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ObjectiveTo analyze the expression of molecular marker affecting the prognosis of acute myeloid leukemia （AML） patients from bioinformatics database， thus providing an experimental basis for further exploration of a novel molecular marker for the prognosis of AML. MethodsThe prognostic data of 179 AML patients from The Cancer Genome Atlas （TCGA） database were examined for differential gene analysis and survival analysis. The bone marrow samples of 74 healthy individuals （HI） and 542 de novo AML patients in the dataset GSE13159 downloaded from the Gene Expression Omnibus （GEO） database were analyzed to detect the difference in the expression levels of differential target genes. Peripheral blood and bone marrow samples were collected from 18 de novo AML patients and 20 age- and gender-matched healthy controls， and real-time fluorescent quantitative PCR was used to validate the expression levels of the differential genes in the AML patients. ResultsBioinformatics data analysis showed that the optimal cut-off value of Homo sapiens NK2 homeobox 3 （NKX2-3） calculated by R language was 0.051. Survival analysis revealed a statistically poorer overall survival in de novo AML patients with high NKX2-3 expression than in those with low NKX2-3 expression （P = 0.0036）. NKX2-3 was highly expressed in patients with de novo AML than in HI and the difference was statistically significant （P < 0.001）. Real-time fluorescence quantitative PCR verified the expression levels of the NKX2-3 gene in AML patients and confirmed that compared with those in HI， in the de novo AML patients， NKX2-3-1 and NKX2-3-2 were highly expressed and were significantly correlated （P = 0.000， P = 0.000）. ConclusionNKX2-3 is highly expressed in de novo AML patients， and the AML patients with high NKX2-3 expression have poor overal survival. NKX2-3 may be closely related to the clinical outcome and prognosis of AML.
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Effective medical image enhancement method can not only highlight the interested target and region, but also suppress the background and noise, thus improving the quality of the image and reducing the noise while keeping the original geometric structure, which contributes to easier diagnosis in disease based on the image enhanced. This article carries out research on strengthening methods of subtle structure in medical image nowadays, including images sharpening enhancement, rough sets and fuzzy sets, multi-scale geometrical analysis and differential operator. Finally, some commonly used quantitative evaluation criteria of image detail enhancement are given, and further research directions of fine structure enhancement of medical images are discussed.