A Differential Effect of FGF‐2 on Resistance to Doxorubicin‐Induced Necrotic Versus Apoptotic‐Like Damage in Neonatal Rat Cardiomyocyte Cultures
Bibliographic record
Abstract
Therapeutic agents like doxorubicin (DOX), an anti‐cancer anthracycline, can increase the risk of cardiac damage. Strategies are needed to protect the heart that still allows the benefits of drug treatment. “Basic” fibroblast growth factor‐2 (FGF‐2), and specifically the low (18 kDa) molecular weight isoform is cardioprotective, but this has not been reported for DOX‐induced injury. The clinical effects of DOX have been modeled in rat cardiomyocytes, and 0.1–1.0 μM DOX is sufficient to induce cell damage. Here, we assessed the effect of 10 nM FGF‐2 on 0.5 μM DOX‐induced necrotic versus apoptotic‐related damage over 24 hours, as detected by the presence of lactate dehydrogenase in the culture medium (LDH) versus DNA fragmentation by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). DOX induced a decrease in apparent (attached) cell number and a significant increase in LDH within 6 hours. The remaining cells continued to display muscle striations based on alpha‐actinin staining, but an increase in TUNEL intensity from 6 to 24 hours was also seen. Pre‐treatment (30 min) with FGF‐2 resulted in a significant decrease in LDH but not apparent TUNEL intensity. Additional assays to characterize these effects are being pursued. These data suggest that FGF‐2 can increase resistance to DOX‐induced plasma membrane damage but not DNA fragmentation under the conditions tested.
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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 | 0.001 |
| 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".