The impact of sample processing on inflammatory markers in serum: Lessons learned
Bibliographic record
Abstract
Objectives: To investigate the effect of sample handling on inflammatory cytokines in serum and highlight challenges with using samples pre-collected from biobanks for biomarker research.Methods: Cytokine concentrations (IL-1β, IL-2, IL-6, IL-8, IL-10, TNFα, and IFNγ) were measured in serum samples of 205 patients with bipoldar disorder (BD) from the Mayo Clinic Bipolar Disorder Biobank and 205 non-psychiatric controls from the Mayo Clinic Biobank. As cytokine concentrations varied by recruitment site, post-hoc models were used to test the effect of clinical variables and pre-processing time on cytokines. To evaluate the effect of pre-processing time experimentally, cytokines were assayed in serum and plasma from 6 healthy volunteers processed at different time points.Results: Cytokine levels were significantly higher in the BD group. However, both cytokine levels and pre-processing times differed by recruitment site, and post-hoc analyses revealed that pre-processing time was significantly associated with several cytokines. An experiment using samples from healthy volunteers confirmed that concentrations for most cytokines increased with longer pre-processing times.Conclusions: Delays in processing influence cytokine concentrations in blood samples. Given the increasing use of biobanks in research, this study highlights the need to carefully evaluate sample collection and handling methods when designing biomarker studies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".