S100A8/A9 and sRAGE kinetic after polytrauma; an explorative observational study
Bibliographic record
Abstract
BACKGROUND: Following tissue injury after trauma, the activation of innate immune pathways results in systemic inflammation, organ failure and an increased risk of infections. The objective of this study was to characterize the kinetics of the S100A8/S100A9 complex, a new-recognized alarmin, as well as its soluble receptor sRAGE, over time after trauma as potential early biomarkers of the risk of organ damage. METHODS: We collected comprehensive data from consenting patients admitted to an ICU following severe trauma. The blood samples were taken at Day 0 (admission), Day1, 3 and 5 S100A8/A9 and sRAGE were measured by ELISA. Biomarkers levels were reported as median (IQR). RESULTS: Thirty-eight patients sustaining in majority a blunt trauma (89%) with a median ISS of 39 were included. In this cohort, the S100A8/A9 complex increased significantly over time (p = 0.001), but its levels increment over time (D0 to D5) was significantly smaller in patients developing infection (7.6 vs 40.1 mcg/mL, p = 0.011). The circulating level of sRAGE circulating levels decreased over time (p < 0.0001) and was higher in patients who remained in shock on day 3 (550 vs 918 pg/mL; p = 0.02) or 5 (498 vs 644 pg/mL; p = 0.045). Admission sRAGE levels were significantly higher in non-survivors (1694 vs 745 pg/mL; p = 0.015) and was higher in patients developing renal failure (1143 vs 696 pg/mL, p = 0.011). DISCUSSION: Our findings reveal an interesting association between the biomarker S100A8/9 least increase over time and the presence of infectious complication after trauma. We describe that the sRAGE decline over time is in relation with shock and markers of ischemic injury. We also confirm the association of sRAGE levels measured at admission with mortality and the development of renal failure. CONCLUSIONS: This work illustrates the importance of following the circulating level of biomarker overtime. The utilization of S1008/9 as a tool to stratify infection risk and trigger early interventions need to be validated prospectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".