How to train a mouse-methodological issues in pre-clinical exercise oncology.

Exercise oncology dose-response human relevance rodent models translational research

Journal

American journal of cancer research
ISSN: 2156-6976
Titre abrégé: Am J Cancer Res
Pays: United States
ID NLM: 101549944

Informations de publication

Date de publication:
2019
Historique:
received: 07 05 2019
accepted: 18 05 2019
entrez: 10 7 2019
pubmed: 10 7 2019
medline: 10 7 2019
Statut: epublish

Résumé

We point at several challenges that current exercise oncology rodent models face, which call their human-relevance into question: the vast majority of pre-clinical studies in exercise oncology treat "physical exercise" as a primitive concept without further analysis or qualification, and their results are based on dosages that no human can endure. The lack of analysis and qualification together with the dosage mismatch conceal the fact that rodents do not run like humans. Consequently, while these pre-clinical studies may yield insights into potential biological mechanisms underlying the systemic effects of physical exercise on cancer, the applicability of this knowledge to preventive interventions in healthy humans and the ability to translate it to practical therapies in the critically ill remain limited. We propose an alternative exercise rodent model that has better chances of meeting these challenges.

Identifiants

pubmed: 31285956
pmc: PMC6610064

Types de publication

Journal Article

Langues

eng

Pagination

1246-1253

Déclaration de conflit d'intérêts

None.

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Auteurs

Luma Melo (L)

Department of Environmental Health, School of Public Health, Indiana University Bloomington, IN 47405, USA.
Department of History & Philosophy of Science & Medicine, Indiana University Bloomington, IN, USA.

Amit Hagar (A)

Department of Environmental Health, School of Public Health, Indiana University Bloomington, IN 47405, USA.
Department of Intelligent Systems Engineering, Indiana University Bloomington, IN, USA.
Department of History & Philosophy of Science & Medicine, Indiana University Bloomington, IN, USA.

Classifications MeSH