Quantification of Conjunctival TNF‐α in Aqueous‐Deficient Dry Eye
Bibliographic record
Abstract
PURPOSE: This study aimed to quantify and compare conjunctival epithelial tumor necrosis factor (NF) α mRNA expression in Sjögren syndrome (SS), non-Sjögren syndrome aqueous-deficient dry eye (non-SS DE), and non-dry eye (NDE) control subjects. METHODS: A total of 76 subjects were recruited for this study: 25 SS (confirmed via American-European Consensus Criteria 2002), 25 non-SS DE (confirmed by symptoms and Schirmer scores ≤ 10 mm), and 26 NDE. Superior and temporal bulbar conjunctival epithelial cells were collected via impression cytology. Epithelial RNA was extracted, and TNF-α mRNA expression was quantified by real-time quantitative polymerase chain reaction. RESULTS: The expression of TNF-α mRNA was found to be significantly higher in the SS group (2.48 ± 1.79) compared to both non-SS DE (0.95 ± 1.18; p < 0.05) and NDE (0.84 ± 0.51; p < 0.05) groups. No difference in TNF-α mRNA expression was found between the non-SS DE and NDE groups (p = 0.67). CONCLUSIONS: These results demonstrate that SS-associated aqueous-deficient dry eye is associated with a significant upregulation of conjunctival epithelial TNF-α mRNA relative to both non-SS DE and control groups. The degree to which TNF-α mRNA is upregulated in SS may contribute to the severe ocular surface damage observed in these patients.
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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) | 0.000 | 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".