Cryopreserved whole blood for the quantification of monocyte, T‐cell and NK‐cell subsets, and monocyte receptor expression by multi‐color flow cytometry: A methodological study based on participants from the canadian longitudinal study on aging
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Bibliographic record
Abstract
Immunophenotyping by multi-color flow cytometry is arguably the best tool to identify and quantify distinct cell lineages from the peripheral blood and other biological fluids/tissues. Effective in both clinical and research settings, it can be used to estimate the frequency of a given cell type or measure its phenotypic or functional properties. Normally, immunophenotyping is performed in fresh or fractionated blood (i.e., PBMCs) the same day, or within 24 hours of collection; however, this may not be feasible for all study designs. We have previously shown that cryopreserved blood, a biospecimen that is simple and inexpensive to prepare, is comparable to fresh blood for the enumeration of major leukocyte cell types. For the following study, we sought to extend these observations to distinct subsets of: monocytes (classical, intermediate, and non-classical), T-cells (CD4/CD8 naïve, central and effector memory, senescent, and terminally differentiated, and regulatory T-cells), and NK-cells (CD56 bright and dim); we also examined the expression of monocyte cell-surface receptors CX3CR1, CCR2, TLR2, and TLR4. Our results indicate that cryopreserved blood is comparable to fresh blood; with exception to relatively rare subsets and lowly expressed receptors, the absolute or relative frequency of cell subsets generally correlated >0.80 between blood types, while monocyte receptor expressed was mostly >0.70. Furthermore, the day-to-day coefficient of variation for most cell subsets and parameters was below 20%. Given these findings, we suggest that cryopreserved peripheral blood be given greater consideration for studies in which the quantification of distinct leukocyte subsets is required. © 2018 International Society for Advancement of Cytometry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it