The challenge of measuring <scp>IL</scp>‐33 in serum using commercial <scp>ELISA</scp>: lessons from asthma
Bibliographic record
Abstract
BACKGROUND: Interleukin-33 (IL-33) has been subject of extensive study in the context of inflammatory disorders, particularly in asthma. Many human biological samples, including serum, have been used to determine the protein levels of IL-33, aiming to investigate its involvement in asthma. Reliable methods are required to study the association of IL-33 with disease, especially considering the complex nature of serum samples. OBJECTIVE: We evaluated four IL-33 ELISA kits, aiming to determine a robust and reproducible approach to quantifying IL-33 in human serum from asthma patients. METHODS: IL-33 levels were investigated in serum of well-defined asthma patients by the Quantikine, DuoSet (both R&D systems), ADI-900-201 (Enzo Life Sciences), and SKR038 (GenWay Biotech Inc San Diego USA) immunoassays, as well as spiking experiments were performed using recombinant IL-33 and its soluble receptor IL-1RL1-a. RESULTS: We show that 1) IL-33 is difficult to detect by ELISA in human serum, due to lack of sensitivity and specificity of currently available assays; 2) human serum interferes with IL-33 quantification, in part through IL-1RL1-a; and 3) using non-serum certified kits may lead to spurious findings. CONCLUSION AND CLINICAL RELEVANCE: If IL-33 is to be studied in the serum of asthma patients and other diseases, a more sensitive and specific assay method is required, which will be vital for further understanding and targeting of the IL-33/IL-1RL1 axis in human disease.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".