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 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) | 0.001 | 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".