α1-Proteinase Inhibitor, α1-Antichymotrypsin, and α2-Macroglobulin Are the Antiapoptotic Factors of Vascular Smooth Muscle Cells
Bibliographic record
Abstract
Serum depletion induces cell death. Whereas serum contains growth factors and adhesion molecules that are important for survival, serum is also likely to have antiapoptotic factor(s). We show here that the plasma proteinase inhibitors alpha1-proteinase inhibitor, alpha1-antichymotrypsin, and alpha2-macroglobulin function as critical antiapoptotic factors for human vascular smooth muscle cells. Cell survival was assured when serum-free medium was supplemented with any one or all of the above serine proteinase inhibitors. In contrast, the cells were sensitive to apoptosis when cultured in medium containing serum from which the proteinase inhibitors were removed. The antiapoptotic effect conferred by the proteinase inhibitors was proportional to proteinase inhibitory activity. Without proteinase inhibitors, the extracellular matrix was degraded, and cells could not attach to the matrix. Cell survival was dependent on the intact extracellular matrix. In the presence of the caspase inhibitor z-VAD, the cells detached but did not die. The activity of caspases was elevated without proteinase inhibitors; in contrast, caspases were not activated when medium was supplemented with one of the proteinase inhibitors. In conclusion, the plasma proteinase inhibitors prevent degradation of extracellular matrix by proteinases derived from cells. Presumably an intact cell-matrix interaction inhibits caspase activation and supports cell survival.
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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".