Quantification of Serum Ovalbumin-specific Immunoglobulin E Titre via in vivo Passive Cutaneous Anaphylaxis Assay
Bibliographic record
Abstract
Murine models of allergic airway disease are frequently used as a tool to elucidate the cellular and molecular mechanisms of tissue-specific asthmatic disease pathogenesis. paramount to the success of these models is the induction of experimental antigen sensitization, as indicated by the presence of antigen-specific serum immunoglobulin E. The quantification of antigen-specific serum lgE is routinely performed via enzyme-linked immunosorbent assay. However, the reproducibility of these in vitro assays can vary dramatically in our experience. Furthermore, quantifying lgE via in vitro methodologies does not enable the functional relevance of circulating lgE levels to be considered. As a biologically appropriate alternative method, we describe herein a highly reproducible in vivo passive cutaneous anaphylaxis assay using Sprague Dawley rats for the quantification of ovalbumin-specific lgE in serum samples ovalbumin-sensitized murine models. Briefly, this in vivo assay involves subcutaneous injections of serum samples on the back of a Sprague Dawley rat, followed 24 h later by intravenous injection of ovalbumin and a blue detection dye. The subsequent result of antigen-lgE mediated inflammation and leakage of blue dye into the initial injection site indicates the presence of ovalbumin-specific lgE within the corresponding serum sample.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".