Application of Chitosan, Chitooligosaccharide, and Their Derivatives in the Treatment of Alzheimer’s Disease
Bibliographic record
Abstract
Classic hypotheses of Alzheimer's disease (AD) include cholinergic neuron death, acetylcholine (ACh) deficiency, metal ion dynamic equilibrium disorder, and deposition of amyloid and tau. Increased evidence suggests neuroinflammation and oxidative stress may cause AD. However, none of these factors induces AD independently, but they are all associated with the formation of Aβ and tau proteins. Current clinical treatments based on ACh deficiency can only temporarily relieve symptoms, accompanied with many side-effects. Hence, searching for natural neuroprotective agents, which can significantly improve the major symptoms and reverse disease progress, have received great attention. Currently, several bioactive marine products have shown neuroprotective activities, immunomodulatory and anti-inflammatory effects with low toxicity and mild side effects in laboratory studies. Recently, chitosan (CTS), chitooligosaccharide (COS) and their derivatives from exoskeletons of crustaceans and cell walls of fungi have shown neuroprotective and antioxidative effects, matrix metalloproteinase inhibition, anti-HIV and anti-inflammatory properties. With regards to the hypotheses of AD, the neuroprotective effect of CTS, COS, and their derivatives on AD-like changes in several models have been reported. CTS and COS exert beneficial effects on cognitive impairments via inhibiting oxidative stress and neuroinflammation. They are also a new type of non-toxic β-secretase and AChE inhibitor. As neuroprotective agents, they could reduce the cell membrane damage caused by copper ions and decrease the content of reactive oxygen species. This review will focus on their anti-neuroinflammation, antioxidants and their inhibition of β-amyloid, acetylcholinesterase and copper ions adsorption. Finally, the limitations and future work will be discussed.
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 | 0.001 | 0.001 |
| 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".