A comparison of patient matched meibum and tear lipidomes
Bibliographic record
Abstract
Purpose - To quantify the molecular lipid composition of patient-matched tear and meibum samples and compare tear and meibum lipid molecular profiles. Methods - Lipids were extracted from tears and meibum by bi-phasic methods using 10:3 tertbutyl methyl ether:methanol, washed with aqueous ammonium acetate, and analyzed by chipbased nanoelectrospray ionization tandem mass spectrometry. Targeted precursor ion and neutral loss scans identified individual molecular lipids and quantification was obtained by comparison to internal standards in each lipid class. Results - Two hundred and thirty-six lipid species were identified and quantified from nine lipid classes comprised of cholesterol esters, wax esters, (O-acyl)-x-hydroxy fatty acids, triacylglycerols, phosphatidylcholine, lysophosphatidylcholine, phosphatidylethanolamine, sphingomyelin, and phosphatidylserine. With the exception of phospholipids, lipid molecular profiles were strikingly similar between tears and meibum. Conclusions - Comparisons between tears and meibum indicate that meibum is likely to supply the majority of lipids in the tear film lipid layer. However, the observed higher mole ratio of phospholipid in tears shows that analysis of meibum alone does not provide a complete understanding of the tear film lipid composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".