Innate immune response in acute critical illness: a narrative review
Bibliographic record
Abstract
BACKGROUND: Activation of innate immunity is a first line of host defense during acute critical illness (ACI) that aims to contain injury and avoid tissue damages. Aberrant activation of innate immunity may also participate in the occurrence of organ failures during critical illness. This review aims to provide a narrative overview of recent advances in the field of innate immunity in critical illness, and to consider future potential therapeutic strategies. MAIN TEXT: Understanding the underlying biological concepts supporting therapeutic strategies modulating immune response is essential in decision-making. We will develop the multiple facets of innate immune response, especially its cellular aspects, and its interaction with other defense mechanisms. We will first describe the pathophysiological mechanisms of initiation of innate immune response and its implication during ACI. We will then develop the amplification of innate immunity mediated by multiple effectors. Our review will mainly focus on myeloid and lymphoid cellular effectors, the major actors involved in innate immune-mediated organ failure. We will third discuss the interaction and integration of innate immune response in a global view of host defense, thus considering interaction with non-immune cells through immunothrombosis, immunometabolism and long-term reprogramming via trained immunity. The last part of this review will focus on the specificities of the immune response in children and the older population. CONCLUSIONS: Recent understanding of the innate immune response integrates immunity in a highly dynamic global vision of host response. A better knowledge of the implicated mechanisms and their tissue-compartmentalization allows to characterize the individual immune profile, and one day eventually, to develop individualized bench-to-bedside immunomodulation approaches as an adjuvant resuscitation strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".