Immune mechanisms of intraocular inflammation
Bibliographic record
Abstract
Understanding immune mechanisms of noninfectious intraocular inflammation via animal models, and the relatively more restricted insights that can be achieved through studies in humans, continues to generate successful immunotherapies. This translational conduit elaborates immunopathogenic mechanisms, and illuminates further prospects of tailored therapies and biomarkers of disease activity and prognosis. More recently, our increased understanding has moved on from the success of previous biologic therapies, such as anti-TNF and IFN-α treatments, revealing other possible avenues to target; for example, Th17 cells, immune cell migration and the use of T-regulatory and dendritic cells to induce immunological tolerance. We now recognize that the ocular environment is endowed with many regulatory mechanisms but is hardly privileged in as much as ocular inflammation remains prevalent. Nevertheless, future therapeutic and diagnostic developments will harness our understanding of the local immunoregulatory networks to not only restrain immune-mediated damage, but also to restore homeostasis and neuronal function. This article attempts to crystallize our understanding of local immune regulation and immune mechanisms leading to intraocular inflammation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".