Special considerations for studies of extracellular vesicles from parasitic helminths: A community‐led roadmap to increase rigour and reproducibility
Bibliographic record
Abstract
Abstract Over the last decade, research interest in defining how extracellular vesicles (EVs) shape cross‐species communication has grown rapidly. Parasitic helminths, worm species found in the phyla Nematoda and Platyhelminthes, are well‐recognised manipulators of host immune function and physiology. Emerging evidence supports a role for helminth‐derived EVs in these processes and highlights EVs as an important participant in cross‐phylum communication. While the mammalian EV field is guided by a community‐agreed framework for studying EVs derived from model organisms or cell systems [e.g., Minimal Information for Studies of Extracellular Vesicles (MISEV)], the helminth community requires a supplementary set of principles due to the additional challenges that accompany working with such divergent organisms. These challenges include, but are not limited to, generating sufficient quantities of EVs for descriptive or functional studies, defining pan‐helminth EV markers, genetically modifying these organisms, and identifying rigorous methodologies for in vitro and in vivo studies. Here, we outline best practices for those investigating the biology of helminth‐derived EVs to complement the MISEV guidelines. We summarise community‐agreed standards for studying EVs derived from this broad set of non‐model organisms, raise awareness of issues associated with helminth EVs and provide future perspectives for how progress in the field will be achieved.
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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 | 0.545 | 0.510 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.012 | 0.033 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".