Platelet-Neutrophil Interplay: Insights Into Neutrophil Extracellular Trap (NET)-Driven Coagulation in Infection
Bibliographic record
Abstract
Well established for their central role in hemostasis, platelets have increasingly been appreciated as immune cells in recent years. This emerging role should not come as a surprise as the central immune cells of invertebrates, hemocytes, are able to phagocytose, secrete soluble mediators and promote coagulation of hemolymph, blurring the line between immunity and hemostasis. The undeniable evolutionary link between coagulation and immunity becomes even clearer as the role of platelets in inflammation is better understood. Platelets exert a range of immune-related functions, many of which involve an intimate interplay with leukocytes. Platelets promote leukocyte recruitment via endothelial activation and can serve as "landing pads" for leukocytes, facilitating cellular adhesion in vascular beds devoid of classic adhesion molecules. Moreover, platelets enhance leukocyte function both through direct interactions and through release of soluble mediators. Among neutrophil-platelets interactions, the modulation of neutrophil extracellular traps (NETs) is of great interest. Platelets have been shown to induce NET formation; and, in turn, NET components further regulate platelet and neutrophil function. While NETs have been shown to ensnare and kill pathogens, they also initiate coagulation via thrombin activation. In fact, increased NET formation has been associated with hypercoagulability in septic patients as well as in chronic vascular disorders. This review will delve into current knowledge of platelet-neutrophil interactions, with a focus on NET-driven coagulation, in the context of infectious diseases. A better understanding of these mechanisms will shed a light on the therapeutic potential of uncoupling immunity and coagulation through targeting of NETs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".