Feline leukemia virus detection in corneal tissues of cats by polymerase chain reaction and immunohistochemistry
Bibliographic record
Abstract
OBJECTIVES: To determine the presence of feline leukemia virus (FeLV) in the corneas of FeLV-infected cats. ANIMALS STUDIED: Thirty-four random source cats. PROCEDURES: Seventeen cats were found positive for FeLV serum p27 antigen by enzyme-linked immunosorbent assay (ELISA). Twelve ELISA positive cats were also positive on peripheral blood samples by immunofluorescent antibody (IFA) testing. Seventeen ELISA negative cats served as negative controls. Full thickness corneal specimens were collected from the left cornea of all cats and analyzed for FeLV proviral DNA and gp70 antigen by polymerase chain reaction (PCR) and immunohistochemical (IHC) testing, respectively. RESULTS: Eleven (64.7%) positive corneal PCR results were obtained from 17 ELISA positive cats. Of 12 cats that were both ELISA and IFA positive on peripheral blood, 10 (83.3%) had positive corneal PCR results. All corneal tissues from ELISA negative cats were PCR negative. IHC staining of corneal sections revealed the presence of FeLV gp70 in corneal tissues of nine (52.9%) ELISA positive cats. Of the 12 cats that were both ELISA and IFA positive on peripheral blood, eight (66.7%) had positive corneal IHC results. Positive IHC staining was localized to the corneal epithelium. Corneal tissues of all ELISA negative cats and all IFA negative cats were negative on IHC testing. CONCLUSIONS: FeLV antigens and proviral DNA are present in corneal tissues of some FeLV-infected cats. Screening corneal donors for FeLV infection is warranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".