An analysis of the effects of stretch on IGF-I secretion from rat ventricular fibroblasts
Bibliographic record
Abstract
Mechanical force can induce a number of fundamental short- and long-term responses in myocardium. These include alterations in ECM, activation of cell-signaling pathways, altered gene regulation, changes in cell proliferation and growth, and secretion of a number of peptides and growth factors. It is now known that a number of these autocrine/paracrine factors are secreted from both cardiomyocytes and ventricular cardiac fibroblasts (CFb) in response to stretch. One such substance is IGF-I. IGF-I is an important autocrine/paracrine factor that can regulate physiological or pathophysiological responses, such as hypertrophy. In this study, we addressed the possible effects of mechanical perturbation, biaxial strain, on IGF-I secretion from adult rat CFb. CFb were subjected to either static stretch (3-10%) or cyclic stretch (10%; 0.1-1 Hz) over a 24-h period. IGF-1 secretion from CFb in response to selected stretch paradigms was examined using ELISA to measure IGF-I concentrations in conditioned media. Static stretch did not result in any measurable modulation of IGF-I secretion from CFb. However, cyclic stretch significantly increased IGF-I secretion from CFb in a frequency- and time-dependent manner compared with nonstretched controls. This stretch-induced increase in secretion was relatively insensitive to changes in extracellular [Ca(2+)] or to block of L-type Ca(2+) channels. In contrast, thapsigargin, an inhibitor of sarco(endo)plasmic reticulum Ca(2+) ATPase, remarkably decreased stretch-induced IGF-I secretion from CFb. We further show that IGF-I can upregulate mRNA expression of atrial natriuretic peptide in myocytes. In summary, cyclic stretch can significantly increase IGF-I secretion from CFb, and this effect is dependent on a thapsigargin-sensitive pool of intracellular [Ca(2+)].
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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".