Isolation of bacterial extracellular vesicles from raw samples using a portable microstructured electrochemical device
Bibliographic record
Abstract
Bacterial extracellular vesicles (EVs) are nanosized vesicles released by both Gram-negative and Gram-positive bacteria, playing critical roles in microbial communication, host-pathogen interactions, and immune modulation. Despite their significance in research and clinical applications, conventional isolation methods, such as ultracentrifugation (UC), are often slow, labor-intensive, and susceptible to contamination. In this study, we evaluated a novel portable microstructured electrochemical device (PMED) designed for rapid and selective bacterial EV isolation directly from biological samples. Using immunoaffinity-based capture and voltage-triggered release, the device-isolated EVs from Gram-negative Escherichia coli (E. coli), Gram-positive Lactobacillus fermentum (Lb. fermentum) culture supernatants and from urine samples spiked with E. coli , showing superior purity compared to UC. Characterization through nanoparticle tracking analysis (NTA), dynamic light scattering (DLS), and Western blot confirms enhanced selectivity and reduced contaminants. Functional assays demonstrated that device-isolated Lb. fermentum EVs selectively activated Toll-like receptor 4 (TLR4) without triggering TLR2, unlike UC-isolated EVs, suggesting a more refined immunomodulatory effect. These findings highlight the device's translational potential for EV-based diagnostics, particularly for noninvasive urinary tract infection detection, and its broader applications in studying bacterial communication and immune regulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".