In vivo stress reporters as early biomarkers of the cellular changes associated with progeria
Bibliographic record
Abstract
Age-related diseases account for a high proportion of the total global burden of disease. Despite recent advances in understanding their molecular basis, there is a lack of suitable early biomarkers to test selected compounds and accelerate their translation to clinical trials. We have investigated the utility of in vivo stress reporter systems as surrogate early biomarkers of the degenerative disease progression. We hypothesized that cellular stress observed in models of human degenerative disease preceded overt cellular damage and at the same time will identify potential cytoprotective pathways. To test this hypothesis, we generated novel accelerated ageing (progeria) reporter mice by crossing the LmnaG609G mice into our oxidative stress/inflammation (Hmox1) and DNA damage (p21) stress reporter models. Histological analysis of reporter expression demonstrated a time-dependent and tissue-specific activation of the reporters in tissues directly associated with Progeria, including smooth muscle cells, the vasculature and gastrointestinal tract. Importantly, reporter expression was detected prior to any perceptible deleterious phenotype. Reporter expression can therefore be used as an early marker of progeria pathogenesis and to test therapeutic interventions. This work also demonstrates the potential to use stress reporter approaches to study and find new treatments for other degenerative diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".