Guidelines for the purification and characterization of extracellular vesicles of parasites
Bibliographic record
Abstract
Parasites are responsible for the most neglected tropical diseases, affecting over a billion people worldwide (WHO, 2015) and accounting for billions of cases a year and responsible for several millions of deaths. Research on extracellular vesicles (EVs) has increased in recent years and demonstrated that EVs shed by pathogenic parasites interact with host cells playing an important role in the parasite's survival, such as facilitation of infection, immunomodulation, parasite adaptation to the host environment and the transfer of drug resistance factors. Thus, EVs released by parasites mediate parasite-parasite and parasite-host intercellular communication. In addition, they are being explored as biomarkers of asymptomatic infections and disease prognosis after drug treatment. However, most current protocols used for the isolation, size determination, quantification and characterization of molecular cargo of EVs lack greater rigor, standardization, and adequate quality controls to certify the enrichment or purity of the ensuing bioproducts. We are now initiating major guidelines based on the evolution of collective knowledge in recent years. The main points covered in this position paper are methods for the isolation and molecular characterization of EVs obtained from parasite-infected cell cultures, experimental animals, and patients. The guideline also includes a discussion of suggested protocols and functional assays in host cells.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.030 |
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".