Tropisetron attenuates amyloid‐beta‐induced inflammatory and apoptotic responses in rats
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder featured by deposition of beta-amyloid (Aβ) plaques in the hippocampus and associated cortices and progressive cognitive decline. Tropisetron, a selective 5-HT3 receptor antagonist, is conventionally used to counteract chemotherapy-induced emesis. Recent investigations describe antiphlogistic properties for tropisetron. It has been shown that tropisetron protects against rat embolic stroke. We investigated protective properties of tropisetron in a beta-amyloid (Aβ) rat model of AD and possible involvement of 5-HT3 receptors. MATERIAL AND METHODS: Aβ (1-42) was injected into the hippocampus of male rats. Animals were treated intracerebroventricularly with tropisetron, mCPBG (selective 5-HT3 receptor agonist) or mCPBG plus tropisetron on days 1, 3, 5 and 7. Seven days following Aβ administration, inflammatory markers (TNF-α, COX-2, iNOS and NF-κB), apoptotic markers (caspase 3 cytochrome c release) and calcineurin phosphatase activity were assessed in hippocampus. RESULTS: Seven days following Aβ inoculation, control animals displayed dramatic increase in TNF-α, COX-2, iNOS, NF-κB, active caspase 3, cytochrome c release and calcineurin phosphatase activity in the hippocampus. Tropisetron significantly diminished the elevated levels of these markers and reversed the cognitive deficit. Interestingly, tropisetron was also found to be a potent inhibitor of calcineurin phosphatase activity. The selective 5-HT3 receptor agonist mCPBG, when co-administered with tropisetron, completely reversed the procognitive and anti-apoptotic properties of tropisetron while it could only partially counteract the anti-inflammatory effects. mCPBG alone significantly aggravated Aβ-induced injury. CONCLUSION: Our findings indicate that tropisetron protects against Aβ-induced neurotoxicity in vivo through both 5-HT3 receptor-dependent and independent pathways.
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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 | 0.000 |
| 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".