Tear Lipocalin and Lysozyme in Sjögren and Non‐Sjogren Dry Eye
Bibliographic record
Abstract
PURPOSE: To evaluate the concentration of tear lipocalin, lysozyme, and total protein in Sjögren's Syndrome (SS), non-Sjögren's keratoconjunctivitis sicca (KCS), and non-dry-eyed (NDE) individuals. METHODS: Seventy-six subjects were recruited for this study: 25 SS subjects; 25 KCS subjects, and 26 NDE individuals. Symptoms were measured with a visual analogue scale. Tear flow was measured by the Schirmer I test without anesthesia. Tears were collected using an eye wash technique. Total tear protein was quantified using the DC Protein Assay Kit. Tear lipocalin and lysozyme were quantified via Western blotting performed on a Phast System. RESULTS: By definition, the SS and KCS groups both had significantly lower mean Schirmer scores (5.12 +/- 5.96 mm and 7.84 +/- 7.35 mm) compared with the NDE group (23.83 +/- 7.85 mm; p < 0.0001). There was no difference in mean Schirmer scores between SS and KCS groups (p = 0.19). The tear film of the SS group was characterized by significantly reduced (p < 0.0001) total protein and lipocalin concentrations compared with both KCS and NDE groups. No difference between the KCS and NDE groups was found in total protein (p = 0.92) or lipocalin (p = 0.19) concentration. In contrast, the concentration of tear film lysozyme was found to be statistically similar in all three groups examined. No statistically significant correlation was found in any group between mean Schirmer values compared with total protein, lipocalin or lysozyme concentration. CONCLUSION: Our data demonstrate a biochemical distinction between the Sjögren's group compared with both KCS and control groups, in that both tear lipocalin and total tear protein were significantly reduced. Although correlations were not found between protein measurements and tear flow, a combination of tests including Schirmer I and quantitation of tear film biomarkers may allow for the identification of SS patients without the need for invasive testing.
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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 | 0.001 |
| 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 | 0.000 | 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".