Developing a novel peptide aptamer‐based treatment of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is a major cause of dementia and age‐related neurodegenerative disease with no current treatment. Pathologically, AD is characterized primarily by accumulation of amyloid‐β (Aβ) in the brain. The interaction of Aβ‐oligomers (Aβo) with the cellular prion protein (PrPC) subsequently mediated AD pathologies. The interference with Aβo‐PrPC interaction is a valuable strategy for developing therapeutics for AD. Previously, we developed peptide aptamers (PAs) binding to the PrPC, partially covering the binding site of Aβo . In this study, we aimed to investigate the PAs effects on preventing Aβo‐PrPC interaction and toxicity in vitro and pathologies in AD models. Method Wild type mouse neuroblastoma N2a cells (overexpressing mouse PrP) were treated with Aβ (1 mM) and PAs (10 µg/ml), and after 24 hr performed the MTT assay. Following these in vitro experiments, we carried out in vivo experiments using transgenic 5xFAD (expressing human APP and PSEN1 transgenes harboring in total 5 mutations associated with familial AD) mice. The 5xFAD mice were treated with PAs at a 14.4 ug /day dosage for 6 weeks by intraventricular infusion using Alzet® osmotic pumps. After completion of treatment, fear conditioning test (FCT) was performed for consolidated leaning and memory functions of the 5xFAD mice. Result The MTT results indicated that PA treatment significantly reduced Aβo‐induced toxicity. Overall, our in vitro results indicated that PAs reduced Aβ‐induced neurotoxicity. In the FCT, we observed that PAs increased the time of freezing percentage as comparted to the non‐treated 5xFAD mice. The FCT results represented that PAs significantly improved consolidated learning and memory functions of 5xFAD mice as compared to the non‐treated 5xFAD mice. Conclusion Our results demonstrate that treatment with PAs targeting Aβo‐PrPC interaction would be valuable and novel strategy to treat 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) | 0.000 | 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".