Effects of Neurotrophic Support and Amyloid-Targeted Combined Therapy on Adult Hippocampal Neurogenesis in a Transgenic Model of Alzheimer's Disease
Bibliographic record
Abstract
Although it is recognized that multi-drug therapies may be necessary to combat AD, there is a paucity of preclinical proof of concept studies. We present a combination treatment paradigm, which temporally affects different aspects of Alzheimer's disease (AD)-like pathology, specifically Aβ-toxicity and neurogenesis. At early stages of AD-like pathology, in TgCRND8 mice, we found that combating Aβ pathology with scyllo-inositol ameliorated deficits in neurogenesis. Older TgCRND8 mice with established amyloid load had decreased progenitor cell proliferation and survival compared to non-transgenic mice, regardless of scyllo-inositol treatment. The prolonged exposure to Aβ-pathology leads to deficits in the neurogenic niche, thus targeting Aβ alone is insufficient to rescue neurogenesis. To support the neurogenic niche, we combined scyllo-inositol treatment with leteprinim potassium (neotrofin), the latter of which stimulates neurotrophin expression. We show that the combination treatment of scyllo-inositol and neotrofin enhances neuronal survival and differentiation. We propose this proof of concept combination therapy of targeting Aβ-pathology and neurotrophin deficits as a potential treatment for 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) | 0.001 | 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".