NLRP3 knockout enhances immune infiltration and inflammatory responses and improves survival in a burn sepsis model
Bibliographic record
Abstract
Abstract Although sepsis in burn patients is a major contributor to mortality, treatments are not always effective and underlying mechanisms have yet to be completely elucidated. NLRP3 inflammasome orchestrates burn‐induced, inflammatory‐driven pathophysiologic processes. Here, we determined the mechanism of NLRP3 inflammasome activation on bacterial clearance and mortality in burn sepsis. We obtained tissue and blood from 30 wild‐type and 30 Nlrp3−/− mice. Mice were subjected to a two‐hit model of 25–30% TBSA scald burn followed by Pseudomonas aeruginosa wound infection 72 hours after injury. We also obtained tissue from 34 adult burn patients (≥18 years of age) with early (0–11 days post‐burn) and later (≥12 days post‐burn) surgical time‐points and ten healthy controls. Murine studies indicated that Nlrp3−/− had 30% improved survival and bacterial clearance at the site of injury and is systemically relative to burn sepsis wild type. Greater macrophage and neutrophil infiltration occurred acutely after infection (12 hours) to the site of injury and adipose tissue. This was followed by increased macrophage and neutrophil infiltration to lymphoid organs and liver beyond the acute phase (24 and 72 hours). Interestingly, Nlrp3 ablation increased acute systemic inflammation (IL‐6, TNF‐α, IL‐1β). Septic burn patients had persistently increased adipose NLRP3 by‐product expression beyond the acute phase that was more pronounced in late‐onset sepsis. Our findings suggest that Nlrp3 genetic ablation enhanced acute tissue‐specific inflammatory responsiveness. Likely, this occurs by paradoxically increasing acute immune infiltration and inflammation with a non‐persistent response. Clinically, persistent NLRP3‐mediated inflammation occurs in septic versus normal burn patients and potentially detrimentally impacts patient outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".