Mechanism of platelet adhesion to neutrophils in sepsis (94.3)
Bibliographic record
Abstract
Abstract Sepsis is a systemic inflammatory response to infection which has a high mortality rate in the intensive care unit (ICU) resulting in 215000 deaths a year in United States. Neutrophils and platelets play an important role in the induction of inflammation and defense against infection in this pathology. Recent studies by our group have revealed that in severe sepsis TLR4 stimulated platelets interact with already adherent neutrophils. This interaction leads to the formation of Neutrophil Extracellular Traps (NETs) in the circulation, which helps to enhance bacterial trapping and clearance from the body; however, this may also enhance host injury in the liver sinusoids and pulmonary capillaries. To further elucidate the mechanisms by which activated platelets can adhere to immobilized neutrophils in the setting of sepsis, we used an in vitro flow chamber system using LPS and septic plasma as platelet stimulants. Antibodies to several adhesion molecules on platelets and neutrophils were tested. TLR4 activated platelets did not express P-Selectin on their surface and their interaction with immobilized neutrophils was not P-selectin-dependent. Findings show that LFA-1 and CD11c/CD18 on neutrophils, and JAM-A and ICAM-2 on platelets are involved in LPS-induced platelet adhesion to immobilized neutrophils. In addition, results from septic plasma stimulated platelets show that LFA-1 and CD11c/CD18, but not JAM-A, are involved in platelet adhesion to immobilized neutrophils. Blocking the adhesion molecules that take place in platelet adhesion to immobilized neutrophil could prevent NET formation and tissue damage specifically in lungs and liver capillaries providing a target for new therapeutics in this pathology.
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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) | 0.000 | 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 | 0.001 |
| 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".