Anti-DCN (aa 232-246) polyclonal antibody

Flexor tendon injuries and tendinopathy are very common but remain challenging in clinical treatment. Exosomes-based cell-free therapy appears to be a promising strategy for tendon healing, while limited studies have evaluated its impacts on tenocyte biology. The objective of this study was to characterize a novel purified exosome product (PEP) derived from plasma, as well as to explore its cellular effects on canine tenocyte biology. The transmission electron microscope revealed that exosomes of PEP present cup-shaped structures with the diameters ranged from 80 to 141 nm, and the NanoSight report presented that their size mainly concentrated around 100 nm. The enzyme-linked immunosorbent assay kits analysis showed that PEP was positive for CD63 and AChE expression, and the cellular uptake of exosomes internalized into tenocyte cytoplasm was observed. The cell growth assays displayed that tenocyte proliferation ability was enhanced by PEP solution in a dose-dependent manner. Tenogenic phenotype was preserved as is evident by that tendon-related genes expression (SCX, COL1A, COL3A1, TNMD, DCN, and MKX) were expressed insistently in a high level, while tenocytes were treated with 5% PEP solution. Furthermore, migration capability was maintained and total collagen deposition was increased. More interesting, dexamethasone-induced cellular apoptosis was attenuated during the incubation of tenocytes with a 5% PEP solution. These findings will provide the basic understandings about the PEP, and support the potential use of this biological strategy for tendon healing.

Designing computer-assisted diagnosis (CAD) systems that can precisely identify lesions from mammography images would be useful for clinicians. Considering the morphological variation in breast cancer, it is necessary to extract robust features from the mammogram. Here, we propose a mass detection CAD system that is based on Faster R-CNN. First, we applied a novel convolution network in the backbone of Faster R-CNN, namely deformable convolution network (DCN), which improves the detection of lesions with varying shapes and sizes. Second, the original Faster R-CNN uses the output of the last layer of the backbone as a single-scale feature map. To facilitate the detection of small lesions, we used a multiscale feature pyramid network of multiple cross-scale connections between the different output layers of the backbone, called the neural architecture search-feature pyramid network (NAS-FPN). Thus, we were able to integrate the best features into the model. We then evaluated our method by using the datasets the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, respectively. Our method yielded a true positive rate of 0.9345 at 2.2805 false positive per image on CBIS-DDSM and a true positive rate of 0.9554 at 0.3829 false positive per image on INbreast. Graphical abstract