Brain Derived Neurotrophic Factor (BDNF) as a Mediator of Microglia-Induced Neuroinflammation
Bibliographic record
Abstract
Microglia are the primary immunocompetent cells that protect the brain from environmental stressors; however, their activation can also have deleterious effects on brain functioning.Indeed, environmental toxins and microbial agents can induce microglial driven inflammatory processes that increase levels of pro-inflammatory cytokines and induce a cytotoxic environment.Recent therapeutic strategies have sought to determine how to modulate microglia, so as to favour their neuroprotective effects, while minimizing toxic outcomes.BDNF is one of the most commonly expressed neurotrophins in the brain and is important for the regulation of plasticity, synapse formation, and general neuron health.Yet, little is known about how exogenous BDNF directly effects microglial activity.We hypothesized that BDNF would have a modulatory effect on inflammation in isolated microglia cultures in the context of a bacterial endotoxin.To this end, we indeed found that a BDNF treatment following LPS-induced inflammation attenuated the release of both IL-6 and TNF-α in primary microglia.In neurons, LPS-activated microglial media was able produce a minor inflammatory response, while secondary treatment of BDNF reduced this effect.Interestingly, LPS activated microglial media alone was found to increase production of anti-inflammatory IL-4 in neurons.We speculate that BDNF plays a role in regulating microglia activation and localized microglia-neuron crosstalk may be crucial in preventing damaging effects of inflammatory mechanisms.
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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) | 0.002 | 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".