Neurochemokines: a menage a trois providing new insights on the functions of chemokines in the central nervous system
Bibliographic record
Abstract
Recent observations suggest that besides their role in the immune system, chemokines have important functions in the brain. There is a great line of evidence to suggest that chemokines are a unique class of neurotransmitters/neuromodulators, which regulate many biological aspects as diverse as neurodevelopment, neuroinflammation and synaptic transmission. In physiopathological conditions, many chemokines are synthesized in activated astrocytes and microglial cells, suggesting their involvement in brain defense mechanisms. However, when evoking chemokine functions in the nervous system, it is important to make a distinction between resting conditions and various pathological states including inflammatory diseases, autoimmune or neurodegenerative disorders in which chemokine functions have been extensively studied. We illustrate here the emergent concept of the neuromodulatory/neurotransmitter activities of neurochemokines and their potential role as a regulatory alarm system and as a group of messenger molecules for the crosstalk between neurons and cells from their surrounding microenvironment. In this deliberately challenging review, we provide novel hypotheses on the role of these subtle messenger molecules in brain functions leading to the evidence that previous dogmas concerning chemokines should be reconsidered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".