CNS-Spleen Axis – a Close Interplay in Mediating Inflammatory Responses in Burn Patients and a Key to Novel Burn Therapeutics
Bibliographic record
Abstract
Severe burn-induced inflammation and subsequent hypermetabolic response can lead to profound infection and sepsis, resulting in multiple organ failure and high mortality risk in patients. This represents an extremely challenging issue for clinicians as sepsis is the leading cause of mortality in burn patients. Since hyperinflammation and immune dysfunction are a result of an immune imbalance, restoring these conditions seem to have promising benefits for burn patients. A key network that modulates the immune balance is the central nervous system (CNS)-spleen axis, which coordinates multiple signaling pathways, including sympathetic and parasympathetic pathways. Modulating inflammation is a key strategy that researchers use to understand neuroimmunomodulation in other hyperinflammatory disease models and modulating the CNS-spleen axis has led to improved clinical outcomes in patients. As the immune balance is paramount for recovery in burn-induced sepsis and patients with hyperinflammatory conditions, it appears that severe burn injuries substantially alter this CNS-spleen axis. Therefore, it is essential to address and discuss the potential therapeutic techniques that target the CNS-spleen axis that aim to restore homeostasis in burn patients. To understand this in detail, we have conducted a systematic review to explore the role of the CNS-spleen axis and its impact on immunomodulation concerning the burn-induced hypermetabolic response and associated sepsis complications. Furthermore, this thorough review explores the role of the spleen, CNS-spleen axis in the ebb and flow phases following a severe burn, how this axis induces metabolic factors and immune dysfunction, and therapeutic techniques and chemical interventions that restore the immune balance via neuroimmunomodulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".