A novel ELISA for eosinophil peroxidase provides a sensitive high throughput assay for eosinophil degranulation in either mouse or human biological samples (65.29)
Bibliographic record
Abstract
Abstract Eosinophils comprise only 1-3% of circulating leukocytes but are often a diagnostic feature associated with a variety of inflammatory and disease states, including parasitic and fungal infections, allergic diseases (e.g., asthma, rhinitis and sensitivities to specific foods), cancer, and transplant rejection. The accurate evaluations of eosinophilia as well as the release of stored eosinophil granule proteins are important for the monitoring disease progression and/or assessing the effectiveness of a given treatment strategy(ies). Despite the availability of detection methods for each of the eosinophil granule proteins logistical difficulties limiting either the assays specificity and/or sensitivity have prevented their extensive use. We have generated unique eosinophil peroxidase specific monoclonal antibodies (EPX-mAb) and a high throughput sandwich ELISA assay as a means of overcoming these difficulties. Our EPX-mAb based ELISA is ~10 times more sensitive than traditional OPD based activity assays and allows detection of EPX in mouse BAL fluid after an acute OVA protocol as well as EPX release from mouse eosinophils stimulated ex vivo. The assay is highly specific and gives no signal in samples from EPX deficient mice even with massive eosinophilia (e.g., EPX deficient IL-5 transgenic mice). More significantly, this assay detects EPX in human tissue extracts and biological fluids and thus represents novel diagnostic assay previously unavailable in clinical 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".