A systematic review of exercise protocols applied to athymic mice in tumor-related experiments
Bibliographic record
Abstract
Athymic mice are unable to produce T-cells and are then characterized as immunodeficient. This characteristic makes these animals ideal for tumor biology and xenograft research. New non-pharmacological therapeutics are required owing to the exponential increase in global oncology costs over the last 10 years and the high cancer mortality rate. In this sense, physical exercise is regarded as a relevant component of cancer treatment. However, the scientific community lacks information regarding the effect of manipulating training variables on cancer in humans, and experiments with athymic mice. Therefore, this systematic review aimed to address the exercise protocols used in tumor-related experiments using athymic mice. The PubMed, Web of Science, and Scopus databases were searched without restrictions on published data. A combination of key terms such as athymic mice, nude mice, physical activity, physical exercise, and training was used. The database search retrieved 852 studies (PubMed, 245; Web of Science, 390; and Scopus, 217). After title, abstract, and full-text screening, 10 articles were eligible. Based on the included studies, this report highlights the considerable divergences in the training variables adopted for this animal model. No studies have reported the determination of a physiological marker for intensity individualization. Future studies are recommended to explore whether invasive procedures can result in pathogenic infections in athymic mice. Moreover, time-consuming tests cannot be applied to experiments with specific characteristics such as tumor implantation. In summary, non-invasive, low-cost, and time-saving approaches can suppress these limitations and improve the welfare of these animals during experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".