The polysaccharide chitosan facilitates the isolation of small extracellular vesicles from multiple biofluids
Bibliographic record
Abstract
Several studies have demonstrated the potential uses of extracellular vesicles (EVs) for liquid biopsy-based diagnostic tests and therapeutic applications; however, clinical use of EVs presents a challenge as many currently-available EV isolation methods have limitations related to efficiency, purity, and complexity of the methods. Moreover, many EV isolation methods do not perform efficiently in all biofluids due to their differential physicochemical properties. Thus, there continues to be a need for novel EV isolation methods that are simple, robust, non-toxic, and/or clinically-amenable. Here we demonstrate a rapid and efficient method for small extracellular vesicle (sEV) isolation that uses chitosan, a linear cationic polyelectrolyte polysaccharide that exhibits biocompatibility, non-immunogenicity, biodegradability, and low toxicity. Chitosan-precipitated material was characterized using Western blotting, nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and relevant proteomic-based gene ontology analyses. We find that chitosan facilitates the isolation of sEVs from multiple biofluids, including cell culture-conditioned media, human urine, plasma and saliva. Overall, our data support the potential for chitosan to isolate a population of sEVs from a variety of biofluids and may have the potential to be a clinically amenable sEV isolation method.
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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) | 0.000 | 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".