Lipidomic Profiling of Sinus Mucosa from Patients with Chronic Rhinosinusitis
Bibliographic record
Abstract
Sinusitis is a cause of significant morbidity, substantial healthcare costs, and negative effects on quality of life. The primary objective of this study is to characterize the previously unknown lipid profile of sinonasal mucosa from patients with chronic rhinosinusitis (CRS) and from controls. Sinus mucosa samples were analyzed from 9 CRS patients with concomitant nasal polyps, 11 CRS patients without polyps, and 12 controls. Ten lone polyp samples were also analyzed. Samples were subjected to a modified Bligh/Dyer lipid extraction, then high performance thin layer chromatography (HPTLC), combined gas chromatography/electron impact-mass spectrometry (GC/EI-MS), and flow-injection/electrospray ionization-tandem mass spectrometry (FI/ESI-MS/MS). Data was analyzed for identification and profiling of major components. HPTLC revealed an array of species reflecting the lipid complexity of the samples. GC/EI-MS revealed cholesterol and several fatty acids. FI/ESI-MSMS revealed numerous lipid species, namely a host of phosphatidylcholines, phosphatidylethanolamines, ceramides and cholesteryl esters, but no detectable amounts of phosphatidyinositols or sulfated lipids. These results are a first step to uncover unique molecular biomarkers in CRS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".