Impact of Glucocorticoids on Neuroinflammation in the TgF344‐AD Rat Model
Bibliographic record
Abstract
BACKGROUND: Glial fibrillary acidic protein (GFAP) levels, a key marker of astrocyte reactivity, increase in response to Alzheimer's disease (AD) pathology, but its role in AD-related neuroinflammation remains unclear. Dexamethasone, a glucocorticoid, modulates inflammation by binding to glucocorticoid receptors, suppressing pro-inflammatory cytokines and immune cell activation. This study investigates the anti-inflammatory effects of glucocorticoids on astroglial and microglial markers in the TgF344-AD rat model. METHOD: Male TgF344-AD rats (n = 8), in an early amyloid plaque stage (7-8 months), received 0.25 mg/kg of dexamethasone (i.p.) for 14 days. After treatment, blood, cerebrospinal fluid (CSF), and brain tissue were collected. We performed [3H]-glutamate uptake assay on acute cortical tissue slices and analyzed the cortical immunocontent and expression of inflammatory, astroglial, and microglial activation markers. Additionally, plasma and CSF glucose levels were measured with a colorimetric assay. Data were analyzed using Student's t-test (p <0.05). RESULT: We observed higher plasma glucose levels in dexamethasone-treated animals, confirming the systemic treatment efficacy (p = 0.0104) (Figure 1A). Additionally, there was a tendency of reduction in CSF glucose levels (Figure 1B). No significant changes in glutamate uptake and glial activation markers were observed (Figure 1-2). However, the cortical expression of C1qB was reduced in dexamethasone-treated animals (p = 0.02) and, contradictory, increased in TNF-α was observed (p = 0.04) (Figure 2). CONCLUSION: Our initial results indicate that, despite no changes in glial marker levels, the treatment reduced C1qB levels while paradoxically increasing TNF-α in the TgF344-AD model. Since both TNF-α and C1qB are predominantly secreted by microglia, further studies are warranted to investigate whether dexamethasone induces a distinct microglial phenotype. Additional experiments are necessary to determine if these changes signify a shift in immune balance and whether they have protective or detrimental effects.
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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 | 0.000 |
| 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".