Role of matrix metalloproteinases 2 and 9, toll-like receptor 4 and platelet-leukocyte aggregate formation in sepsis-associated thrombocytopenia
Bibliographic record
Abstract
BACKGROUND: The development of thrombocytopenia in sepsis is a poor prognostic indicator associated with a significantly increased mortality risk. Mechanisms underlying this phenomenon remain to be clearly elucidated. Matrix metalloproteinases (MMPs) are enzymes that regulate the turnover of the extra-cellular matrix. MMP-2 is recognised as a platelet agonist with MMP-9 proposed as an inhibitor of platelet activation. The existence of MMP-9 in platelets is a subject of debate. There is limited evidence thus far to suggest that toll-like receptor 4 (TLR-4) and platelet-leukocyte aggregate (PLA) formation may be implicated in the development of sepsis-associated thrombocytopenia. OBJECTIVES: To investigate whether MMP -2/-9, toll-like receptor 4 (TLR-4) or platelet-leukocyte aggregate (PLA) formation are implicated in a decline in platelet numbers during septic shock. METHODS: This was an observational study which recruited healthy controls, non-thrombocytopenic septic donors and thrombocytopenic septic donors. MMP-2, MMP-9 and TLR-4 platelet surface expression as well as PLA formation was examined using flow cytometry. In addition MMP-2 and MMP-9 were examined by gelatin zymography and enzyme-linked immunosorbent assay (ELISA) using a 3 compartment model (plasma, intraplatelet and platelet membrane). RESULTS: There was no difference found in MMP-2, MMP-9 or TLR-4 levels between non-thrombocytopenic and thrombocytopenic septic donors. PLA formation was increased in thrombocytopenic patients. MMP-9 was detected in platelets using flow cytometry, gelatin zymography and ELISA techniques. CONCLUSIONS: Platelet consumption into PLAs may account for the development of thrombocytopenia in septic shock. MMP-9 is found in platelets and it is upregulated during septic shock.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".