ELISA and Multiplex Technologies for Cytokine Measurement in Inflammation and Aging Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it