Protein Expression Profiling Reveals Distinctive Changes in Serum Proteins Associated With Chronic Pancreatitis
Bibliographic record
Abstract
OBJECTIVE: Testing of serum for protein patterns to monitor progression of suspected to definite chronic pancreatitis (CP). METHODS: Serum samples of CP patients and healthy volunteers were fractionated on anion exchange columns and analyzed by surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry to elucidate CP-related protein alterations and to identify biomarkers for this disease. Potential biomarkers were purified and identified by mass spectrometry. RESULTS: In total, 258 protein peaks were found that discriminated between the 2 groups. Analysis revealed 28 most prominent peaks on immobilized metal affinity capture coupled with Cu and CM10 protein chips, covering the m/z range between 3.3 and 33.3 kd. Performing multivariate pattern analysis, the best pattern model was obtained using fraction 6 on immobilized metal affinity capture coupled with Cu arrays with a sensitivity of 96% and a specificity of 84%. Using a combination of matrix-assisted laser desorption-ionization-time-of-flight mass spectrometry and immunodepletion, we identified 14-m/z peaks. The proteins were found to be significantly decreased in CP serum and were identified as retinol-binding protein, serum amyloid-alpha, apolipoprotein A-II (Apo A-II), Apo C-I, Apo C-II, Apo C-III, and transthyretin and truncated forms thereof. CONCLUSIONS: Distinct protein profile differences exist between normal and CP serum and reflect the metabolic and inflammatory condition in CP patients. The identified protein panel may eventually serve as a diagnostic marker set for CP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".