obtain Pinctada martensii purified peptides (PMPPs). In this study, we explored the mechanism of PMPPs on wound healing by in vivo, in vitro, and in silico experiments. LC-MS/MS results showed that PMPPs consisted of 33 peptides with molecular weights ranging from 758.43 to 2014.04 Da, and the characteristic peptide was Leu-Asp. The results of cellular assays showed that PMPPs promoted the proliferation of human skin fibroblasts (HSF) (135%) and human immortalized keratinocyte (HaCaT) cells (125%) very significantly at 12.5 µg/mL. The in vivo results showed that PMPPs could achieve scarless healing by inhibiting the inflammatory response, accelerating the epithelialization process, and regulating collagen I/III ratio. The optimal peptide sequence FAFQAEIAQLMS of PMPPs was screened for key protein receptors in wound healing (EGFR1, FGFR1, and MMP-1) with the help of molecular docking technique, which also showed to be the key pro-healing active peptide sequence. Therefore, it may provide a therapeutic strategy with great potential for wound healing.

Real-Time Scene Text Detection With Differentiable Binarization and Adaptive Scale Fusion.

Recently, segmentation-based scene text detection methods have drawn extensive attention in the scene text detection field, because of their superiority in detecting the text instances of arbitrary shapes and extreme aspect ratios, profiting from the pixel-level descriptions. However, the vast majority of the existing segmentation-based approaches are limited to their complex post-processing algorithms and the scale robustness of their segmentation models, where the post-processing algorithms are not only isolated to the model optimization but also time-consuming and the scale robustness is usually strengthened by fusing multi-scale feature maps directly. In this paper, we propose a Differentiable Binarization (DB) module that integrates the binarization process, one of the most important steps in the post-processing procedure, into a segmentation network. Optimized along with the proposed DB module, the segmentation network can produce more accurate results, which enhances the accuracy of text detection with a simple pipeline. Furthermore, an efficient Adaptive Scale Fusion (ASF) module is proposed to improve the scale robustness by fusing features of different scales adaptively. By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks.