Lipidomic Profiling of Mastoid Bone and Tissue from Patients with Chronic Otomastoiditis
Bibliographic record
Abstract
Introduction Chronic otomastoiditis causes pain, otorrhea, and hearing loss resulting from the growth of tissue within the normally hollow mastoid cavity. Objectives In this report, we used a lipidomics approach to profile major mastoid bone and tissue lipids from patients with and without otomastoiditis. Methods The bone dust created during mastoidectomy, as well as the mastoid tissue, was analyzed from seven patients. Bone dust was also collected and analyzed in an additional four otologic cases (parotidectomy requiring mastoidectomy). 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-MSMS). Data were analyzed for identification and profiling of major lipid components. Results HPTLC revealed the presence of various lipid classes, including phosphatidylcholines, cholesterol, and triacylglycerols. GC/EI-MS analysis revealed the presence of cholesterol and several fatty acids. FI/ESI-MSMS analysis revealed a host of phosphatidylcholines, phosphatidylethanolamines, and cholesteryl esters. Conclusion We used a lipidomics approach to develop an efficient (both in time and tissue amount) methodology for analysis of these tissues, identify the most abundant and common lipid species, and create a base of knowledge from which more focused endeavors in biomarker discovery can emerge. In an effort toward improved patient categorization and individualized intervention, the ultimate goal of this work is to correlate these lipid molecules to disease state and progression. This is the first reported study of its kind on these tissues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".