Phenotypic Characterization of Acoustically Enriched Extracellular Vesicles from Pathogen-Activated Platelets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extracellular vesicles (EVs) are derived from the membrane of platelets and released into the circulation upon activation or injury. Analogous to the parent cell, platelet-derived EVs play an important role in hemostasis and immune responses by transfer of bioactive cargo from the parent cells. Platelet activation and release of EVs increase in several pathological inflammatory diseases, such as sepsis. We have previously reported that the M1 protein released from the bacterial pathogen Streptococcus pyogenes directly mediates platelet activation. In this study, EVs were isolated from these pathogen-activated platelets using acoustic trapping, and their inflammation phenotype was characterized using quantitative mass spectrometry-based proteomics and cell-based models of inflammation. We determined that M1 protein mediated release of platelet-derived EVs that contained the M1 protein. The isolated EVs derived from pathogen-activated platelets contained a similar protein cargo to those from physiologically activated platelets (thrombin) and included platelet membrane proteins, granule proteins, cytoskeletal proteins, coagulation factors, and immune mediators. Immunomodulatory cargo, complement proteins, and IgG3 were significantly enriched in EVs isolated from M1 protein-stimulated platelets. Acoustically enriched EVs were functionally intact and exhibited pro-inflammatory effects on addition to blood, including platelet-neutrophil complex formation, neutrophil activation, and cytokine release. Collectively, our findings reveal novel aspects of pathogen-mediated platelet activation during invasive streptococcal infection.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it