Macromolecular nanoparticles to attenuate both reactive oxygen species and inflammatory damage for treating Alzheimer's disease
Bibliographic record
Abstract
Prevention and early intervention are the current focus of treatment for Alzheimer's disease (AD). An increase in reactive oxygen species (ROS) is a feature of the early stages of AD, thus suggesting that the removal of excess ROS can be a viable method of improving AD. Natural polyphenols are able to scavenge ROS and thus promising for treating AD. However, some issues need to be addressed. Among them, important are that most polyphenols are hydrophobic, have low bioavailability in the body, are easily degraded, and that single polyphenols have insufficient antioxidant capacity. In this study, we employed two polyphenols, resveratrol (RES) and oligomeric proanthocyanidin (OPC), and creatively grafted them with hyaluronic acid (HA) to form nanoparticles to address the aforementioned issues. Meanwhile, we strategically grafted the nanoparticles with the B6 peptide, enabling the nanoparticles to cross the blood-brain barrier (BBB) and enter the brain for AD treatment. Our results illustrate that B6-RES-OPC-HA nanoparticles can significantly scavenge ROS, reduce brain inflammation, and improve learning and memory ability in AD mice. B6-RES-OPC-HA nanoparticles have the potential to prevent and alleviate early 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 | 0.000 |
| 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".