Identification of protein biomarkers in Dupuytren's contracture using surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS).
Bibliographic record
Abstract
BACKGROUND: To study the protein expression profiles associated with Dupuytren's contracture (DC) to identify potential disease protein biomarkers (PBM) using a proteomic technology--Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). METHODS: Normal and disease palmar fascia from DC patients were analyzed using Ciphergen's SELDI-TOF-MS Protein Biological System II (PBSII) ProteinChip reader. Analysis of the resulting SELDI-TOF spectra was carried out using the peak cluster analysis program (BioMarker Wizard, Ciphergen). Common peak clusters were then filtered using a bootstrap algorithm called SAM (Significant Analysis of Microarrays) for increased fidelity in our analysis. RESULTS: Several differentially expressed low molecular weight (<20 kDa) tissue proteins were identified. Spectra generated using both ZipTipC18 aided Au array and WCX2 array based SELDI-TOF-MS were reproducible, with an average peak cluster mass standard deviation for both methods of <1.74 x10(-4). Initial peak cluster analysis of the SELDI spectra identified both up-(14) and down-(3)regulated proteins associated with DC. Further analysis of the peak cluster data using the bootstrap algorithm SAM identified three disease-associated protein features (4600.8 Da, 10254.5 Da, and 11405.1 Da) that were elevated (5.45, 11.7, and 4.28 fold, respectively, with a 0% median false discovery rate). CONCLUSION: SELDI-TOF-MS identified three potential low molecular weight tissue protein markers (p4.6DC, p1ODC, p11.7DC) for DC. The ability of SELDI-TOF-MS to resolve low molecular weight proteins suggests that the method may provide a means of deciphering the biomarker-rich low molecular weight region of the human proteome. Application of such novel technology may help clinicians to focus on specific molecular abnormalities in diseases with no known molecular pathogenesis, and uncover therapeutic and/or diagnostic targets.
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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 | 0.001 |
| 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".