Monitoring Collagen Synthesis in Fibroblasts Using Fluorescently Labeled tRNA Pairs
Bibliographic record
Abstract
There is a critical need for techniques that directly monitor protein synthesis within cells isolated from normal and diseased tissue. Fibrotic disease, for which there is no drug treatment, is characterized by the overexpression of collagens. Here, we use a bioinformatics approach to identify a pair of glycine and proline isoacceptor tRNAs as being specific for the decoding of collagen mRNAs, leading to development of a FRET-based approach, dicodon monitoring of protein synthesis (DiCoMPS), that directly monitors the synthesis of collagen. DiCoMPS aimed at detecting collagen synthesis will be helpful in identifying novel anti-fibrotic compounds in cells derived from patients with fibrosis of any etiology, and, suitably adapted, should be widely applicable in monitoring the synthesis of other proteins in cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".