Objective:To summarize the 10 year clinical experience of treating tibiafibular fractures with rectangle shaped intramedullary nails(RIN). Methods:From January 1987 to December 1996, 4 682 cases (3 278 male and 1 404 female) of tibiafibular fractures from 9 hospitals were treated with RIN . Three kinds of reduction methods including open reduction, semi open reduction and closed reduction were used during operation. Results:Results showed 2 173 cases (62.89%) got excellent result, 947 got good (27.40%), 214 got moderate (6.19%), 121 got poor (3.50%). The total healing rate was 90.29%. Conclusion:RIN has excellent biological characteristics which can provide a flexible interfixation when treating tibiafibular fractures, and the operation is simple, it also can be used for severe open fractures. RIN is one of the good techniques in treating tibiafibular fractures.

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A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.
Drug Delivery Systems ; Proteins ; Transcriptome