| Title |
Detection approaches for multidrug resistance genes of leukemia
|
|---|---|
| Published in |
Drug Design, Development and Therapy, April 2017
|
| DOI | 10.2147/dddt.s134529 |
| Pubmed ID | |
| Authors |
Ying Du, Baoan Chen |
| Abstract |
Leukemia is a clonal malignant hematopoietic stem cell disease. It is the sixth most lethal cancer and accounts for 4% of all cancers. The main form of treatment for leukemia is chemotherapy. While some cancer types with a higher incidence than leukemia, such as lung and gastric cancer, have shown a sharp decline in mortality rates in recent years, leukemia has not followed this trend. Drug resistance is often regarded as the main clinical obstacle to effective chemotherapy in patients diagnosed with leukemia. Many resistance mechanisms have now been identified, and multidrug resistance (MDR) is considered the most important and prevalent mechanism involved in the failure of chemotherapy in leukemia. In order to reverse MDR and improve leukemia prognosis, effective detection methods are needed to identify drug resistance genes at initial diagnosis. This article provides a comprehensive overview of published approaches for the detection of MDR in leukemia. Identification of relevant MDR genes and methods for early detection of these genes will be needed in order to treat leukemia more effectively. |
X Demographics
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 28 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 21% |
| Student > Bachelor | 3 | 11% |
| Student > Ph. D. Student | 2 | 7% |
| Student > Master | 2 | 7% |
| Student > Doctoral Student | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 14 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 25% |
| Chemistry | 2 | 7% |
| Medicine and Dentistry | 2 | 7% |
| Immunology and Microbiology | 1 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 13 | 46% |
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 02 May 2017.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from Drug Design, Development and Therapy
#1,753
of 2,268 outputs
Outputs of similar age
#284,218
of 323,961 outputs
Outputs of similar age from Drug Design, Development and Therapy
#50
of 59 outputs
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