Investigation of the rhythmic recruitment of tear neutrophils to the ocular surface and their phenotypes
Bibliographic record
Abstract
Hundreds of thousands of polymorphonuclear neutrophils (PMNs) are collected from the ocular surface upon waking, while few are harvested during daytime. This study aimed to investigate potential factors contributing to the circadian infiltration of tear PMNs, including changes in IL-8 and C5a in tears, and their phenotypes across different time points in a 24-h cycle. Tear PMNs were collected using a gentle eyewash after 2-h and 7-h of sleep (eye closure, EC) at night, after 2-h EC during the day, and towards the end of the afternoon. Significantly fewer cells were collected after 2-h EC during the day compared to 2-h EC at night. A positive correlation between IL-8 and PMN numbers existed, but not with C5a. Tear PMNs collected after 2-h EC at night were less degranulated and possessed a larger activation potential compared to 7-h EC. Tear PMNs from 7-h EC at night exhibited hyper-segmented nuclei and more NETosis compared to 2 h EC night, indicating an aged and activated phenotype. The diurnal-nocturnal recruitment pattern of tear PMNs may be driven by increased IL-8 in nighttime tears. Higher degranulation and NETs point to the significant activation of tear PMNs on the ocular surface during prolonged eye closure at night.
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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 | 0.000 | 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".