P4–382: Beneficial effects of AC1203 in a dog model of human aging and dementia
Bibliographic record
Abstract
Dogs demonstrate an Alzheimer's disease (AD)–like syndrome that partially models human AD. Key features exhibited in the dog include progressive cognitive decline and neuropathological changes that parallel those seen in AD patients. Thus, dogs provide a suitable model for screening the effectiveness of therapies for AD and age associated memory impairment (AAMI). In addition, such therapies could be useful to improve the quality of life of companion animals. Decreased cerebral glucose metabolism is seen in both normal aging and Alzheimer's disease (AD) and may contribute to cognitive decline in both conditions. The present study determined the efficacy of AC1203, a proprietary pro–drug, food additive, in an aged dog model of human aging and dementia. AC1023 is designed to be metabolized by the liver and provide an alternate energy source to glucose–deprived neurons. Providing metabolic substrates to neurons may improve several facets of impaired neuronal function. We examined cognitive changes in aged dogs receiving 2 doses of AC1203 for a period of six months. Dogs receiving 2 g/kg/day of the additive showed improved visuospatial working memory function and long–term memory, which persisted for approximately 1 month after treatment cessation. Aged dogs receiving 2 g/kg/day of AC1203 also showed improved motor function and improved cerebrovascular integrity. In addition we noted improvement in several markers of oxidative stress and biochemical function. Combined, these findings suggest that AC1203 can attenuate the cognitive symptoms of dementia in a dog model of human aging.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".