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BackgroundThe aim of this study was to explore the source and morphology of a small supernumerary marker chromosome (sSMC) from karyotype analysis of a patient with a unique case of mosaic Turner syndrome. The study findings will provide technical reference and genetic counseling for similar cases.Case PresentationA female patient with 46,X,+mar karyotype was diagnosed by genetic karyotype analysis. Genetic methods including fluorescence in situ hybridization (FISH) and copy number variation sequencing (CNV-seq) based on low-depth whole-genome sequencing were used to explore the source and morphology of sSMC. FISH technology showed that 56.5% of the cells were X and 43.5% of the cells were XY. CNV-seq detection found that the sSMC was chrY, implying that the patient's karyotype was mos 45,X[58.6%]/46,XY[41.4%]. Retrospective karyotype analysis indicated that the female patient's sSMC was inherited from her father's small chrY. Customized FISH probe of Yq12 microdeletion was positive, indicating that the sSMC was a del(Y)(q12). Based on the results of genetic diagnosis, the specialist doctor gave a comprehensive genetic consultation and ordered regular follow-up examinations.ConclusionsThe findings of the current study showed that the chromosome description of the unique Turner case was mos 45,X[56.5%]/46,X,del(Y)(q12)[43.5%]. FISH technology played a key role in diagnosis of mosaicism. The terminal deletion of mosaic chrY provided a scientific and an accurate explanation for masculinity failure and abnormal sexual development of the current case.

With the continuous development of deep learning technology, computers can not only understand the natural language input by users, but also reply to the sentences used for input. This paper designs an intelligent chat robot based on Attention mechanism and Seq2Seq model. It uses jieba word segmentation tool and Word2vec to convert sentences in corpus into semantic vectors, and then trains and tests the model through TensorFlow platform. The experiment compares whether the Attention mechanism is added or not, and proves that the model added with Attention mechanism has better effect than the traditional Seq2Seq model, and performs better in actual dialogue.