Selection of DNA aptamers that prevent the fibrillization of α-synuclein protein in cellular and mouse models
Bibliographic record
Abstract
A neuropathological hallmark of Parkinson's disease (PD) is the aggregation and spreading of misfolded α-synuclein (αSyn) protein. In this study, a selection method was developed to identify aptamers that showed affinity for monomeric αSyn and inhibition of αSyn aggregation. Aptamer a-syn-1 exhibited strong inhibition of αSyn aggregation in vitro by transmission electron microscopy and Thioflavin T fluorescence. A-syn-1 -treated SH-SY5Y cells incubated with pre-formed fibrils (PFFs) showed less intracellular aggregation of αSyn in comparison with a scrambled oligonucleotide control, as observed with fluorescent microscopy. Systemic delivery of a-syn-1 to the brain was achieved using a liposome vehicle and confirmed with fluorescence microscopy and qPCR. Transgenic mice overexpressing the human A53T variant of αSyn protein were injected with a-syn-1 loaded liposomes at 5 months of age both acutely (single intraperitoneal [i.p.] injection) and repeatedly (5 i.p. injections over 5 days). Western blot protein quantification revealed that both acute and repeated injections of a-syn-1 decreased levels of the aggregated form of αSyn in the transgenic mice in the prefrontal cortex, caudate, and substania nigra (SNc). These results provide in vitro and in vivo evidence that a-syn-1 can inhibit pathological αSyn aggregation and may have implications in treatment strategies to target dysregulation in PD.
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) | 0.000 | 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) | 0.004 | 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".