Proteomics of Nasal Mucus in Chronic Rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is among the three most common chronic diseases in North America. The area of proteomics research is providing tremendous insight into the mechanisms of a variety of physiological processes and disease states. The purpose of this study was to evaluate qualitative and quantitative differences in protein content of nasal mucus in patients with chronic hypertrophic sinusitis with nasal polyposis when compared with control subjects. METHODS: A case-control study was performed in a tertiary academic center. Nasal mucus was collected from four patients with CRS and nasal polyposis as well as four control subjects. The protein content was digested using proteolytic enzymes, labeled with iTRAQ reagents, and subjected to mass spectrometry (MS) analysis. RESULTS: A total of 35 proteins were identified, many of which were related to innate and acquired immunity. Lysozyme C precursor was found to be down-regulated by a ratio (R) of 0.65 (p = 0.016) in CRS patients, as was Clara cell phospholipid-binding protein (R = 0.23; p = 0.0018), and antileukoproteinase 1 (R = 0.47; p < 0.0001). A detailed analysis and characterization of the protein isolates is outlined. CONCLUSION: The field of proteomics has great potential in leading to a better understanding of the mechanism of the disease process in CRS. Differences in the expression of proteins related to regulation of immune cells and mediators merit additional investigation.
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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".