Investigating the effect of neuro-immune communication on immune responses in health and disease: Exploring immunological disorders
Bibliographic record
Abstract
Recent recognition of the intricate nervous-immune system interplay has prompted research into the specific cellular components involved in these interactions. Emerging evidence suggests that immune and neural cells collaborate within distinct units and act in concert to regulate tissue function and provide protection. These specialized neuro-immune cell units have been identified in diverse body tissues, ranging from lymphoid organs to the bone marrow and mucosal barriers. Their significance has become increasingly apparent as they are recognized as pivotal regulators influencing a broad spectrum of physiological and pathological processes. This recognition extends to critical roles in hematopoiesis, organ function, inflammatory responses, and intricate tissue repair processes. This review explores the bidirectional communication between the nervous and immune systems. The focus is on understanding the profound impact of this communication on immune cells within key anatomical sites, such as the bone marrow, gastrointestinal tract, and lymphoid organs. By examining these interactions, this review aims to shed light on how this intricate network operates under normal and pathological conditions, offering insights into the mechanisms underlying health and disease.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".