Optimization of Immunohistochemical and Fluorescent Antibody Techniques for Localization of <i>Foot-and-Mouth Disease Virus</i> in Animal Tissues
Bibliographic record
Abstract
Immunohistochemical (IHC) and fluorescent antibody (FA) techniques were optimized for the detection of Foot-and-mouth disease virus (FMDV) structural and nonstructural proteins in frozen and paraformaldehyde-fixed, paraffin-embedded (PFPE) tissues of bovine and porcine origin. Immunohisto-chemical localization of FMDV was compared with 7 detection systems, 8 primary antibodies, and 11 epitope retrieval techniques. All serotypes tested (O, A, Asia, C [cryosection]; O, A, Asia [PFPE]) were localized in association with mature vesicles. Multi-label FA was used in conjunction with IHC and conventional histopathology to characterize vesicle maturation in 4 steers and 2 pigs experimentally infected with FMDV. At the edge of advancing vesicles, a consistent finding was acantholytic degeneration of basal keratinocytes surrounding dermal papillae with suprabasilar clefts and microvesiculation. Progression of microvesiculation led to coalescence with the expanding vesicle. Cells at the leading edge of vesicles were positive for FMDV antigens by IHC and FA. Cell marker profile of these cells by FA was consistent with keratinocytes (i.e., cytokeratin [CK]-positive, S100-negative, MHC-II-negative). In rare instances, CK-negative, MHC-II-positive, and FMDV-positive cells (presumptive dendritic cells or macrophages) were identified within dermis subjacent to vesicles.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".