Identification of potential plasma protein biomarkers for feline pancreatic carcinoma by liquid chromatography tandem mass spectrometry
Bibliographic record
Abstract
In both humans and cats, pancreatic carcinoma is an aggressive cancer with a grave prognosis. Proteomics techniques have successfully identified several blood-based biomarkers of human pancreatic neoplasia. Thus, this study aims to investigate whether similar biomarkers can be identified in the plasma of cats with FePAC by using liquid chromatography tandem mass spectrometry (LC-MS/MS). To facilitate evaluation of the low abundance plasma proteome, a human-based immunodepletion device (MARS-2) was first validated for use with feline plasma. Marked reduction and/or complete removal of albumin and immunoglobulins was confirmed by analysis of electrophoretograms and mass spectral data. Subsequently, plasma collected from 9 cats with pancreatic carcinoma (FePAC), 10 cats with symptomatic pancreatitis, and 10 healthy control cats was immunodepleted and subjected to LC-MS/MS. Thirty-seven plasma proteins were found to be differentially expressed (p < .05 in one-way ANOVA, FC >2 in fold change analysis). Among these proteins, ETS variant transcription factor 4 (p < .05) was overexpressed, while gelsolin (p < .01), tryptophan 2,3-dioxygenase (p < .05), serpin family F member 1 (p < .01), apolipoprotein A-IV (p < .01) and phosphatidylinositol-glycan-specific phospholipase D (p < .05) were down-regulated in cats with FePAC. Further studies on these potential biomarkers are needed to investigate their diagnostic value.
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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 | 0.001 | 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) | 0.000 | 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".