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Long-term treatment effects in chronic myeloid leukemia

Overview of attention for article published in Journal of Mathematical Biology, January 2017
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Title
Long-term treatment effects in chronic myeloid leukemia
Published in
Journal of Mathematical Biology, January 2017
DOI 10.1007/s00285-017-1098-5
Pubmed ID
Authors

Apollos Besse, Thomas Lepoutre, Samuel Bernard

Abstract

We propose and analyze a simplified version of a partial differential equation (PDE) model for chronic myeloid leukemia (CML) derived from an agent-based model proposed by Roeder et al. This model describes the proliferation and differentiation of leukemic stem cells in the bone marrow and the effect of the drug Imatinib on these cells. We first simplify the PDE model by noting that most of the dynamics occurs in a subspace of the original 2D state space. Then we determine the dominant eigenvalue of the corresponding linearized system that controls the long-term behavior of solutions. We mathematically show a non-monotonous dependence of the dominant eigenvalue with respect to treatment dose, with the existence of a unique minimal negative eigenvalue. In terms of CML treatment, this shows that there is a unique dose that maximizes the decay rate of the CML tumor load over long time scales. Moreover this unique dose is lower than the dose that maximizes the initial tumor load decay. Numerical simulations of the full model confirm that this phenomenon is not an artifact of the simplification. Therefore, while optimal asymptotic dosage might not be the best one at short time scales, our results raise interesting perspectives in terms of strategies for achieving and improving long-term deep response.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 21%
Student > Ph. D. Student 3 21%
Student > Doctoral Student 2 14%
Lecturer 1 7%
Professor 1 7%
Other 2 14%
Unknown 2 14%
Readers by discipline Count As %
Mathematics 7 50%
Earth and Planetary Sciences 1 7%
Psychology 1 7%
Unknown 5 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 January 2017.
All research outputs
#17,847,122
of 22,947,506 outputs
Outputs from Journal of Mathematical Biology
#417
of 657 outputs
Outputs of similar age
#292,184
of 419,016 outputs
Outputs of similar age from Journal of Mathematical Biology
#8
of 9 outputs
Altmetric has tracked 22,947,506 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 657 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one.