Modeling of the Senescence-Associated Phenotype in Human Skin Fibroblasts
Bibliographic record
Abstract
Modern understanding of aging is based on the accumulation of cellular damage during one’s life span due to the gradual deterioration of regenerative mechanisms in response to the continuous effect of stress, lifestyle, and environmental factors, followed by increased morbidity and mortality. Simultaneously, the number of senescent cells accumulate exponentially as organisms age. Cell culture models are valuable tools to investigate the mechanisms of aging by inducing cellular senescence in stress-induced premature senescence (SIPS) models. Here, we explain the three-step and one-step H2O2-induced senescence models of SIPS designed and reproduced on different human dermal fibroblast cell lines (CCD-1064Sk, CCD-1135Sk, and BJ-5ta). In both SIPS models, it was evident that the fibroblasts developed similar aging characteristics as cells with replicative senescence. Among the most noticeable senescent biomarkers were increased β-Gal expression, high levels of the p21 protein, altered levels of cell-cycle regulators (i.e., CDK2 and c-Jun), compromised extracellular matrix (ECM) composition, reduced cellular viability, and delayed wound healing properties. Based on the significant increase in senescence biomarkers in fibroblast cultures, reduced functional activity, and metabolic dysfunction, the one-step senescence model was chosen as a feasible and reliable method for future testing of anti-aging compounds.
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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) | 0.001 | 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".