Mechanotransduction from the ECM to the genome: Are the pieces now in place?
Bibliographic record
Abstract
A multitude of biochemical signaling processes have been characterized that affect gene expression and cellular activity. However, living cells often need to integrate biochemical signals with mechanical information from their microenvironment as they respond. In fact, the signals received by shape alone can dictate cell fate. This mechanotrasduction of information is powerful, eliciting proliferation, differentiation, or apoptosis in a manner dependent upon the extent of physical deformation. The cells internal "prestressed" structure and its "hardwired" interaction with the extra-cellular matrix (ECM) appear to confer this ability to filter biochemical signals and decide between divergent cell functions influenced by the nature of signals from the mechanical environment. In some instances mechanical signaling through the tissue microenvironment has been shown to be dominant over genomic defects, imparting a normal phenotype on cells that otherwise have transforming genetic lesions. This mechanical control of phenotype is postulated to have a central role in embryogenesis, tissue physiology as well as the pathology of a wide variety of diseases, including cancer. We will briefly review studies showing physical continuity between the external cellular microenvironment and the interior of the cell nucleus. Newly characterized structures, termed nuclear envelope lamina spanning complexes (NELSC), and their interactions will be described as part of a model for mechanical transduction of extracellular cues from the ECM to the genome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".