AAV-mediated peripheral scFv's administration to reduce cerebral tau in adult P301S transgenic mice: Mono-vs. combination therapy
Bibliographic record
Abstract
. Here, we compared the effects of single-chain variable fragments (scFvs) derived from the well-characterized monoclonal antibodies PHF1 and MC1. We used adeno-associated virus 1 (AAV1) to deliver scFvs to skeletal muscle cells in 8-week-old P301S tau transgenic mice. We evaluated motor and behavioral functions at 16 and 23 weeks of age and measured misfolded, soluble, oligomeric, and insoluble brain tau species. Monotherapy with scFv-MC1 improved motor and behavioral functions more effectively than scFv-PHF1 or combination therapy. Brain glucose metabolism also benefited from scFv-MC1 treatment. Surprisingly, combining scFvs targeting early (MC1) and late (PHF1) tau modifications did not produce additive or synergistic effects. These results confirm that intramuscular AAV1-mediated scFv-MC1 gene therapy holds promise as a potential treatment for AD. Our findings also suggest that combining scFvs targeting different tau epitopes may not necessarily enhance efficacy if administered together in a prevention paradigm. Further research is needed to explore whether other antibodies' combinations and/or administration schedules could improve the efficacy of scFv-MC1 alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".