A comparative proteomic study of plasma in feline pancreatitis and pancreatic carcinoma using 2-dimensional gel electrophoresis to identify diagnostic biomarkers: A pilot study.
Bibliographic record
Abstract
While pancreatitis is now recognized as a common ailment in cats, the diagnosis remains challenging due to discordant results and suboptimal sensitivity of ultrasound and specific feline pancreatic lipase (Spec fPL) assay. Pancreatitis also shares similar clinical features with pancreatic carcinoma, a rare but aggressive disease with a grave prognosis. The objective of this pilot study was to compare the plasma proteomes of normal healthy cats (n = 6), cats with pancreatitis (n = 6), and cats with pancreatic carcinoma (n = 6) in order to identify potential new biomarkers of feline pancreatic disease. After plasma protein separation by 2-dimensional gel electrophoresis, protein spots were detected by Coomassie Brilliant Blue G-250 staining and identified by mass spectrometry. Alpha-1-acid glycoprotein (AGP), apolipoprotein-A1 (Apo-A1), and apolipoprotein-A1 precursor (Pre Apo-A1) appeared to be differentially expressed, which suggests the presence of a systemic acute-phase response and alteration of lipid metabolism in cats with pancreatic disease. Future studies involving greater case numbers are needed in order to assess the utility of these proteins as potential biomarkers. More sensitive proteomic techniques may also be helpful in detecting significant but low-abundance proteins.
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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 | 0.001 | 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".