[Objective]To discuss the treatment of tibiofibular fracture and soft tissue defects by combination of adjacent tissue flap transplant and external fixation.[Method]Sural neurovascular flaps,saphenous neurocutaneous vascular flaps,gastrocnemius flaps or other tissue flaps were selected to repair soft tissue defects.Among them there were 4.5 cm?3 cm-16 cm?8 cm soft tissue defect respectively,with unilateral or ring external fixators to fix tibia fracture.[Result]In the 15 clinical cases,9 cases were with bone exposure caused by necrosis of soft tissue,3 cases with open tibia and fibula fracture with soft tissue defect,3 cases with non-union of bone fracture with scar.All transplanted flaps survived well.The follow-up time ranged from 12 to 72 months with an average of 36 months.All tibial fracture healed.The function of the lower extremities recovered well.[Conclusion]The application of adjacent tissue flap transplant combined with external fixation technique is very effective in treating tibiofibular fracture with soft tissue defect.

Brain decoding based on functional magnetic resonance imaging has recently enabled the identification of visual perception and mental states. However, due to the limitations of sample size and the lack of an effective reconstruction model, accurate reconstruction of natural images is still a major challenge. The current, rapid development of deep learning models provides the possibility of overcoming these obstacles. Here, we propose a deep learning-based framework that includes a latent feature extractor, a latent feature decoder, and a natural image generator, to achieve the accurate reconstruction of natural images from brain activity. The latent feature extractor is used to extract the latent features of natural images. The latent feature decoder predicts the latent features of natural images based on the response signals from the higher visual cortex. The natural image generator is applied to generate reconstructed images from the predicted latent features of natural images and the response signals from the visual cortex. Quantitative and qualitative evaluations were conducted with test images. The results showed that the reconstructed image achieved comparable, accurate reproduction of the presented image in both high-level semantic category information and low-level pixel information. The framework we propose shows promise for decoding the brain activity.