Ask the Experts Special Focus Issue: Special Populations
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Bioanalysis in special populations
As part of this special focus issue, Bioanalysis invited a selection of leading researchers to express their views on bioanalysis in special populations. The topics covered include a discussion of specific population-related factors that can affect sample analysis, technological advances and challenges relating to matrix effects. Their responses provide a valuable insight into current considerations, as well as future developments in the field.
Ian Blair, University of Pennsylvania School of Medicine, PA, USA [email protected]
cancer studies have used controls that were sisters of the patients. This was an excellent approach because the control subjects had a closer genetic background to the cancer group.
QQ In your opinion, over the past 5–10 years, which new technologies have helped the analysis of samples from special populations?
QQ What are some specific populationrelated factors (e.g., ethic groups, gender, age) that can affect sample analysis & which tests are recommended? The ability to obtain samples from agematched controls is extremely challenging, particularly for diseases that are more prevalent in older subjects such as cancer, cardiovascular disease and neurodegenerative disease. In fact, many of the published studies simply rely on control groups that are not matched appropriately for age. A major problem can be finding control subjects that are not only disease-free but also drug-free. The only real solution to this problem is to establish collaborations with physician scientists who are involved in the treatment of a particular disease. They will be able to establish whether the control subjects really are suitable. Some breast
10.4155/BIO.14.282 © 2014 Future Science Ltd
A major factor in the analysis of endogenous hormones such as estrogens in populations at risk for breast cancer relates to their low concentration. For example, concentrations of 17β-estradiol in the serum of postmenopausal women are typically
For reprint orders, please contact [email protected]
Bioanalysis in special populations
As part of this special focus issue, Bioanalysis invited a selection of leading researchers to express their views on bioanalysis in special populations. The topics covered include a discussion of specific population-related factors that can affect sample analysis, technological advances and challenges relating to matrix effects. Their responses provide a valuable insight into current considerations, as well as future developments in the field.
Ian Blair, University of Pennsylvania School of Medicine, PA, USA [email protected]
cancer studies have used controls that were sisters of the patients. This was an excellent approach because the control subjects had a closer genetic background to the cancer group.
QQ In your opinion, over the past 5–10 years, which new technologies have helped the analysis of samples from special populations?
QQ What are some specific populationrelated factors (e.g., ethic groups, gender, age) that can affect sample analysis & which tests are recommended? The ability to obtain samples from agematched controls is extremely challenging, particularly for diseases that are more prevalent in older subjects such as cancer, cardiovascular disease and neurodegenerative disease. In fact, many of the published studies simply rely on control groups that are not matched appropriately for age. A major problem can be finding control subjects that are not only disease-free but also drug-free. The only real solution to this problem is to establish collaborations with physician scientists who are involved in the treatment of a particular disease. They will be able to establish whether the control subjects really are suitable. Some breast
10.4155/BIO.14.282 © 2014 Future Science Ltd
A major factor in the analysis of endogenous hormones such as estrogens in populations at risk for breast cancer relates to their low concentration. For example, concentrations of 17β-estradiol in the serum of postmenopausal women are typically
