Effect of freezing of sputum samples on flow cytometric analysis of lymphocyte subsets
Bibliographic record
Abstract
Sputum samples should be processed shortly after induction to prevent cell degradation. For intermediate storage, freezing of homogenised samples or immediate fixation have been shown to be suitable for cytospins. The aim of this study was to investigate whether freezing or immediate fixation of sputum affect the analysis of lymphocyte subsets by flow cytometry. Selected plugs from 24 sputum samples were homogenised. One aliquot was processed immediately and analysed by flow cytometry. A second aliquot was homogenised, frozen at -20 C after addition of dimethylsulfoxide and stored for a median time of 6 days. In six samples a third aliquot was fixed in formalin after induction and stored for up to 72 h before further processing. Compared to immediate processing, percentages of total lymphocytes and T-suppressor cells were elevated after being frozen, with a minor decrease in the T4/T8 ratio. Proportions of total lymphocytes, T-helper and T-suppressor cells correlated between native and frozen samples, intra-class correlation coefficients being 0.74, 0.85 and 0.70, respectively. The formalin-fixed aliquots could not be analysed with the antibodies used. In conclusion, freezing seems to be a suitable technique to store sputum samples for flow cytometry of CD3, CD4 and CD8 lymphocyte subsets. Its effects were minor compared to the variation between subjects.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".