Fibroblast Growth Factor 19 Improves LPS‐Induced Lipid Disorder and Organ Injury by Regulating Metabolomic Characteristics in Mice
Bibliographic record
Abstract
Sepsis is extremely heterogeneous pathology characterized by complex metabolic changes. Fibroblast growth factor 19 (FGF19) is a well-known intestine-derived inhibitor of bile acid biosynthesis. However, it is largely unknown about the roles of FGF19 in improving sepsis-associated metabolic disorder and organ injury. In the present study, mice were intravenously injected recombinant human FGF19 daily for 7 days followed by lipopolysaccharide (LPS) administration. At 24 hours after LPS stimuli, sera were collected for metabolomic analysis. Ingenuity pathway analysis (IPA) network based on differential metabolites (DMs) was conducted. Here, metabolomic analysis revealed that FGF19 pretreatment reversed the increase of LPS-induced fatty acids. IPA network indicated that altered linoleic acid (LA) and gamma-linolenic acid (GLA) were involved in the regulation of oxidative stress and mitochondrial function and were closely related to reactive oxygen species (ROS) generation. Further investigation proved that FGF19 pretreatment decreased serum malondialdehyde (MDA) levels and increased serum catalase (CAT) levels. In livers, FGF19 suppressed the expression of inducible NO synthase (iNOS) and enhanced the expression of nuclear factor erythroid 2-related factor 2 (NRF2) and hemeoxygenase-1 (HO-1). Finally, FGF19 pretreatment protected mice against LPS-induced liver, ileum, and kidney injury. Taken together, FGF19 alleviates LPS-induced organ injury associated with improved serum LA and GLA levels and oxidative stress, suggesting that FGF19 might be a promising target for metabolic therapy for sepsis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".