Quantitative proteomics of nasal mucus in chronic sinusitis with nasal polyposis.
Bibliographic record
Abstract
BACKGROUND: Proteomics has been used as a tool for identification of the protein content of nasal mucus in diseased and healthy subjects. Thirty-five proteins in both chronic rhinosinusitis (CRS) and control groups were identified in a previous study by our group using conventional mass spectrometry analysis. Ten of these proteins were related to innate and acquired immunity and showed differences in expression between the two groups. OBJECTIVE: To investigate the quantitative differential expression of specific nasal mucus proteins previously identified by our group using multiple reaction monitoring (MRM) mass spectrometry in patients with CRS with nasal polyposis compared with normal subjects. METHODS: In a prospective case control study, nasal mucus from patients and control subjects was collected, desalted, resolubilized, and digested using proteolytic enzymes. Previously identified nasal mucus proteins with differential expression in CRS patients were targeted and quantitatively measured using MRM mass spectrometry. RESULTS: Analysis of 12 samples (6 patients and 6 controls) identified 7 of the 10 targeted proteins, many of which were related to innate and acquired immunity. Quantitative analysis showed differential expression in CRS patients compared with control subjects. A detailed analysis and characterization of the protein isolates is outlined. CONCLUSION: This is the first proteomics study of nasal mucus in CRS with polyposis using the MRM technique. The findings suggest that innate and acquired immunity may play a role in the pathophysiology of CRS. Future steps in evaluating the protein characteristics of the mucus of CRS patients are aimed at developing biomarkers and potentially targeted therapies.
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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 | 0.001 | 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) | 0.000 | 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 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".