From patients to politicians: a cognitive engineering view of patient safety
Bibliographic record
Abstract
Abstract Epithelial-mesenchymal transition (EMT) is a change in cell shape and mobility that occurs during normal development or cancer metastasis. Multiple intermediate EMT states reflecting hybrid epithelial and mesenchymal phenotypes were observed in various physiological and pathological conditions. Previous theoretical models explaining the intermediate EMT states rely on multiple regulatory loops involving transcriptional feedback. These models produce three or four attractors with a given set of rate constants, which is incompatible with experimentally observed non-genetic heterogeneity reflecting a continuum-like EMT spectrum. EMT is regulated by many microRNAs that typically bind transcripts of EMT-related genes via multiple binding sites. It was unclear whether post-transcriptional regulations associated with the microRNA binding sites alone can stabilize intermediate EMT states. Here, we used models describing the post-transcriptional regulations with elementary reaction networks, finding that cooperative RNA degradation via multiple microRNA binding sites can generate four-attractor systems without transcriptional feedback. We identified many specific, experimentally supported instances of network structures predicted to permit intermediate EMT states. Furthermore, transcriptional feedback and the newly identified intermediates-enabling circuits can be combined to produce even more intermediate EMT states in both modular and emergent manners. Finally, multisite-mediated cooperative RNA degradation can increase the distribution of gene expression in the EMT spectrum and support the phenotypic continuum without the need of higher noise. Our work reveals a previously unknown role of cooperative RNA degradation and microRNA in EMT, providing a theoretical framework that can help to bridge the gap between mechanistic models and single-cell experiments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".