GM1-Modified Lipoprotein-like Nanoparticle: Multifunctional Nanoplatform for the Combination Therapy of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) exerts a heavy health burden for modern society and has a complicated pathological background. The accumulation of extracellular β-amyloid (Aβ) is crucial in AD pathogenesis, and Aβ-initiated secondary pathological processes could independently lead to neuronal degeneration and pathogenesis in AD. Thus, the development of combination therapeutics that can not only accelerate Aβ clearance but also simultaneously protect neurons or inhibit other subsequent pathological cascade represents a promising strategy for AD intervention. Here, we designed a nanostructure, monosialotetrahexosylganglioside (GM1)-modified reconstituted high density lipoprotein (GM1-rHDL), that possesses antibody-like high binding affinity to Aβ, facilitates Aβ degradation by microglia, and Aβ efflux across the blood-brain barrier (BBB), displays high brain biodistribution efficiency following intranasal administration, and simultaneously allows the efficient loading of a neuroprotective peptide, NAP, as a nanoparticulate drug delivery system for the combination therapy of AD. The resulting multifunctional nanostructure, αNAP-GM1-rHDL, was found to be able to protect neurons from Aβ(1-42) oligomer/glutamic acid-induced cell toxicity better than GM1-rHDL in vitro and reduced Aβ deposition, ameliorated neurologic changes, and rescued memory loss more efficiently than both αNAP solution and GM1-rHDL in AD model mice following intranasal administration with no observable cytotoxicity noted. Taken together, this work presents direct experimental evidence of the rational design of a biomimetic nanostructure to serve as a safe and efficient multifunctional nanoplatform for the combination therapy of AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".