A Humanin Derivative Reduces Amyloid Beta Accumulation and Ameliorates Memory Deficit in Triple Transgenic Mice
Bibliographic record
Abstract
Humanin (HN), a 24-residue peptide, was identified as a novel neuroprotective factor and shows anti-cell death activity against a wide spectrum of Alzheimer's disease (AD)-related cytotoxicities, including exposure to amyloid beta (Abeta), in vitro. We previously demonstrated that the injection of S14G-HN, a highly potent HN derivative, into brain ameliorated memory loss in an Abeta-injection mouse model. To fully understand HN's functions under AD-associated pathological conditions, we examined the effect of S14G-HN on triple transgenic mice harboring APP(swe), tau(P310L), and PS-1(M146V) that show the age-dependent development of multiple pathologies relating to AD. After 3 months of intranasal treatment, behavioral analyses showed that S14G-HN ameliorated cognitive impairment in male mice. Moreover, ELISA and immunohistochemical analyses showed that Abeta levels in brains were markedly lower in S14G-HN-treated male and female mice than in vehicle control mice. We also found the expression level of neprilysin, an Abeta degrading enzyme, in the outer molecular layer of hippocampal formation was increased in S14G-HN-treated mouse brains. NEP activity was also elevated by S14G-HN treatment in vitro. These findings suggest that decreased Abeta level in these mice is at least partly attributed to S14G-HN-induced increase of neprilysin level. Although HN was identified as an anti-neuronal death factor, these results indicate that HN may also have a therapeutic effect on amyloid accumulation in AD.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".