Bibliographic record
Abstract
Smooth muscles develop isometric force over a very wide range of cell lengths. The molecular mechanisms of this phenomenon are undefined, but are described as reflecting "mechanical plasticity" of smooth muscle cells. Plasticity is defined here as a persistent change in cell structure or function in response to a change in the environment. Important environmental stimuli that trigger muscle plasticity include chemical (e.g., neurotransmitters, autacoids, and cytokines) and external mechanical signals (e.g., applied stress and strain). Both kinds of signals are probably transduced by ionic and protein kinase signaling cascades to alter gene expression patterns and changes in the cytoskeleton and contractile system. Defining the signaling mechanisms and effector proteins mediating phenotypic and mechanical plasticity of smooth muscles is a major goal in muscle cell biology. Some of the signaling cascades likely to be important include calcium-dependent protein kinases, small GTPases (Rho, Rac, cdc42), Rho kinase, protein kinase C (PKC), Src family tyrosine kinases, mitogen-activated protein (MAP) kinases, and p21 activated protein kinases (PAK). There are many potential targets for these signaling cascades including nuclear processes, metabolic pathways, and structural components of the cytoskeleton. There is growing appreciation of the dynamic nature of the actin cytoskeleton in smooth muscles and the necessity for actin remodeling to occur during contraction. The actin cytoskeleton serves many functions that are probably critical for muscle plasticity including generation and transmission of force vectors, determination of cell shape, and assembly of signal transduction machinery. Evidence is presented showing that actin filaments are dynamic and that actin-associated proteins comprising the contractile element and actin attachment sites are necessary for smooth muscle contraction.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".