Variation in stiffness regulates cardiac myocyte hypertrophy via signaling pathways
Bibliographic record
Abstract
Much diseased human myocardial tissue is fibrotic and stiff, which increases the work that the ventricular myocytes must perform to maintain cardiac output. The hypothesis tested is that the increased load due to greater stiffness of the substrata drives sarcomere assembly of cells, thus strengthening them. Neonatal rat ventricular myocytes (NRVM) were cultured on polyacrylamide or polydimethylsiloxane substrates with stiffness of 10 kPa, 100 kPa, or 400 kPa, or glass with stiffness of 61.9 GPa. Cell size increased with stiffness. Two signaling pathways were explored, phosphorylation of focal adhesion kinase (p-FAK) and lipids by phosphatidylinositol 4,5-bisphosphate (PIP2). Subcellular distributions of both were determined in the sarcomeric fraction by antibody localization, and total amounts were measured by Western or dot blotting, respectively. More p-FAK and PIP2 distributed to the sarcomeres of NRVM grown on stiffer substrates. Actin assembly involves the actin capping protein Z (CapZ). Both actin and CapZ dynamic exchange were significantly increased on stiffer substrates when assessed by fluorescence recovery after photobleaching (FRAP) of green fluorescent protein tags. Blunting of actin FRAP by FAK inhibition implicates linkage from mechano-signalling pathways to cell growth. Thus, increased stiffness of cardiac disease can be modeled with polymeric materials to understand how the microenvironment regulates cardiac hypertrophy.
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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".