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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".