Acetylsalicylic acid improves cognitive performance in sleep deprived adult Zebrafish (Danio rerio) model
Bibliographic record
Abstract
Sleep deprivation (SD) is commonly associated with decreased attention, reduced responsiveness to external stimuli, and impaired locomotor and cognitive performances. Strong evidence indicates that SD disrupts neuro-immuno-endocrine system which is also linked to cognitive function. Recently Zebrafish have emerged as a powerful model sharing organizational and functional characteristics with other vertebrates, providing great translational relevance with rapid and reliable screening results. In the current study, we examined the effects of acetylsalicylic acid (aspirin) on cognitive and locomotor activity in sleep deprived Zebrafish model. Learning and memory were assessed by T-maze and locomotor activity was assessed by partition preference and swimming time in spinning tasks. Furthermore, brain bioavailability of aspirin was determined by high performance liquid chromatography. Following drug exposure and tasks, histopathology of the brain was performed. It was observed that three-day SD significantly reduces learning and memory and locomotion in the Zebrafish. Aspirin was found to restore SD induced cognitive decline and improve the locomotor functions. Neuro-inflammation and impaired functional network connectivity is linked to cognitive defects, which implicate the possible benefits of immunotherapeutics. In the present study, aspirin decreased neutrophil infiltration, and increased spine density in dentate gyrus granular and shrinkage and basophil in the CA1 neurons of hippocampus. This hints the benefit of aspirin on neuroimmune functions in sleep deprived fish and warrants more studies to establish the clear molecular mechanism behind this protective effect.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".