SenSkin™: a human skin-specific cellular senescence gene set
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cellular senescence gene sets have been leveraged to overcome the inadequate sensitivity or specificity of single markers. However, growing evidence of heterogeneity among tissues in senescent cell phenotypes and gene expression profiles has highlighted the need for tissue-specific gene sets. SenSkin™ was curated by an expert review of literature on cellular senescence in the skin and characterized with pathway analysis. To validate SenSkin™, it was evaluated for enrichment with chronological aging in a bulk RNA-sequencing (RNA-seq) dataset and a pseudobulk RNA-seq dataset. Further, changes to SenSkin™ in different skin cell types with photoaging were evaluated in two single-cell RNA-seq datasets. SenSkin™ predominantly included genes related to the senescence-associated secretory phenotype (SASP), which were associated with metabolism and multiple aspects of immune responses. SenSkin™ was more enriched in chronologically aged skin than other commonly used cellular senescence and aging gene sets. In scRNA-seq, SenSkin™ displayed significant upregulation due to photoaging in ten skin cell types. In conclusion, SenSkin™ is a human skin-specific senescence gene set validated in chronological aging and photoaging, which may be more effective at detecting senescent cells in the skin than non-tissue-specific gene sets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it