A framework for modeling ocular drug transport and flow through the eye using micro-CT
Bibliographic record
Abstract
This study uses micro-computed tomography (micro-CT) imaging for assessment of concentration and transport mechanisms of ocular drug surrogates following intravitreal injection. Injections of an iodinated contrast agent were administered to enucleated porcine eyes prior to scanning over 192 min. Image analysis was performed using signal profiles and regions of interest that corresponded to specific iodine concentrations. Diffusion coefficients of the injected iodine solutions were calculated using nonlinear regression analysis with a diffusion model. There was a predominantly diffusive component in the movement of the contrast to the back of the eye in the horizontal (sagittal & coronal) directions, with ultimate retinal fate observed after 120 min. The diffusion coefficients were found to have a mean of 4.87 × 10(-4) mm(2) s(-1) and standard deviation of 8.39 × 10(-5) mm(2) s(-1) for 150 mg ml(-1) iodine concentration and 6.13 × 10(-4) ± 1.83 × 10(-4) mm(2) s(-1) for 37.5 mg ml(-1) concentration. However, it should be noted that these coefficients were time dependent and were found to decay as the diffusion front interacted with the retinal wall. A real-time, accurate, non-invasive method of tracking a bolus and its concentration is achieved using a high spatial resolution and fast scanning speed micro-CT system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".