The Differential Reactive Oxygen Species Production of Tear Neutrophils in Response to Various Stimuli In Vitro
Bibliographic record
Abstract
A large number of polymorphonuclear neutrophils (PMNs) invade the ocular surface during prolonged eye closure (sleep); these leukocytes are commonly referred as tear PMNs. PMNs contribute to homeostasis and possess an arsenal of inflammatory mediators to protect against pathogens and foreign materials. This study examined the ability of tear PMNs to generate reactive oxygen species (ROS), an essential killing mechanism for PMNs which can lead to oxidative stress and imbalance. Cells were collected after sleep from healthy participants using a gentle eye wash. ROS production in stimulated (phorbol-12-myristate-13-acetate (PMA), lipopolysaccharides (LPS) or N-Formylmethionyl-leucyl-phenylalanine (fMLP)) and unstimulated tear PMNs was measured using luminol-enhanced chemiluminescence for 60 min. A high level of constitutive/spontaneous ROS production was observed in tear PMNs in the absence of any stimulus. While tear PMNs were able to produce ROS in response to PMA, they failed to appropriately respond to LPS and fMLP, although fMLP-stimulated tear PMNs generated ROS extracellularly in the first three minutes. Higher ROS generation was observed in isolated tear PMNs which may be due to priming from the magnetic bead cell separation system. The differential responses of tear PMNs in ROS generation provide further evidence of their potential inflammatory roles in ocular complications involving oxidative stress.
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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".