Oral treatment with the all-d-peptide RD2 enhances cognition in aged beagle dogs – A model of sporadic Alzheimer’s disease
Bibliographic record
Abstract
Disease-modifying therapies to treat Alzheimer's disease (AD) are of fundamental interest for aging humans, societies, and health care systems. Predictable disease progression in transgenic AD models favors preclinical studies employing a preventive study design with an early pre-symptomatic treatment start, instead of assessing a truly curative approach with treatment starting after diagnosed disease onset. The aim of this study was to investigate the pharmacokinetic profile and efficacy of RD2 to enhance short-term memory and cognition in cognitively impaired aged Beagle dogs - a non-transgenic model of truly sporadic AD. RD2 has previously demonstrated pharmacodynamic efficacy in three different transgenic AD mouse models in three different laboratories. Here, we demonstrate that oral treatment with RD2 significantly reduced cognitive deficits in cognitively impaired aged Beagle dogs even beyond the treatment end, which suggests in combination with the treatment dependent CSF tau oligomer decrease a disease-modifying effect of RD2 treatment.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".