Concentration of Lipocalin Region of Collagen XXVII Alpha 1 in the Serum of Dogs with Hemangiosarcoma
Bibliographic record
Abstract
BACKGROUND: Hemangiosarcoma (HSA) is a common malignancy of dogs with characteristic early, aggressive metastasis. Diagnosis of HSA is challenging because of lack of sensitive and specific diagnostic tests. HYPOTHESIS: Specific proteins that are increased in serum of dogs with HSA might represent useful biomarkers of the disease. ANIMALS: Thirty-four dogs with HSA and 42 healthy dogs from the Ontario Veterinary College Teaching Hospital. METHODS: This case-control study compared serum proteins in dogs with HSA and healthy dogs. Proteins were separated by 2-dimensional difference gel electrophoresis and identified by liquid chromatography and tandem mass spectrometry. RESULTS: Western blot analysis showed that serum collagen XXVII peptide concentration in serum of dogs with large metastatic HSA burdens (1,488, 231-3,754 DU; median, minimum-maximum); was, on average, 9.5-fold higher than in healthy dogs (156; 46-2,101 DU). While concentrations for dogs with osteosarcomas (678; 124-3,251 DU), lymphomas (423; 92-2,777 DU), carcinomas (1,022; 177-3,448 DU), and inflammatory disease were also increased, values were consistently lower than those for HSA. Receiver operating characteristic curves revealed an estimated area under the curve of 83% for HSA cases whereas areas for other neoplastic and nonneoplastic diseases were nondiscriminatory. Serum collagen XXVII peptide concentration before splenectomy (1,350; 1,156-1,929 DU) was reduced after tumor removal (529; 452-562 DU) and chemotherapy but increased in 2 dogs with tumor recurrence (511-945 DU; 493-650 DU). CONCLUSIONS AND CLINICAL IMPORTANCE: Collagen XXVII peptide might be useful for diagnosis and monitoring of advanced HSA.
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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 | 0.001 |
| 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".