Development of a Monoclonal Antibody-Based Enzyme-Linked Immunosorbent Assay To Quantify Soluble β-Glucans in Oats and Barley
Bibliographic record
Abstract
A set of 31 murine monoclonal antibodies was produced against (1-->3,1-->4)beta-d-glucan from oats (Avena sativa L.) chemically cross-linked to keyhole limpet hemocyanin. Monoclonal antibodies were tested for their cross-reactivity to related and unrelated polysaccharides. The antibodies reacted strongly to unmodified beta-glucan from oats and barley (Hordeum vulgare L.) and to lichenan from Icelandic moss, a polysaccharide with a structure similar to that of beta-glucan but which is not encountered in cereals. Cross-reaction to other polysaccharides tested was minimal at physiological levels. An enzyme-linked immunosorbent assay (ELISA) that could routinely detect and quantify nanogram levels of soluble beta-glucan extracted from the flour of oats or barley was designed with one of these monoclonal antibodies. The beta-glucan extraction procedure from ground oat and barley samples and the ELISA were both optimized for reproducibility, accuracy, and throughput, and results were compared to values obtained from an established, commercially available enzyme-based assay. Correlations between the two assays were consistently high (r (2) > 0.9), indicating that the ELISA presented in this paper is a valuable alternative for assaying beta-glucan levels in cereals and cereal products, both routinely and in preparations in which beta-glucans are present in nanogram amounts. Development of the extraction procedure for ELISA is discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".