Fibroblast Growth Factor 21 Suppressed Neutrophil Extracellular Traps Induced by Myocardial Ischemia/Reperfusion Injury via Adenosine Monophosphate-Activated Protein Kinase
Bibliographic record
Abstract
Background: Previous investigations have established the anti-inflammatory properties of fibroblast growth factor 21 (FGF21). However, the specific mechanism through which FGF21 mitigates myocardial ischemia/reperfusion (I/R) injury by inhibiting neutrophil extracellular traps (NETs) remains unclear. Methods: A mice model of myocardial I/R injury was induced, and myocardial tissue was stained with immunofluorescence to assess NETs. Serum NETs levels were quantified using a PicoGreen kit. In addition, the expression levels of adenosine monophosphate (AMP)-activated protein kinase (AMPK) and FGF21 were evaluated by Wes fully automated protein blotting quantitative analysis system. Moreover, a hypoxia/reoxygenation (H/R) model was established using AMPK inhibitor and agonist pretreated H9c2 cells to further explore the relationship between FGF21 and AMPK. Results: Compared with the control group, serum NETs levels were significantly higher in I/R mice, and a large number of NETs were formed in myocardial tissues (97.63 ± 11.45 vs. 69.65 ± 3.33, P < 0.05). However, NETs levels were reversed in FGF21 pretreated mice (P < 0.05). Further studies showed that FGF21 enhanced AMPK expression, which was significantly increased after inhibition of AMPK and decreased after promotion of AMPK (P < 0.05). Conclusions: FGF21 may exert cardioprotective effects by inhibiting I/R injury-induced NETs via AMPK.
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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".