Neutrophil and T-Cell Homeostasis in the Closed Eye
Bibliographic record
Abstract
Purpose: This study sought to examine the changes and phenotype of the tear neutrophil and T-cell populations between early eyelid closure and after a full night of sleep. Methods: Fourteen healthy participants were recruited and trained to wash the ocular surface with PBS for at-home self-collection of ocular surface and tear leukocytes following up to 1 hour of sleep and a full night of sleep (average 7 hours), on separate days. Cells were isolated, counted, and incubated with fluorescently labeled antibodies to identify neutrophils, monocytes, and T cells. For neutrophil analysis, samples were stimulated with lipopolysaccharide (LPS) or calcium ionophore (CaI) before antibody incubation. Flow cytometry was performed. Results: Following up to 1 hour of sleep, numerous leukocytes were collected (2.6 × 105 ± 3.0 × 105 cells), although significantly (P < 0.005) more accumulated with 7 hours of sleep (9.9 × 105 ± 1.2× 106 cells). Neutrophils (65%), T cells (3%), and monocytes (1%) were identified as part of the closed eye leukocyte infiltration following 7 hours of sleep. Th17 cells represented 22% of the total CD4+ population at the 7-hour time point. Neutrophil phenotype changed with increasing sleep, with a downregulation of membrane receptors CD16, CD11b, CD14, and CD15, indicating a loss in the phagocytic capability of neutrophils. Conclusions: Neutrophils begin accumulating in the closed eye conjunctival sac much earlier than previously demonstrated. The closed eye tears are also populated with T cells, including a subset of Th17 cells. The closed eye environment is more inflammatory than previously thought and is relevant to understanding ocular homeostasis.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".