ELISA and Multiplex Technologies for Cytokine Measurement in Inflammation and Aging Research
Bibliographic record
Abstract
Over the last decade there has been an enormous expansion of research focused on defining the role of inflammation in aging, age-related diseases, disability, and frailty. The availability of methods to measure cytokines and other inflammatory mediators or markers with high sensitivity and specificity is critically important. Enzyme-linked immunosorbent assay (ELISA), the most widely used and best validated method, is limited by its ability to measure only a single protein in each sample. Recent developments in serum cytokine quantification technology include multiplex arrays, which offer the potential of better evaluating the complexity and dynamic nature of inflammatory responses and offer substantial cost and sample savings over traditional ELISA measurements. Despite potential advantages of this new technology, experience with these techniques is limited, and it has not emerged to date as the gold standard in inflammatory mediator measurement. This article reviews ELISA and the emerging multiplex technologies, compares the cost and effectiveness of recently developed multiplex arrays with traditional ELISA technology, and provides specific recommendations for investigators interested in measuring serum inflammatory mediators in older adults.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".