A nasal proteosome adjuvant activates microglia and prevents amyloid deposition
Bibliographic record
Abstract
OBJECTIVE: We assessed whether peripheral activation of microglia by a nasal proteosome-based adjuvant (Protollin) that has been given safely to humans can prevent amyloid deposition in young mice and affect amyloid deposition and memory function in old mice with a large amyloid load. METHODS: Amyloid precursor protein (APP) transgenic (Tg) J20 mice received nasal treatment with Protollin weekly for 8 months beginning at age 5 months. Twenty-four-month-old J20 mice were treated weekly for 6 weeks. RESULTS: We found reduction in the level of fibrillar amyloid (93%), insoluble beta-amyloid (Abeta; 68%), and soluble Abeta (45%) fragments in 14-month-old mice treated with Protollin beginning at age 5 months. Twenty-four-month-old mice treated with nasal Protollin for 6 weeks had decreased soluble and insoluble Abeta (1-40) and (1-42) and improved memory function. Activated microglia (CD11b+ cells) colocalized with Abeta fibrils in the 24-month-old animals, and microglial activation correlated with the decrease in Abeta. No microglial activation was observed in 14-month-old mice, suggesting that once Abeta is cleared, there is downregulation of microglial activation. Both groups had reduction in astrocytosis. Protollin was observed in the nasal cavity and cervical lymph node but not in the brain. Activated CD11b+SRA+ (scavenger receptor A) cells were found in blood and cervical lymph node and increased interleukin-10 in cervical lymph node. No toxicity was associated with treatment. INTERPRETATION: Our results demonstrate a novel antibody-independent immunotherapy for both prevention and treatment of Alzheimer's disease that is mediated by peripheral activation of microglia with no apparent toxicity.
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) | 0.001 | 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".