We previously reported that the transplantation of neural stem/progenitor cells (NSPCs) can contribute to the repair of injured spinal cord in adult rats and monkeys. In some cases, however, most of the transplanted cells adhered to the cavity wall and failed to migrate and integrate into the host spinal cord. In this study we focused on chondroitin sulfate proteoglycan (CSPG), a known constituent of glial scars that is strongly expressed after spinal cord injury (SCI), as a putative inhibitor of NSPC migration in vivo. We hypothesized that the digestion of CSPG by chondroitinase ABC (C-ABC) might promote the migration of transplanted cells and neurite outgrowth after SCI. An in vitro study revealed that the migration of NSPC-derived cells was inhibited by CSPG and that this inhibitory effect was attenuated by C-ABC pre-treatment. Consistently, an in vivo study of C-ABC treatment combined with NSPC transplantation into injured spinal cord revealed that C-ABC pre-treatment promoted the migration of the transplanted cells, whereas CSPG-immunopositive scar tissue around the lesion cavity prevented their migration into the host spinal cord in the absence of C-ABC pre-treatment. Furthermore, this combined treatment significantly induced the outgrowth of a greater number of growth-associated protein-43-positive fibers at the lesion epicentre, compared with NSPC transplantation alone. These findings suggested that the application of C-ABC enhanced the benefits of NSPC transplantation for SCI by reducing the inhibitory effects of the glial scar, indicating that this combined treatment may be a promising strategy for the regeneration of injured spinal cord.
Background
In some cases, a dentist cannot solve the difficulties a patient has with an implant because the implant system is unknown. Therefore, there is a need for a system for identifying the implant system of a patient from limited data that does not depend on the dentist’s knowledge and experience. The purpose of this study was to identify dental implant systems using a deep learning method.
Methods
A dataset of 1282 panoramic radiograph images with implants were used for deep learning. An object detection algorithm (Yolov3) was used to identify the six implant systems by three manufactures. To implement the algorithm, TensorFlow and Keras deep-learning libraries were used. After training was complete, the true positive (TP) ratio and average precision (AP) of each implant system as well as the mean AP (mAP), and mean intersection over union (mIoU) were calculated to evaluate the performance of the model.
Results
The number of each implant system varied from 240 to 1919. The TP ratio and AP of each implant system varied from 0.50 to 0.82 and from 0.51 to 0.85, respectively. The mAP and mIoU of this model were 0.71 and 0.72, respectively.
Conclusions
The results of this study suggest that implants can be identified from panoramic radiographic images using deep learning-based object detection. This identification system could help dentists as well as patients suffering from implant problems. However, more images of other implant systems will be necessary to increase the learning performance to apply this system in clinical practice.
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