Combined treatment with the microdose lithium formulation NP03 and S‐adenosylmethionine reduces cognitive decline and Alzheimer’s‐like amyloid pathology in Mcgill‐R‐Thy1‐APP rats
Bibliographic record
Abstract
Abstract Background By engaging several targets simultaneously, combination therapeutics hold promise for the prevention and treatment of complex conditions such as Alzheimer’s disease. We have previously shown that monotherapy using either NP03, a microemulsion formulation of lithium, which can reach the CNS when applied on mucosae, or S‐adenosylmethionine (SAM), a global methyl donor, can blunt amyloid‐driven pathology and cognitive impairment. Here, we explored the potential therapeutic value of a combination of NP03 and SAM in pre‐plaque McGill‐R‐Thy1‐APP transgenic (Tg) rats. Method We administered NP03 (rectal administration; 40 ug Li/Kg; 5 days per week), SAM (orally; 20 mg/Kg; 3 days per week), a combination of NP03‐SAM, or vehicle to 3‐month‐old Tg and non‐Tg control rats for 12 weeks. Behavioral testing and tissue harvest were performed 3 weeks after ceasing treatment. Results Combined NP03‐SAM treatment had a greater impact in reversing cognitive impairments in the auditory fear‐conditioning task compared with either the NP03 or SAM treatment alone. Moreover, NP03‐SAM‐treated McGill‐APP rats exhibited lower abundance of amyloid‐β peptides. Beneficial effects of NP03‐SAM treatment also included a reduction in neuroinflammation, BACE 1 expression and activity, and global DNA demethylation. Furthermore, combined treatment amplified the rescue of neurogenesis already observed in NP03‐treated rats. Conclusion Our findings provide preclinical evidence suggesting that combined treatment with NP03 and SAM may be of therapeutic value in the early stages of Alzheimer’s disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".