Identification of altered protein abundances in cholesteatoma matrix via mass spectrometry-based proteomic analysis
Bibliographic record
Abstract
BACKGROUND: Cholesteatoma are cyst-like structures lined with a matrix of differentiated squamous epithelium overlying connective tissue. Although epithelium normally exhibits self-limited growth, cholesteatoma matrix erodes mucosa and bone suggesting changes in matrix protein constituents that permit destructive behaviour. Differential proteomic studies can measure and compare the cholesteatoma proteome to normal tissues, revealing protein alterations that may propagate the destructive process. METHODS: Human cholesteatoma matrix, cholesteatoma-involved ossicles, and normal middle ear mucosa, post-auricular skin, and non-involved ossicles were harvested. These tissues were subjected to multiplex peptide labeling followed by liquid chromatography and tandem mass spectrometry analysis. Relative protein abundances were compared and evaluated for ontologic function and putative involvement in cholesteatoma. RESULTS: Our methodology detected 10 764 peptides constituting 1662 unique proteins at 95 % confidence or greater. Twenty-nine candidate proteins were identified in soft tissue analysis, with 29 additional proteins showing altered abundances in bone samples. Ontologic functions and known relevance to cholesteatoma are discussed, with several candidates highlighted for their roles in epithelial integrity, evasion of apoptosis, and immunologic function. CONCLUSION: This study produced an extensive cholesteatoma proteome and identified 58 proteins with altered abundances contributing to disease pathopathysiology. As well, potential biomarkers of residual disease were highlighted. Further investigation into these proteins may provide useful options for novel therapeutics or monitoring disease status.
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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 | 0.000 | 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".