Neurotransmitters and Microglial-Mediated Neuroinflammation
Why this work is in the frame
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Bibliographic record
Abstract
Reciprocal interactions between cells caused by release of soluble factors are essential for brain function. So far, little attention has been paid to interactions between neurons and glia. However, in the last few decades, studies regarding such interactions have given us some important clues about possible mechanisms underlying degenerative processes in neurological diseases such as Alzheimer's disease and Parkinson's disease. Activated microglia and markers of inflammatory reactions have been consistently found in the post-mortem brains of diseased patients. But it has not been clearly understood how microglia respond to neurotransmitters released from neurons during disease progression. The main purpose of this review is to summarize studies performed on neurotransmitter receptor expression in microglia, and the effects of their activation on microglial-mediated neuroinflammation. A possible mechanism underlying transmitter-mediated modulation of microglial response is also suggested. Microglia express receptors for neurotransmitters such as ATP, adenosine, glutamate, GABA, acetylcholine, dopamine and adrenaline. Activation of GABA, cholinergic and adrenergic receptors suppresses microglial responses, whereas activation of ATP or adenosine receptors activates them. This latter effect may be due primarily to activation of a Ca(2+)-signaling pathway which, in turn, results in activation of MAP kinases and NFkB proteins with the release of proinflammatory factors. However, glutamate and dopamine are both pro- and anti-inflammatory depending on the receptor subtypes expressed in microglia. More detailed studies on downstream receptor-signaling cascades are needed to understand the roles of neurotransmitters in controlling neuron-microglia interactions during inflammatory processes in disease progression. Such knowledge may suggest new methods of treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it