Intranasal Paclitaxel Alters Alzheimer’s Disease Phenotypic Features in 3xTg-AD Mice
Bibliographic record
Abstract
BACKGROUND: Microtubule stabilizing drugs, commonly used as anti-cancer therapeutics, have been proposed for treatment of Alzheimer's disease (AD); however, many do not cross the blood-brain barrier. OBJECTIVE: This research investigated if paclitaxel (PTX) delivered via the intranasal (IN) route could alter the phenotypic progression of AD in 3xTg-AD mice. METHODS: We administered intranasal PTX in 3XTg-AD mice (3xTg-AD n = 15, 10 weeks and n = 10, 44 weeks, PTX: 0.6 mg/kg or 0.9%saline (SAL)) at 2-week intervals. After treatment, 3XTg-AD mice underwent manganese-enhanced magnetic resonance imaging to measure in vivo axonal transport. In a separate 3XTg-AD cohort, PTX-treated mice were tested in a radial water tread maze at 52 weeks of age after four treatments, and at 72 weeks of age, anxiety was assessed by an elevated-plus maze after 14 total treatments. RESULTS: PTX increased axonal transport rates in treated 3XTg-AD compared to controls (p≤0.003). Further investigation using an in vitro neuron model of Aβ-induced axonal transport disruption confirmed PTX prevented axonal transport deficits. Confocal microscopy after treatment found fewer phospho-tau containing neurons (5.25±3.8 versus 8.33±2.5, p < 0.04) in the CA1, altered microglia, and reduced reactive astrocytes. PTX improved performance of 3xTg-AD on the water tread maze compared to controls and not significantly different from WT (Day 5, 143.8±43 versus 91.5±77s and Day 12, 138.3±52 versus 107.7±75s for SAL versus PTX). Elevated plus maze revealed that PTX-treated 3xTg-AD mice spent more time exploring open arms (Open arm 129.1±80 versus 20.9±31s for PTX versus SAL, p≤0.05). CONCLUSION: Taken collectively, these findings indicate that intranasal-administered microtubule-stabilizing drugs may offer a potential therapeutic option for treating AD.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".