A comprehensive assessment of immunophenotyping performed in cryopreserved peripheral whole blood
Bibliographic record
Abstract
BACKGROUND: Monitoring the frequency and phenotype of white blood cell subsets using flow cytometry (immunophenotyping) has proven to be an incredibly powerful tool in the assessment of health. Although improved technologies have aided in the practical implementation of immunophenotyping in clinical and epidemiological studies, the transportation of blood from the site of collection to a central laboratory for analysis within a reasonable timeframe may not be feasible. Hence, the purpose of the following study was to investigate the validity of cryopreserved whole blood as a simple to prepare and cost-effective biospecimen for multi-colour immunophenotyping in a large epidemiological study-namely, The Canadian Longitudinal Study on Aging (CLSA). METHODS: In fresh and cryopreserved blood and cryopreserved peripheral blood mononuclear cells (PBMCs) we measured cellular viability and the quantities (absolute counts and relative frequencies) of total leukocytes, neutrophils, monocytes, CD4/CD8 T-lymphocytes, B-lymphocytes, natural killer (NK) cells, NKT cells, plasmacytoid dendritic cells (pDCs), and basophils. RESULTS: Estimates obtained from immunophenotyping in cryopreserved blood were comparable to fresh blood (Avg. rho: absolute cell counts = 0.71, frequency relative to CD45 leukocytes = 0.84, frequency relative to PBMCs = 0.86), cryopreserved PBMCs (0.86), and complete blood counts by hematological analyzer (0.71) and exhibited good intra- and inter-assay precision (Avg. CV = 4% and 3%, respectively). CONCLUSIONS: Given this, cryopreserved blood should be considered a feasible biospecimen in clinical and epidemiological studies requiring leukocyte immunophenotyping. © 2017 International Clinical Cytometry Society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".