Leukocyte-endothelial interactions within the ocular microcirculation in inflammation and infection
Bibliographic record
Abstract
Leukocyte-endothelial interactions within the microvasculature represent a hallmark of inflammation regardless of whether the inflammation results from non-infectious or infectious triggers. In this review, we highlight features of leukocyte recruitment in ocular disease and postulate mechanisms by which the infiltrating cells may lead to the progression of the ocular inflammatory response, including cytokine and chemokine production, T cell or non-T cell responses. Additionally, ex-vivo and in vivo methods used to study the general features of the immune response are discussed, with a specific focus on intravital imaging, which allows real-time non-invasive examination of leukocyte-endothelial interactions in the ocular microvasculature. At the present time there are still significant gaps in our understanding of the process of leukocyte recruitment in vivo in different microvascular beds. Further studies using non-invasive imaging approaches, such as intravital microscopy, provide an opportunity to study dynamic tissue-specific leukocyte-endothelial interactions in vivo and identify novel targets for early intervention in the inflammatory process. This knowledge is essential to the rational use of therapeutics to resolve inflammation in ocular disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".