Comparative proteomic characterization of articular cartilage tissue from normal donors and patients with osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To identify potential molecular mediators and biomarkers for osteoarthritis (OA), through comparative proteomic analysis of articular cartilage tissue obtained from normal donors without OA (n = 7) and patients with OA (n = 7). METHODS: The proteomic analyses comprised extraction of soluble proteins from cartilage, separation of the protein mixtures by sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by in-gel digestion, and subsequent nano-liquid chromatography-tandem mass spectrometry analysis in conjunction with a database search for protein identification and semiquantitation. RESULTS: A total of 814 distinct proteins were identified with high confidence from 14 samples; 420 of these proteins were detected with > or = 3 unique peptides in at least 4 samples from the same group. Using stringent criteria, 59 proteins were found to be differentially expressed in OA cartilage. Gene Ontology and Ingenuity pathway analysis tools were used to characterize these proteins into functional categories. One of the up-regulated proteins, HtrA1, a serine protease, was detected at high levels in cartilage. CONCLUSION: Altered protein expression in the disease state is associated with many aspects of the pathogenesis of OA, such as increased proteolysis, lipid metabolism, immune response, and decreased signal transduction. To our knowledge, this is the first time that a large portion of these proteins and their expression patterns were identified in cartilage, thus providing new insights for finding novel pathologic mediators and biomarkers of OA.
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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) | 0.001 | 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) | 0.002 | 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".