Interactions between the Autonomic Nervous System and the Immune System after Stroke
Bibliographic record
Abstract
Acute stroke is one of the leading causes of morbidity and mortality worldwide. Stroke-induced immune-inflammatory response occurs in the perilesion areas and the periphery. Although stroke-induced immunosuppression may alleviate brain injury, it hinders brain repair as the immune-inflammatory response plays a bidirectional role after acute stroke. Furthermore, suppression of the systemic immune-inflammatory response increases the risk of life-threatening systemic bacterial infections after acute stroke. Therefore, it is essential to explore the mechanisms that underlie the stroke-induced immune-inflammatory response. Autonomic nervous system (ANS) activation is critical for regulating the local and systemic immune-inflammatory responses and may influence the prognosis of acute stroke. We review the changes in the sympathetic and parasympathetic nervous systems and their influence on the immune-inflammatory response after stroke. Importantly, this article summarizes the mechanisms on how ANS regulates the immune-inflammatory response through neurotransmitters and their receptors in immunocytes and immune organs after stroke. To facilitate translational research, we also discuss the promising therapeutic approaches modulating the activation of the ANS or the immune-inflammatory response to promote neurologic recovery after stroke. © 2022 American Physiological Society. Compr Physiol 12:3665-3704, 2022.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".