Detectability of Cytokine and Chemokine using ELISA, following Sample-inactivation using Triton X-100 or Heat
Bibliographic record
Abstract
Abstract Background Routine characterization of clinical samples for their immunological responses against infectious pathogens often involves assessing cytokine/chemokine profiles and/or production of pathogen-specific antibodies. To prevent transmission of infectious materials in laboratories, these clinical samples are often inactivated by detergents or heat before the molecular assays are performed. Antibody-based molecular assays, such as ELISA, are highly sensitive to conformational changes in analytes. How specific inactivation procedures impact on cytokine/chemokine detectability in the clinical samples is not fully elucidated. This study compared two commonly used inactivation methods (Triton X-100, heat-inactivation) and untreated native samples in the cytokine/chemokine assays. Method Plasma, endotracheal tube aspirate (ETTA), and nasopharyngeal (NP) samples underwent inactivation with 0.05% Triton X-100 or heat (60°C, 1 hour). Cytokines/chemokine levels were assessed using Meso-Scale-Multi-Spot assays. Data were analyzed against untreated samples using one-way and Tukey’s multiple comparisons tests. Additionally, the conformational instability of cytokines/chemokines, predicted by their amino acid sequence, was examined to determine its contribution to detectability in inactivated samples. Results Heat treatment significantly impacted cytokine/chemokine detection across sample types. IL-1α levels were substantially reduced in ETTA, NP, and plasma samples. In heat-inactivated plasma, IL-12p40, IL-15, IL-16, VEGF, IL-7, and TNF-β, among 36 cytokines, were reduced by 33-99% (p-values ≤0.02). Conversely, Triton X-100 minimally affected cytokine/chemokine detection in plasma and NP samples by 11-37% (p-values ≤0.04). Triton X-100 increased the detection of IL-15, IL-16, IL-1α, VEGF, and IL-7 levels in NP samples. Triton X-100-inactivated ETTA samples showed no significant impact on cytokine/chemokine detectability. Heat inactivation had more profound impacts on protein detectability. Structural analysis revealed heat-affected cytokines had more hydrophobic residues and higher instability indices, although protein features alone could not reliably predict susceptibility. Conclusion Our findings demonstrated the importance of empirical assessments of inactivation protocols in the measurements of cytokine/chemokine responses in clinical samples. Overall, Triton X-100 performed better than heat inactivation in preserving protein conformation for antibodies-based immunological studies.
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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 | 0.001 |
| 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) | 0.004 | 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".