• No results found

Clinical features of De Novo acute myeloid leukemia with concurrent DNMT3A, FLT3 and NPM1 mutations

N/A
N/A
Protected

Academic year: 2020

Share "Clinical features of De Novo acute myeloid leukemia with concurrent DNMT3A, FLT3 and NPM1 mutations"

Copied!
10
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

Figure 1 Mutation subgroups of de novo acute myeloid leukemia included in the study.
Table 1 Demographic, clinical and laboratory characteristics of patients in the study group
Figure 2 Schematic representation of mutations. (a) Mutation status of genes assessed in all patients in the study group
Figure 3 Estimated DNMT3A, FLT3, and NPM1 variant (allelic) frequencies in a subset of de novo acute myeloid leukemia samplesharboring all three mutations.
+3

References

Related documents

The study was aimed: to determine the social demographic profile of severely malnourished children and/or parents of children with severe acute malnutrition, to

Victor Melgar is a coffee farmer in Guatemala, Central America. Given current low coffee prices, he is worried that he may not be able to maintain the family tradition of

In another paper on adaptation to climate stress in the area, we show that households cultivate both improved and local varieties and that seeds originally sourced from the

This current ISSP Data Report – Religious Attitudes and Religious Change examines data collected at three different points over 17 years, from up to 42 ISSP member

In this study, we assessed the sensitivity of Interferon gamma release assay (IGRA) in active tuberculosis patients who were positive for HIV infection and compared it with that

Classification and characterization of composite materials; mechanical behavior of composite materials; stress-strain relation for anisotropic materials; invariant properties of

New solar power plants will be constructed only if the prices for the delivered solar electricity and solar fresh water are substantially higher than the local prices, because the

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising