Bibliographic record
Abstract
Cellular senescence suppresses cancer by eliminating potentially oncogenic cells, participates in tissue repair, contributes to cancer therapy, and promotes organismal aging. Numerous activities of senescent cells depend on the aptitude of these cells to secrete myriads of bioactive molecules, a behavior termed the senescence-associated secretory phenotype (SASP). The SASP supports cell-autonomous functions like the senescence-associated growth arrest, and mediates paracrine interactions between senescent cells and their surrounding microenvironment. The biological functions and the regulation of the SASP are beginning to emerge, and current SASP assessment techniques include the analysis of SASP factors at the mRNA level, the direct measurement of factors inside or outside the cell (i.e., secreted), and the detection of SASP-provoked cellular responses. Here, we focus on a simple approach to collect SASP-conditioned media in order to directly measure secreted SASP factors using sandwich enzyme-linked immunosorbent assay. As an example, we discuss the assessment of the major SASP factor interleukin-6 in senescent human fibroblasts. Supplemental notes are provided to easily adapt this procedure to other SASP factors, change cell types, or scale the techniques for different volumes or high-throughput measurements. These techniques should facilitate the discovery of novel functions and regulators of the SASP.
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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 | 0.001 | 0.001 |
| 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".