Abstract 2225 Assessing Compound Efficacy in Huntington's Disease Pathology using pEGFP-Q74 Transfected HeLa Cells
Bibliographic record
Abstract
Huntington's disease (HD) is a rare neurodegenerative disease impacting approximately 2.7 individuals out of 100,000 worldwide. However, it presents an appealing focus for nucleic acid-targeting therapies. This is due to its origin in an expanded DNA repeat, which, when transcribed, produces harmful repeat RNA. Thus, we hypothesized that a compound that binds to the disease-causing DNA and inhibits transcription to prevent the formation of toxic RNA could be promising as a HD therapeutic. By preventing the formation of toxic RNA, the downstream translation that forms homopolymeric proteins could be knocked down, potentially diminishing protein aggregates that often cause HD symptoms and disease progression. This investigation focuses on a specific compound functioning as a groove binder. In this study we tested the compound of interest in Hela Cells transfected with the pEGFP-Q74 plasmid. A fluorescence microscope was used to determine if the amount of protein aggregates increased or decreased in the presence of our compound in a normal length repeat as compared to the disease length. Thus, this work contributes to understanding the efficacy of this compound as a treatment for HD.
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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) | 0.002 | 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".