↓ Skip to main content

Personalized or Precision Medicine? The Example of Cystic Fibrosis

Overview of attention for article published in Frontiers in Pharmacology, January 2017
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

twitter
6 X users

Citations

dimensions_citation
60 Dimensions

Readers on

mendeley
149 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Personalized or Precision Medicine? The Example of Cystic Fibrosis
Published in
Frontiers in Pharmacology, January 2017
DOI 10.3389/fphar.2017.00390
Pubmed ID
Authors

Fernando A. L. Marson, Carmen S. Bertuzzo, José D. Ribeiro

Abstract

The advent of the knowledge on human genetics, by the identification of disease-associated variants, culminated in the understanding of human variability. With the genetic knowledge, the specificity of the clinical phenotype and the drug response of each individual were understood. Using the cystic fibrosis (CF) as an example, the new terms that emerged such as personalized medicine and precision medicine can be characterized. The genetic knowledge in CF is broad and the presence of a monogenic disease caused by mutations in the CFTR gene enables the phenotype-genotype association studies (including the response to drugs), considering the wide clinical and laboratory spectrum dependent on the mutual action of genotype, environment, and lifestyle. Regarding the CF disease, personalized medicine is the treatment directed at the symptoms, and this treatment is adjusted depending on the patient's phenotype. However, more recently, the term precision medicine began to be widely used, although its correct application and understanding are still vague and poorly characterized. In precision medicine, we understand the individual as a response to the interrelation between environment, lifestyle, and genetic factors, which enabled the advent of new therapeutic models, such as conventional drugs adjustment by individual patient dosage and drug type and response, development of new drugs (read through, broker, enhancer, stabilizer, and amplifier compounds), genome editing by homologous recombination, zinc finger nucleases, TALEN (transcription activator-like effector nuclease), CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats-CRISPR-associated endonuclease 9), and gene therapy. Thus, we introduced the terms personalized medicine and precision medicine based on the CF.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users 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 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 149 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 31 21%
Student > Master 20 13%
Student > Ph. D. Student 19 13%
Other 12 8%
Researcher 12 8%
Other 14 9%
Unknown 41 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 26%
Medicine and Dentistry 34 23%
Agricultural and Biological Sciences 12 8%
Pharmacology, Toxicology and Pharmaceutical Science 6 4%
Computer Science 3 2%
Other 9 6%
Unknown 47 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 21 June 2017.
All research outputs
#12,749,177
of 22,979,862 outputs
Outputs from Frontiers in Pharmacology
#3,427
of 16,262 outputs
Outputs of similar age
#195,162
of 421,122 outputs
Outputs of similar age from Frontiers in Pharmacology
#52
of 170 outputs
Altmetric has tracked 22,979,862 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 16,262 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,122 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 170 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.