Anti-CNN2 monoclonal antibody Made to order

Neural crest cells (NCCs) migrate throughout the embryo to differentiate into cell types of all germ layers. Initial directed NCC emigration relies on planar cell polarity (PCP), which through the activity of the small GTPases RhoA and Rac governs the actin-driven formation of polarized cell protrusions. We found that the actin binding protein calponin 2 (Cnn2) was expressed in protrusions at the leading edge of migratory NCCs in chicks and frogs. Cnn2 knockdown resulted in NCC migration defects in frogs and chicks and randomized outgrowth of cell protrusions in NCC explants. Morphant cells showed central stress fibers at the expense of the peripheral actin network. Cnn2 acted downstream of Wnt/PCP, as migration defects induced by dominant-negative Wnt11 or inhibition of RhoA function were rescued by Cnn2 knockdown. These results suggest that Cnn2 modulates actin dynamics during NCC migration as an effector of noncanonical Wnt/PCP signaling.

Background Accurate identification of the differentiation status and margins for early gastric cancer (EGC) is critical for determining the surgical strategy and achieving curative resection in EGC patients. The aim of this study was to develop a real-time system to accurately identify differentiation status and delineate the margins of EGC on magnifying narrow-band imaging (ME-NBI) endoscopy. Methods 2217 images from 145 EGC patients and 1870 images from 139 EGC patients were retrospectively collected to train and test the first convolutional neural network (CNN1) to identify EGC differentiation status. The performance of CNN1 was then compared with that of experts using 882 images from 58 EGC patients. Finally, 928 images from 132 EGC patients and 742 images from 87 EGC patients were used to train and test CNN2 to delineate the EGC margins. Results The system correctly predicted the differentiation status of EGCs with an accuracy of 83.3% (95% confidence interval [CI] 81.5%-84.9%) in the testing dataset. In the man-machine contest, CNN1 performed significantly better than the five experts (86.2%, 95%CI 75.1%-92.8% vs. 69.7%, 95%CI 64.1%-74.7%). For delineating EGC margins, the system achieved an accuracy of 82.7% (95%CI 78.6%-86.1%) in differentiated EGC and 88.1% (95%CI 84.2%-91.1%) in undifferentiated EGC under an overlap ratio of 0.80.In unprocessed EGC videos, the system achieved real-time diagnosis of EGC differentiation status and EGC margin delineation in ME-NBI endoscopy. Conclusion We developed a deep learning-based system to accurately identify differentiation status and delineate the margins of EGC in ME-NBI endoscopy. This system achieved superior performance when compared with experts and was successfully tested in real EGC videos.