Beyond angiogenesis: the cardioprotective potential of fibroblast growth factor-2
Bibliographic record
Abstract
In the field of cardiovascular research, a number of independent approaches have been explored to protect the heart from acute and chronic ischemic damage. Fibroblast growth factor-2 (FGF-2) recently has received considerable attention with respect to its angiogenic potential. While therapeutic angiogenesis may serve to salvage chronically ischemic myocardium, more acute treatments are in demand to increase cardiac resistance to injury (preconditioning) and to guard against secondary injury after an acute ischemic insult. Here, we look beyond the angiogenic potential of FGF-2 and examine its acute cardioprotective activity as demonstrated under experimental conditions, both as an agent of a preconditioning-like response and for secondary injury prevention at the time of reperfusion. Factors to consider in moving to the clinical setting will be discussed, including issues of dosage, treatment duration, and routes of administration. Finally, issues of safety and clinical trial design will be considered. The prospect of such a multipotent growth factor having clinical usefulness opens the door to effective treatment of both acute and chronic ischemic heart disease, something well worth the attention of the cardiovascular community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".