Green Tea Epigallocatechin-3-Gallate Mediates T Cellular NF-κB Inhibition and Exerts Neuroprotection in Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Recent studies in multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis (EAE), point to the fact that even in the early phase of inflammation, neuronal pathology plays a pivotal role in the sustained disability of affected individuals. We show that the major green tea constituent, (-)-epigallocatechin-3-gallate (EGCG), dramatically suppresses EAE induced by proteolipid protein 139-151. EGCG reduced clinical severity when given at initiation or after the onset of EAE by both limiting brain inflammation and reducing neuronal damage. In orally treated mice, we found abrogated proliferation and TNF-alpha production of encephalitogenic T cells. In human myelin-specific CD4+ T cells, cell cycle arrest was induced, down-regulating the cyclin-dependent kinase 4. Interference with both T cell growth and effector function was mediated by blockade of the catalytic activities of the 20S/26S proteasome complex, resulting in intracellular accumulation of IkappaB-alpha and subsequent inhibition of NF-kappaB activation. Because its structure implicates additional antioxidative properties, EGCG was capable of protecting against neuronal injury in living brain tissue induced by N-methyl-D-aspartate or TRAIL and of directly blocking the formation of neurotoxic reactive oxygen species in neurons. Thus, a natural green tea constituent may open up a new therapeutic avenue for young disabled adults with inflammatory brain disease by combining, on one hand, anti-inflammatory and, on the other hand, neuroprotective capacities.
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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) | 0.000 | 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 | 0.000 |
| 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".