When monocytes and platelets compete: The effect of platelet count on the flow cytometric measurement of monocyte CD36
Bibliographic record
Abstract
BACKGROUND: Flow cytometric measurement of monocyte surface CD36 is relevant to several conditions including diabetes, cardiovascular disease, lipid disorders, platelet isoimmunization, and susceptibility to P falciparum malaria. CD36 is also strongly expressed on platelets where it is also known as platelet glycoprotein IV. METHODS: Whole blood samples, containing identical monocyte concentrations, were adjusted to contain platelets ranging from 20,000/uL to 600,000/uL, were stained with fluorescent-labeled anti-CD36, and analyzed by flow cytometry. RESULTS: CD36 median fluorescent intensity (MFI) observed on monocytes decreased as the platelet concentration in the sample increased with more than a 50% decline in monocyte MFI over the normal range of platelet values. The effect was not abolished by using larger volumes of monoclonal antibody and was observed with different clones of reagent anti-CD36. The findings were most consistent with competition by platelets for the CD36 reagent. Similar findings were observed with antibody to class I HLA. Under defined assay conditions, monocyte CD36 MFI declined with rising platelet concentration in a predictable fashion following an inverse linear relationship. CONCLUSIONS: Measurement of CD36 expression on monocytes by flow cytometry in whole blood samples is affected by the sample platelet count. When comparing the monocyte CD36 expression among different individuals, our approach can be used to adjust measured monocyte CD36 expression for the effect of the platelet concentration in the sample. Competition by platelets for monoclonal reagents may occur in other settings when whole blood assays are used and when the target antigen is strongly expressed on both platelets and leukocytes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".