Low molecular weight fibroblast growth factor-2 signals via protein kinase C and myofibrillar proteins to protect against postischemic cardiac dysfunction
Bibliographic record
Abstract
Among its many biological roles, fibroblast growth factor-2 (FGF2) acutely protects the heart from dysfunction associated with ischemia/reperfusion (I/R) injury. Our laboratory has demonstrated that this is due to the activity of the low molecular weight (LMW) isoform of FGF2 and that FGF2-mediated cardioprotection relies on the activity of protein kinase C (PKC); however, which PKC isoforms are responsible for LMW FGF2-mediated cardioprotection, and their downstream targets, remain to be elucidated. To identify the PKC pathway(s) that contributes to postischemic cardiac recovery by LMW FGF2, mouse hearts expressing only LMW FGF2 (HMWKO) were bred to mouse hearts not expressing PKCα (PKCαKO) or subjected to a selective PKCε inhibitor (εV(1-2)) before and during I/R. Hearts only expressing LMW FGF2 showed significantly improved postischemic recovery of cardiac function following I/R (P < 0.05), which was significantly abrogated in the absence of PKCα (P < 0.05) or presence of PKCε inhibition (P < 0.05). Hearts only expressing LMW FGF2 demonstrated differences in actomyosin ATPase activity as well as increases in the phosphorylation of troponin I and T during I/R compared with wild-type hearts; several of these effects were dependent on PKCα activity. This evidence indicates that both PKCα and PKCε play a role in LMW FGF2-mediated protection from cardiac dysfunction and that PKCα signaling to the contractile apparatus is a key step in the mechanism of LMW FGF2-mediated protection against myocardial dysfunction.
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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) | 0.001 | 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 | 0.001 |
| 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".