Cellulose ether treatment inhibits amyloid beta aggregation, neuroinflammation and cognitive deficits in transgenic mouse model of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is an incurable, progressive and devastating neurodegenerative disease. Pathogenesis of AD is associated with the aggregation and accumulation of amyloid beta (Aβ), a major neurotoxic mediator that triggers neuroinflammation and memory impairment. Recently, we found that cellulose ether compounds (CEs) have beneficial effects against prion diseases by inhibiting protein misfolding and replication of prions, which share their replication mechanism with Aβ. CEs are FDA-approved safe additives in foods and pharmaceuticals. Herein, for the first time we determined the therapeutic effects of the representative CE (TC-5RW) in AD using in vitro and in vivo models. Our in vitro studies showed that TC-5RW inhibits Aβ aggregation, as well as neurotoxicity and immunoreactivity in Aβ-exposed human and murine neuroblastoma cells. In in vivo studies, for the first time we observed that single and weekly TC-5RW administration, respectively, improved memory functions of transgenic 5XFAD mouse model of AD. We further demonstrate that TC-5RW treatment of 5XFAD mice significantly inhibited Aβ oligomer and plaque burden and its associated neuroinflammation via regulating astrogliosis, microgliosis and proinflammatory mediator glial maturation factor beta (GMFβ). Additionally, we determined that TC-5RW reduced lipopolysaccharide-induced activated gliosis and GMFβ in vitro. In conclusion, our results demonstrate that CEs have therapeutic effects against Aβ pathologies and cognitive impairments, and direct, potent anti-inflammatory activity to rescue neuroinflammation. Therefore, these FDA-approved compounds are effective candidates for developing therapeutics for AD and related neurodegenerative diseases associated with protein misfolding.
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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) | 0.000 | 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 | 0.000 | 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".