Characterization of Human Tear Proteome Using Multiple Proteomic Analysis Techniques
Bibliographic record
Abstract
Tear proteome profiling may generate useful information for the understanding of the interaction between an eye and its contacting objects, such as a contact lens or a lens implant. This is important for designing improved eye-care devices and maintaining the health of an eye. Proteome profiles of tear fluids may also be used for disease diagnosis and prognosis. However, only a small volume of tear fluid (<5 microL) can be collected in a clinical laboratory under normal operational conditions, which makes proteome profiling a challenge. In this work we apply several proteomic analysis techniques, including gel-based and solution-based approaches with LC-ESI and LC-MALDI MS and MS/MS to gauge the relative merits of producing proteome profiles and to generate as broad a coverage of the tear proteome as possible from this small amount of sample. It is shown that a total of 54 proteins can be confidently identified using less than 5 microL of tear fluid. Of these, 44 proteins can be detected by LC-MALDI MS alone with a consumption of 2 microL of tear fluid. Furthermore, LC-MALDI can be used to determine post-translational modifications (PTMs), such as glycosylation and phosphorylation, without any sample enrichment or treatment. This work represents one of the most extensive proteome profiles (i.e., proteins identified and PTMs characterized) generated from tear fluids using clinically relevant amounts of sample.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".