Evaluating the long-term biological stability of cytokine biomarkers in ocular fluid samples
Bibliographic record
Abstract
PURPOSE: The quality of biological fluid samples is vital for optimal preanalytical procedures and a requirement for effective translational biomarker research. This study aims to determine the effects of storage duration and freeze-thawing on the levels of various cytokines in the human aqueous humour and vitreous samples. METHODS AND ANALYSIS: Human ocular aqueous humour and vitreous samples were obtained from 25 eyes and stored at -80°C for analysis. All samples were assayed for 27 cytokine biomarker concentrations (pg/mL) using a multiplex assay. Four sample storage durations following sample collection were evaluated (1 week, 3 months, 9 months and 15 months). Additionally, samples underwent up to three freeze-thaw cycles within the study period. RESULTS: Among the 27 cytokine biomarkers, concentrations of four cytokines (Interleukin (IL)-2, IL-10, IL-12 and platelet-derived growth factor-BB) were significantly decreased by storage duration at all time points, as early as 3 months following sample collection (range of 9%-37% decline between 1 week and 15 months, p<0.001). Freeze-thawing of up to three cycles did not significantly impact the cytokine biomarker concentrations in aqueous humour or vitreous. Separability of patient-specific cytokine biomarker profiles in the principal component analysis remained relatively the same over the 15 months of storage duration. CONCLUSION: The findings from this study suggest that several intraocular cytokine biomarkers in human aqueous humour and vitreous samples may be susceptible to degradation with long-term storage, as early as 3 months after collection. The overall patient-specific cytokine biomarker profiles are more stable than concentrations of individual cytokines. Future studies should focus on developing guidelines for optimal and standardised sample handling methods to ensure correct research findings about intraocular biomarkers are translated into clinical practice.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".