Platelets Promote Macrophage Polarization toward Pro-inflammatory Phenotype and Increase Survival of Septic Mice
Bibliographic record
Abstract
We investigated the contribution of human platelets to macrophage effector properties in the presence of lipopolysaccharide (LPS), as well as the beneficial effects and time frame for platelet transfusion in septic animals. Our results show that platelets sequester both pro-(TNF-α/IL-6) and anti-(IL-10) inflammatory cytokines released by monocytes. Low LPS concentrations (0.01 ng/mL) induced M2 macrophage polarization by decreasing CD64 and augmenting CD206 and CD163 expression; yet, the presence of platelets skewed monocytes toward type 1 macrophage (M1) phenotype in a cell-contact-dependent manner by the glycoprotein Ib (GPIb)-CD11b axis. Accordingly, platelet-licensed macrophages showed increased TNF-α levels, bacterial phagocytic activity, and a reduced healing capability. Platelet transfusion increased inducible nitric oxide synthase (iNOS) + macrophages, improving bacterial clearance and survival rates in septic mice up to 6 h post-infection, an effect that was abolished by CD11b and GPIb blockade. Our results demonstrate that platelets orchestrate macrophage effector responses, improving the clinical outcome of sepsis in a narrow but relevant time frame.
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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 | 0.000 | 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".