Neuroimmune Communication in Health and Disease
Bibliographic record
Abstract
The immune and nervous systems are tightly integrated, with each system capable of influencing the other to respond to infectious or inflammatory perturbations of homeostasis. Recent studies demonstrating the ability of neural stimulation to significantly reduce the severity of immunopathology and consequently reduce mortality have led to a resurgence in the field of neuroimmunology. Highlighting the tight integration of the nervous and immune systems, afferent neurons can be activated by a diverse range of substances from bacterial-derived products to cytokines released by host cells. While activation of vagal afferents by these substances dominates the literature, additional sensory neurons are responsive as well. It is becoming increasingly clear that although the cholinergic anti-inflammatory pathway has become the predominant model, a multitude of functional circuits exist through which neuronal messengers can influence immunological outcomes. These include pathways whereby efferent signaling occurs independent of the vagus nerve through sympathetic neurons. To receive input from the nervous system, immune cells including B and T cells, macrophages, and professional antigen presenting cells express specific neurotransmitter receptors that affect immune cell function. Specialized immune cell populations not only express neurotransmitter receptors, but express the enzymatic machinery required to produce neurotransmitters, such as acetylcholine, allowing them to act as signaling intermediaries. Although elegant experiments have begun to decipher some of these interactions, integration of these molecules, cells, and anatomy into defined neuroimmune circuits in health and disease is in its infancy. This review describes these circuits and highlights continued challenges and opportunities for the field.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".