Antidepressant-Like Effects of Low- and High-Molecular Weight FGF-2 on Chronic Unpredictable Mild Stress Mice
Bibliographic record
Abstract
The occurrence of depressive disorder has long been attributed to changes in monoamines, with the focus of drug treatment strategies being to change the effectiveness of monoamines. However, the success achieved by changing these processes is limited and further stimulates the exploration of alternative mechanisms and treatments. Fibroblast growth factor 2 (FGF-2), which occurs in a high-molecular weight (HMW) and low-molecular weight (LMW) form, is a potent developmental modulator and nervous system regulator that has been suggested to play an important role in various psychiatric disorders. In this study, we investigated the antidepressant effects of HMW and LMW FGF-2 on depression induced by chronic stress. Both peripheral LMW and HMW FGF-2 attenuated the depression-like behaviors in chronic unpredictable mild stress (CUMS) mice to a similar extent, as determined by the forced swimming, tail suspension, and sucrose preference tests. We then showed that CUMS-induced oxidative stresses in mice were inhibited by FGF-2 treatments both in central and peripheral. We also showed that both forms of FGF-2 increased the phosphorylation of ERK and AKT, increased Bcl-2 expression and inhibited caspase-3 activation in CUMS mice. Interestingly, HMW FGF-2 enhanced the activity of the brain-derived neurotrophic factor (BDNF) to a greater extent than did LMW FGF-2 in the hippocampus. Taken together, these results suggest that depressive symptoms can be relieved by administering different forms of FGF-2 peripherally in a CUMS-induced depression model through a similar antidepressant signaling pathway, therefore suggesting a potential clinical use for FGF-2 as a treatment for depression.
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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".