The regulation of pituitary-thyroid abnormalities by peripheral administration of levothyroxine increased brain-derived neurotrophic factor and reelin protein expression in an animal model of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is associated with decreased serum levels of thyroid hormones (THs), increased levels of thyroid-stimulating hormone (TSH), and decreased protein expression of brain-derived neurotrophic factor (BDNF) and reelin in the hippocampus. In this study, we have evaluated the effect of subcutaneous administration of levothyroxine (L-T4) on levels of THs and TSH as well as protein expression of BDNF and reelin in AD rats. To make an animal model of AD, amyloid-beta peptide (Aβ) plus ibotenic acid were infused intrahippocampally, and rats were treated with L-T4 and (or) saline for 10 days. The levels of THs and TSH were measured by ELISA kits. Protein synthesis was detected by Western blotting method. Results have been shown that serum level of THs, BDNF, and reelin protein expression in the hippocampus were significantly decreased (P < 0.001) in AD animals and elevated significantly in AD rats treated with L-T4 (P < 0.01). Data showed that TSH level significantly decreased in AD rats treated with L-T4 (P < 0.05). These findings indicated that L-T4 increased BDNF and reelin protein expression by regulation of serum THs and TSH level in Aβ-induced AD rats.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".