Optophysiology: Depth-resolved probing of retinal physiology with functional ultrahigh-resolution optical coherence tomography
Bibliographic record
Abstract
Noncontact, depth-resolved, optical probing of retinal response to visual stimulation with a <10-microm spatial resolution, achieved by using functional ultrahigh-resolution optical coherence tomography (fUHROCT), is demonstrated in isolated rabbit retinas. The method takes advantage of the fact that physiological changes in dark-adapted retinas caused by light stimulation can result in local variation of the tissue reflectivity. fUHROCT scans were acquired from isolated retinas synchronously with electrical recordings before, during, and after light stimulation. Pronounced stimulus-related changes in the retinal reflectivity profile were observed in the inner/outer segments of the photoreceptor layer and the plexiform layers. Control experiments (e.g., dark adaptation vs. light stimulation), pharmacological inhibition of photoreceptor function, and synaptic transmission to the inner retina confirmed that the origin of the observed optical changes is the altered physiological state of the retina evoked by the light stimulus. We have demonstrated that fUHROCT allows for simultaneous, noninvasive probing of both retinal morphology and function, which could significantly improve the early diagnosis of various ophthalmic pathologies and could lead to better understanding of pathogenesis.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".