Microparticles as biomarkers of lung disease: enumeration in biological fluids using lipid bilayer microspheres
Bibliographic record
Abstract
Extracellular vesicles, specifically microparticles (MPs), are rapidly gaining attention for their capacity to act as biomarkers for diagnosis, prognosis, or responsiveness to therapy in lung disease, in keeping with the concept of precision medicine. However, MP analysis by high-sensitivity flow cytometry (FCM) is complicated by a lack of accurate means for MP enumeration. To address this gap, we report here an enhanced FCM MP gating and enumeration technique based on the use of novel engineered lipid bilayer microspheres (LBMs). By comparison of LBM-based MP enumeration with conventional bead- or fluorescent-based FCM enumeration techniques and a gravimetric consumption gold standard, we found LBMs to be superior to commercial bead preparations, showing the smallest fixed bias and limits of agreement in Bland Altman analyses. LBMs had simultaneous capacity to aid FCM enumeration of MPs in plasma, BAL, and cell culture supernatants. LBM enumeration detected differences in MP counts in mice exposed to intraperitoneal lipopolysaccharide or saline. LBMs provided for 1) higher sensitivity for gating MPs populations, 2) reduced background within MP gates, 3) more appropriate size, and 4) an inexpensive alternative amenable to different fluorescent tags. LBM-based MP enumeration was useful for a series of different FCM systems assessed, whereas LBM gating benefited high- but not low-sensitivity FCM systems compared with fluorescence gating. By offering exclusive advantages over current means of gating and enumerating MPs, LBMs are uniquely suited to realizing the potential of MPs as biomarkers in biological lung fluids and facilitating precision medicine in lung disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".