Label‐free distribution of anti‐amyloid D‐AIP in <i>Drosophila melanogaster</i>: prevention of Aβ42‐induced toxicity without side effects in transgenic flies
Bibliographic record
Abstract
Soluble oligomers of the 42-amino acid amyloid beta (Aβ42) peptide are highly toxic and suspected as the causative agent of synaptic dysfunction and neuronal loss in Alzheimer's disease (AD). Previously, we have shown that a small, D-amino acid Aβ42-oligomer interacting peptide (D-AIP) can neutralize human Aβ42-mediated toxicity using in vitro and cell-based assays. In the present longitudinal study using a transgenic Drosophila melanogaster model, advanced live confocal imaging and mass spectrometry imaging (MALDI-MSI) showed that the eight amino acid D-AIP can attenuate Aβ42-induced toxicity in vivo. By separating male and female flies into distinct groups, the resultant distribution of ingested D-AIP was different between the sexes. The Aβ42-induced 'rough eye' phenotype could be rescued in the female transgenics, likely because of the co-localization of D-AIP with human Aβ42 in the female fly heads. Interestingly, the phenotype could not be rescued in the male transgenics, likely because of the co-localization of D-AIP with a confounding male-specific sex peptide (Acp70A candidate in MSI spectra) in the gut of the male flies. As a novel, more cost-effective strategy to prevent toxic amyloid formation during the early stages of AD (i.e. neutralization of toxic low-order Aβ42 oligomers without creating larger aggregates in the process), our longitudinal study establishes that D-AIP is a stable and highly effective neutralizer of toxic Aβ42 peptides in vivo. Cover Image for this issue: doi: 10.1111/jnc.14512.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".