Role for platelet toll-like receptor 4 (TLR4) in the formation of Neutrophil Extracellular Traps (NETs) in sepsis (44.22)
Bibliographic record
Abstract
Abstract It has been known for many years that LPS, neutrophils and platelets all participate in the pathogenesis of severe sepsis, but the inter-relationship between these players is completely unknown. Using flow chambers in vitro we suggest a novel innate immune response leading to enhanced trapping of bacteria in blood vessels. The mechanism involved platelet TLR4 detecting LPS in blood and inducing a unique response, specifically platelet binding to adherent neutrophils, but not platelet aggregation or P-selectin expression. Subsequently, the platelets stimulated very robust neutrophil activation leading to formation of NETs. These NETs retained their integrity under flow conditions and functioned to ensnare bacteria within the vasculature. Plasma from severely septic patients also induced TLR4-dependent platelet-neutrophil interactions leading to the production of NETs. We propose that this novel bacterial trapping mechanism would only occur under extreme conditions such as severe sepsis and platelet TLR4 (not leukocyte TLR4) functioned as the threshold switch for this innate immune response to occur. With the advent of antibiotics perhaps reducing the need for NET formation, we would propose that inhibiting platelet activation with TLR4 inhibitors may inhibit NET formation and reduce inadvertent tissue injury. This work was funded by: CIHR and AHFMR
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| 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".