Quantification of MUCIN 1, cell surface associated and MUCIN16, cell surface associated proteins in tears and conjunctival epithelial cells collected from postmenopausal women.
Bibliographic record
Abstract
PURPOSE: To quantify the expression of mucin 1, cell surface associated (MUC1) and mucin 16, cell surface associated (MUC16) proteins and messenger ribonucleic acid (mRNA) in a cohort of postmenopausal women (PMW), to explore the relationship between mucin expression, dry eye symptomology, and tear stability. METHODS: Thirty-nine healthy PMW (>50 years of age) were enrolled in this study. No specific inclusion criteria were used to define dry eye; instead, a range of subjects were recruited based on responses to the Allergan Ocular Surface Disease Index (OSDI) questionnaire and tear stability measurements as assessed by non-invasive tear breakup time (NITBUT). Tears were collected from the inferior tear meniscus using a disposable glass capillary tube, and total RNA and total protein were isolated from conjunctival epithelial cells collected via impression cytology. Expression of membrane-bound and soluble MUC1 and MUC16 were quantified with western blotting, and expression of MUC1 and MUC16 mRNA was assessed with real-time PCR. RESULTS: OSDI responses ranged from 0 to 60, and NITBUT ranged from 18.5 to 2.9 s. Only two statistically significant correlations were found: soluble MUC16 protein concentration and MUC16 mRNA expression with OSDI vision related (-0.47; p=0.01) and ocular symptom (0.39; p=0.02) subscores, respectively. Post hoc exploratory analysis on absolute expression values was performed on two subsets of subjects defined as asymptomatic (OSDI≤6, n=12) and moderate to severe symptomatic (OSDI≥20, n=12). The only significant difference between the two subgroups was a significant reduction in MUC16 mRNA expression found in the symptomatic dry eye group (1.52±1.19 versus 0.57±0.44; p=0.03). CONCLUSIONS: A broad exploration of mucin expression compared to either a sign (NITBUT) or symptoms of dry eye failed to reveal compelling evidence supporting a significant relationship, other than a potential association between MUC16 with specific symptoms. Furthermore, comparison of mucin protein and expression levels between the asymptomatic and moderate to severe symptomatic subgroups revealed only one significant difference, a reduction in MUC16 mRNA expression in the symptomatic subgroup.
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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".