GABA exerts anti-inflammatory and immunosuppressive effects (P5175)
Bibliographic record
Abstract
Abstract Gamma amino-butyric acid (GABA) is an inhibitory neurotransmitter in the CNS, but it also exerts important functions in the immune system and the islets of Langerhans. Thus, functional GABA receptors have been identified on immune cells and islet cells. Previously, we found that in both autoimmune (NOD) and streptozotocin (STZ)-induced diabetes GABA increases islet-cell mass, while exerting anti-apoptotic effects. GABA induced the regeneration of islet beta cells. It also suppressed inflammatory cytokine production, which is likely important in allowing survival of new islet beta cells. In this study, we examine how GABA exerts immunoprotective effects. We report that GABA suppresses both T cells and macrophages. It increases TGF-beta production and regulatory T cells (Tr or Treg). Notably, GABA inhibits NF-kB activation in both lymphocytes and pancreatic islet beta cells. It also protects beta cells from apoptosis induced by various mechanisms, and this may be related to NF-kB inhibition. The mechanisms by which GABA exerts these effects are largely unknown. However, in immune cells it stimulates GABA type A receptors, which act as ligand-gated chloride channels. This may open voltage-dependent calcium channels in the membrane and decrease intra-cellular calcium levels. Conclusion: GABA acts on both T cells and macrophages and exerts potent anti-inflammatory effects, which are protective in models of type 1 diabetes.
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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) | 0.003 | 0.001 |
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".