Development of an antigen-capture enzyme-linked immunosorbent assay for <i>Clostridium perfringens</i> beta2-toxin in porcine feces and the neonatal piglet intestine
Bibliographic record
Abstract
An enzyme-linked immunosorbent assay (ELISA) was developed for detection and quantitation of beta2-toxin in neonatal piglet intestinal contents. Polystyrene plates were coated with polyclonal capture antibodies prepared against consensus recombinant beta2-toxin. The ELISA was developed using consensus recombinant beta2-toxin, atypical recombinant beta2-toxin, purified consensus native beta2-toxin, and field samples of neonatal porcine intestinal contents. Captured antigen was detected using a horseradish peroxidase-labeled monoclonal antibody against consensus recombinant beta2-toxin. The limit of detection of the ELISA for consensus beta2-toxin was between 2.0 and 3.5 ng/ml. The ELISA detected atypical recombinant beta2-toxin only weakly. Optical density was protein concentration dependent. The test confirmed differences between consensus and atypical recombinant beta2-toxin, but similar results obtained when testing pure consensus recombinant beta2-toxin and native beta2-toxin. Results obtained from intestinal content samples, particularly from the small intestine, were highly inconsistent and suggested variable protease activity. Addition of protease inhibitors partially prevented degradation of the toxin; however, sample processing at low temperature, at a lower pH (citrate buffer with 5% of bovine serum albumin, pH 6.1), and "cold incubation" of applied antigens abolished protease activity. The recombinant toxin was preserved in spiked intestinal samples by freezing at -70°C, suggesting that necropsy samples can be stored frozen for periodic testing. With appropriate sample preparation, antigen-capture ELISA can detect beta2-toxin in the intestinal content and feces of neonatal piglets.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".