Novel test for microparticles in platelet‐rich plasma and platelet concentrates using dynamic light scattering
Bibliographic record
Abstract
BACKGROUND: The level and clinical importance of platelet (PLT)-derived microparticles (PMPs) in PLT-rich plasma (PRP) and PLT transfusions is largely unknown due to the lack of technology to routinely determine the number and size of PMP in PLT samples. Dynamic light scattering (DLS) is ideally suited to measure particles of submicron size but has previously been limited to the analysis of PLT-free samples. STUDY DESIGN AND METHODS: PMPs were enumerated in 81 PRP and 79 apheresis PLT concentrate (APC) samples from the same donors using ThromboLUX (LightIntegra Technology, Inc.), a new DLS PLT quality test. The ThromboLUX results were compared with flow cytometry. Phase contrast and differential interference contrast (DIC) microscopy were used to qualitatively determine PMP levels. RESULTS: The relative counts of PMPs measured by flow cytometry strongly correlated with the relative light scattering intensities of the PMP determined by ThromboLUX in both PRP (R = 0.7596, p < 0.0001) and APC (R = 0.6572, p < 0.0001) samples. High or low PMP levels in PLT samples were confirmed by phase contrast and DIC microscopy. The mean PMP radius measured with ThromboLUX, an absolute sizing technology, was 117.1 ± 77.6 nm as determined from the distribution of PMP content in all PLT samples investigated in this study. CONCLUSIONS: Correlation with flow cytometry and microscopy showed that ThromboLUX is well suited to measure PMP concentration and size distribution in PLT concentrate samples. In combination with noninvasive sampling, ThromboLUX could provide routine microparticle enumeration of PLT-containing samples.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".