Identification of potential novel proteomic markers of Leishmania spp.-derived exosomes
Bibliographic record
Abstract
Introduction Extracellular vesicles (EVs) are heterogenous cell-derived membrane-bound structures which can be subdivided into three distinct classes according to distinct morphological characteristics, cellular origins, and functions. Small EVs, or exosomes, can be produced by the protozoan parasite Leishmania through the evolutionarily conserved ESCRT pathway, and act as effectors of virulence and drivers of pathogenesis within mammalian hosts. Techniques for the identification of EVs of non-mammalian origin, however, remain inaccurate in comparison to their well-characterized mammalian counterparts. Thus, we still lack reliable and specific markers for Leishmania -derived exosomes, which poses a significant challenge to the field. Methods Herein, we utilized serial differential ultracentrifugation to separate Leishmania -derived EV populations into three distinct fractions. Nanoparticle tracking analysis and transmission electron microscopy were used to validate their morphological characteristics, and bioinformatic analysis of LC-MS/MS proteomics corroborated cellular origins and function. Discussion Proteomic data indicated potential novel proteic markers of Leishmania -derived exosomes, including proteins involved in endosomal machinery and the ESCRT pathway, as well as the parasitic phosphatase PRL-1. Further investigation is required to determine the specificity and sensitivity of these markers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".