Chronic Dietary Selenomethionine Exposure Induces Oxidative Stress, Dopaminergic Dysfunction, and Cognitive Impairment in Adult Zebrafish (<i>Danio rerio</i>)
Bibliographic record
Abstract
The present study was designed to investigate the effects of chronic dietary exposure to selenium (Se) on zebrafish cognition and also to elucidate possible mechanism(s) by which Se exerts its neurotoxicity. To this end, adult zebrafish were exposed to different concentrations of dietary l-selenomethionine (control, 2.3, 9.7, 32.5, or 57.7 μg Se/g dry weight) for 30 days. Cognitive performance of fish was tested using a latent learning paradigm in a complex maze. In addition, we also evaluated oxidative stress biomarkers and the expression of genes involved in dopaminergic neurotransmission in the zebrafish brain. Fish treated with higher dietary Se doses (32.5 and 57.5 μg Se/g) exhibited impaired performance in the latent learning task. The impaired learning was associated with the induction of oxidative stress and altered mRNA expression of dopamine receptors, tyrosine hydroxylase, and dopamine transporter genes in the zebrafish brain. Collectively, our results illustrate that cognitive impairment in zebrafish could be associated with Se-induced oxidative stress and altered dopaminergic neurotransmission in the brain.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".