Objective To investigate the diagnostic value of low field MRI for adenomyosis. Methods MRI features of adenomyosis pathologically proved in 18 cases were retrospectively analysed.Results 15 cases were diffusive adenomyosis,the junctional zones of uterus were exteusive thickened to 12.0～32.6 mm,mean 16.2 mm,diffusive high signal intensity distributed over in myometrium which was low signal intensity in 11 cases,it was typical “snowing sign”,lower signal intensity in the myometrium in another 4 cases on T 2WI and fat-suppression imaging. A little high signal intensity was found in 6 cases on T 1WI. 3 cases were focal adenomyosis(adenomyoma), 4 lesions totally. The adenomyoma’s boundaries were not distinct and their shapes were roundish or irregular. The lesions were low signal intensity or diffusive high signal intensity distributed in the low signal intensity fields on T 2WI and fat-suppression imaging. A little high signal intensity was found in 2 lesions on T 1WI. Conclusion T 2WI and fat-suppression imaging of low field MRI are very useful techniques of the diagnosis of adenomyosis.

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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