Tear Protein Biomarkers for Ocular Mucous Membrane Pemphigoid Uncovered Using Targeted LC-MS/MS
Bibliographic record
Abstract
Mucous membrane pemphigoid (MMP) is a multisystemic rare autoimmune disease affecting the skin and mucous membranes. Ocular involvement is characterized by chronic conjunctival inflammation causing scar formation, leading to corneal opacification and vision loss. Conjunctival biopsies are currently used to confirm diagnosis, and the associated immunosuppression treatments prescribed can have serious consequences on patients. To address these challenges, a noninvasive approach to collect patient tears using untargeted and targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses has been developed to identify a list of potential biomarkers of ocular MMP. Samples were collected on Schirmer strips and subjected to tryptic digestion and LC-MS/MS analysis. Three cohorts of patients were studied, and targeted LC-scheduled multiple reaction monitoring (LC-sMRM) methods were developed for the verification of putative biomarkers. The comparison of three groups of patients, those diagnosed with ocular MMP, non-ocular MMP, and lichen planus, another ocular cicatricial conjunctivitis disorder, yielded 56 biomarkers of interest. Proteins distinguishing MMP patients with and without ocular involvement were linked to the extracellular matrix, metabolism, and neutrophil degranulation. The comparison between MMP and lichen planus patients highlighted lower levels of metabolic enzymes in the latter. This study highlights the use of multiple patient cohorts and tailored targeted quantitative proteomics methods for the discovery of biomarkers for diagnostic and prognostic purposes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".