Long-term storage stability of plasma TNF-α and IL-6 concentrations of psychiatric emergency patients: The Signature Biobank
Bibliographic record
Abstract
Cytokines are increasingly incorporated in psychiatric research. While biobanking provides advantages for different study designs, long-term storage could degrade cytokine quality and introduce bias. To date, reports on cytokine stability are based on small sample sizes ( n < 20) and do not address long-term (4+ years) storage effects. This article reports two studies evaluating IL-6 and TNF-α measurements in human plasma samples biobanked for long periods. In the first study, with samples stored between 5 and 139 months (11.6 years), IL-6 concentrations were not significantly correlated with storage length in all available baseline data of the Signature Biobank (n[IL-6] = 1206, n[TNF-α] = 1223). TNF-α correlated negatively with storage length ( r = −0.217; p < 0.001) indicating a decrease of concentrations with longer storage. In the second study, IL-6 and TNF-α concentrations were measured twice in the same plasma samples of 50 psychiatric participants from the Signature Biobank stored between analyses from 32 to 45 months. We assessed if storage effects differed between analytes. In IL-6, we observe a relatively good stability in samples stored up to 6 years. For TNF-α, we observed only a moderate stability of measurements (rTNF-α = 0.59; rIL-6 = 0.71) with linear decrease over time. As a potential solution, a corrective equation extracted from Study 1 was applied to TNF-α in the Study 2 sample; however, this did not improve correlation coefficients but might be useful in other settings. Integrating samples' age in statistical analyses and/or more systematic quality controls could mitigate degradation process. • Plasma IL-6 & TNF-α seem stable for up to 6 years at −80 °C. • TNF-α appears sensitive to storage time superior to 6 years and degrades. • IL-6 appears stable when stored over 11 years. • Statistical control for storage length could account for long storage degradation. • Further biobank studies are needed to confirm rates of cytokine degradation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".