Inositol Stereoisomers Stabilize an Oligomeric Aggregate of Alzheimer Amyloid β Peptide and Inhibit Aβ-induced Toxicity
Bibliographic record
Abstract
Inositol has 8 stereoisomers, four of which are physiologically active. myo-Inositol is the most abundant isomer in the brain and more recently shown that epi- and scyllo-inositol are also present. myo-Inositol complexes with Abeta42 in vitro to form a small stable micelle. The ability of inositol stereoisomers to interact with and stabilize small Abeta complexes was addressed. Circular dichroism spectroscopy demonstrated that epi- and scyllo- but not chiro-inositol were able to induce a structural transition from random to beta-structure in Abeta42. Alternatively, none of the stereoisomers were able to induce a structural transition in Abeta40. Electron microscopy demonstrated that inositol stabilizes small aggregates of Abeta42. We demonstrate that inositol-Abeta interactions result in a complex that is non-toxic to nerve growth factor-differentiated PC-12 cells and primary human neuronal cultures. The attenuation of toxicity is the result of Abeta-inositol interaction, as inositol uptake inhibitors had no effect on neuronal survival. The use of inositol stereoisomers allowed us to elucidate an important structure-activity relationship between Abeta and inositol. Inositol stereoisomers are naturally occurring molecules that readily cross the blood-brain barrier and may represent a viable treatment for AD through the complexation of Abeta and attenuation of Abeta neurotoxic effects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".