Zebrafish (Danio rerio) as a model for the study of aging and exercise: Physical ability and trainability decrease with age
Bibliographic record
Abstract
A rapidly aging global population has motivated the development and use of models for human aging. Studies on aging have shown parallels between zebrafish and humans at the internal organization level; however, few parallels have been studied at the whole-organism level. Furthermore, the effectiveness of exercise as a method to mitigate the effects of aging has not been studied in zebrafish. We investigated the effects of aging and intermittent exercise on swimming performance, kinematics and behavior. Young, middle-aged and old zebrafish (20-29, 36-48 and 60-71% of average lifespan, respectively) were exercised to exhaustion in endurance and sprint swimming tests once a week for four weeks. Both endurance and sprint performance decreased with increased age. Swimming performance improved with exercise training in young and middle-aged zebrafish, but not in old zebrafish. Tail-beat amplitude, which is akin to stride length in humans, increased for all age groups with training. Zebrafish turning frequency, which is an indicator of routine activity, decreased with age but showed no change with exercise. In sum, our results show that zebrafish exhibit a decline in whole-organism performance and trainability with age. These findings closely resemble the senescence-related declines in physical ability experienced by humans and mammalian aging models and therefore support the use of zebrafish as a model for human exercise and aging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".