Phenotypic Characterization of Acoustically Enriched Extracellular Vesicles from Pathogen-Activated Platelets
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".