Neuroimmune Communication in Health and Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it