Silencing of Amyloid Precursor Protein Expression Using a New Engineered Delta Ribozyme
Bibliographic record
Abstract
Alzheimer's disease (AD) etiological studies suggest that an elevation in amyloid-β peptides (Aβ) level contributes to aggregations of the peptide and subsequent development of the disease. The major constituent of these amyloid peptides is the 1 to 40-42 residue peptide (Aβ(40-42)) derived from amyloid protein precursor (APP). Most likely, reducing Aβ levels in the brain may block both its aggregation and neurotoxicity and would be beneficial for patients with AD. Among the several possible ways to lower Aβ accumulation in the cells, we have selectively chosen to target the primary step in the Aβ cascade, namely, to reduce APP gene expression. Toward this end, we engineered specific SOFA-HDV ribozymes, a new generation of catalytic RNA tools, to decrease APP mRNA levels. Additionally, we demonstrated that APP-ribozymes are effective at decreasing APP mRNA and protein levels as well as Aβ levels in neuronal cells. Our results could lay the groundwork for a new protective 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) | 0.000 | 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) | 0.000 | 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".