Critical considerations in determining the surface charge of small extracellular vesicles
Bibliographic record
Abstract
Small extracellular vesicles (EVs) have emerged as a focal point of EV research due to their significant role in a wide range of physiological and pathological processes within living systems. However, uncertainties about the nature of these vesicles have added considerable complexity to the already difficult task of developing EV-based diagnostics and therapeutics. Whereas small EVs have been shown to be negatively charged, their surface charge has not yet been properly quantified. This gap in knowledge has made it challenging to fully understand the nature of these particles and the way they interact with one another, and with other biological structures like cells. Most published studies have evaluated EV charge by focusing on zeta potential calculated using classical theoretical approaches. However, these approaches tend to underestimate zeta potential at the nanoscale. Moreover, zeta potential alone cannot provide a complete picture of the electrical properties of small EVs since it ignores the effect of ions that bind tightly to the surface of these particles. The absence of validated methods to accurately estimate the actual surface charge (electrical valence) and determine the zeta potential of EVs is a significant knowledge gap, as it limits the development of effective label-free methods for EV isolation and detection. In this study, for the first time, we show how the electrical charge of small EVs can be more accurately determined by accounting for the impact of tightly bound ions. This was accomplished by measuring the electrophoretic mobility of EVs, and then analytically correlating the measured values to their charge in the form of zeta potential and electrical valence. In contrast to the currently used theoretical expressions, the employed analytical method in this study enabled a more accurate estimation of EV surface charge, which will facilitate the development of EV-based diagnostic and therapeutic applications.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".