Rise and shine: Open your eyes to produce anti-inflammatory NETs
Bibliographic record
Abstract
The eye is a unique organ, traditionally considered to be immune privileged. Now we are seeing that there is a delicate balance in ocular immunity. This immunity allows the surface of the eye to remain clear of pathogens and healthy despite being continuously exposed to the external environment. As we learn more about various eye disorders including macular degeneration and dry eye disorder, we are finding there is a large immunological component to these diseases.1,2 Previously, it was thought that the major player in protection of the eye was the antibacterial activity of tear film; however, as we learn more about the immunology of the eye, we are finding resident and recruited immune cells that are able to help in ocular health. What is unique to this organ is the open eye versus the closed eye environment as it differs vastly. During the hours of the day we are awake, the eye is in direct contact with the external environment, perpetually fighting off pathogens using proteins such as secretory IgA and lactoferrin found in the tear film. However, when our eyes are closed during sleep, how we maintain homeostasis and clear pathogens and debris is not well understood. Prolonged eye closure can lead to an environment that is hypoxic, which causes edema and other problems. Mahajan et al.12 expand our knowledge of the ocular environment, shining light onto how the closed eye of a healthy individual maintains a milieu that does not cause inflammation and tissue damage. There is in fact a coordinated sequence of events that keeps our eyes from becoming inflamed; Mahajan and colleagues for the first time systematically describe these events.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".