Identification of<i>E. coli</i>K88 Receptor in Porcine Intestinal Mucus using Anti-idiotypic Antibodies
Bibliographic record
Abstract
Escherichia coli K88 receptors were purified from porcine intestinal mucus by affinity chromatography using a K88 fimbrial antigen attached to Sepharose 4B. Receptor eluate from the column was identified using anti-idiotypic antibodies that bore an internal image to K88. They were produced in chickens against both mouse anti-K88 monoclonal and rabbit anti-K88 polyclonal antibodies. The anti-idiotypic antibodies were tested against purified receptors using an indirect ELISA. Higher absorbance values (1.3 and 1.2) were observed for the anti-polyclonal anti-idiotypic antibodies than those for anti-monoclonal anti-idiotypic antibodies (0.95 and 0.56). Analysis of purified receptors by SDS-PAGE electrophoresis and immunoblotting with K88 fimbriae revealed several bands corresponding to 40, 45, 50 and 70 kDa. However, the anti-polyclonal anti-idiotypic antibodies recognized only the 70 kDa protein while the anti-monoclonal anti-idiotypic antibodies failed to recognize any of these proteins. Sugar staining suggested that only the 50 kDa protein contained a sugar moiety. This study indicated that a 70 kDa protein in the intestinal mucus of pigs may be a dominant receptor for K88 and demonstrated that anti-idiotypic antibodies are useful tools for receptor identification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".