ObjectiveTo evaluate the mid-term clinical effect of nano-hydroxyapatite and polyamide 66 (n-HA/PA66) intervertebral fusion cage in treatment of thoracolumbar burst fractures.Methods A total of 87 patients with thoracolumbar burst fractures were managed by thoracolumbar body resection combined with n-HA/PA66 intervertebral fusion cage from December 2007 to September 2008.The clinical effect, safety and radiographic outcomes were evaluated.Results No nerve damage was deteriorated in all the patients.The neural function was improved for 1-2 grade except for four patients at Frankel grade A.The patients were followed up for mean 21.3 months (17-24 months).The kyphosis was (14.4 ± 12.6)° preoperatively, (3.7 ± 8.7) ° immediately after surgery and (4.0 ± 8.3)° at final follow-up.The distance between the upper and lower vertebral bodies was (96.9 ± 17.2) mm preoperatively, (109.5 ± 17.1) mm immediately after surgery and (108.3 ± 16.4) mm at final follow-up.No cage replacement, internal fixation breakage or neurologic impairment were observed during follow-up period.There were 58 patients with grade E fusion, 22 with grade D fusion and 7 with grade C fusion.ConclusionsAnterior decompression combined with n-HA/PA66 intervertebral fusion cage is an effective method for treatment of thoracolumbar burst fracture.The kyphosis is rectified and the intervertebral distance is corrected, with a high rate of fusion.

Objective To construct a TCM Zhengsu differentiation model for type 2 diabetes based on deep learning and multimodal fusion,thus providing algorithmic support for full intelligence in TCM Zhengsu differentiation.Methods A total of 2585 patients with type 2 diabetes were recruited.Three experts were invited to perform the Zhengsu differentiation separately.Deep fully connected neural networks,U2-Net and ResNet34 networks were applied to construct the symptom-based differentiation model(S-Model)and the tongue image-based differentiation model(T-Model),respectively,while multimodal fusion techniques were employed to build the multimodal fusion model(TS-Model)with the above two as co-inputs.Finally,the prediction performance of the above models was compared by F1 value,accuracy,and recall.Results The predicted F1 values of the T-Model fluctuated from 0.000%to 86.726%,while those in the S-Model and TS-Model fluctuated from 0.000%to 97.826%and from 55.556%to 99.065%,respectively.A stable and high F1 value was found in the TS-Model.Conclusion The multimodal fusion technique was demonstrated to be applicable in the TCM Zhengsu differentiation model,which provided methodological support for developingof a fully intelligent Zhengsu differentiation model with high objective four diagnostic information.