Stability of cytokines in supernatants of stimulated mouse immune cells
Bibliographic record
Abstract
Measurements of cytokines in cell culture supernatants are widely used to evaluate the immune response. Cytokine levels in secretomes are usually quantified using enzyme-linked immunosorbent assays (ELISA), which have easy, sensitive, specific, rapid, cost-effective, and reproducible protocols. To our knowledge, the stability of cytokines in secretomes has not been hitherto investigated. We present data that involve; time-dependent changes during storage at +4°C, and the effects of freeze-thaw cycles in samples frozen at -80(o)C, instant freezing of samples with liquid nitrogen, and addition of protease inhibitors on the stability of certain cytokines (TNF-α, MIP-2, IFN-γ, IL-6, IL-10, IL-17A), in secrotomes of spleen and lymph nodes from tumor-bearing animals. Our results show that IL-6 remains stable, MIP-2, IFN-γ and IL-10 are somewhat stable, while TNF-α and IL-17A are degradable cytokines: instant freezing by liquid nitrogen or adding protease inhibitor does not preserve the stability of these cytokines. From these results it can be concluded that, if possible, TNF-α measurements should be perform in fresh samples, and IL-17A and IL-10 samples can be stored at -80°C, but should be used at the first thaw.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".