Detectability of cytokine and chemokine using ELISA, following sample-inactivation using Triton X-100 or heat
Bibliographic record
Abstract
Clinical samples are routinely inactivated before molecular assays to prevent pathogen transmission. Antibody-based assays are sensitive to changes in analyte conformation, but the impact of inactivation on the analyte detectability has been overlooked. This study assessed the effects of commonly used inactivation-methods, Triton X-100 (0.5%) and heat (60 °C, 1 h), on cytokine/chemokine detection in plasma, lung aspirates, and nasopharyngeal samples. Heat significantly reduced analyte detectability in plasma (IL-12p40, IL-15, IL-16, VEGF, IL-7, TNF-β) by 33-99% (p ≤ 0.02), while Triton X-100 minimally affected analytes in plasma and nasopharyngeal samples (11-37%, p ≤ 0.04) and had no significant impact on lung aspirates. Structural analysis revealed that cytokines affected by heat had more hydrophobic residues and higher instability-indices. As the protein-detectability was affected differently in different sample types, the sample environment could also influence protein stability. This underscores the importance of selecting the most suitable inactivation methods for clinical samples to ensure accurate cytokine/chemokine analysis in both clinical and research settings.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".