Assessment of Retinal and Choroidal Blood Flow Changes Using Laser Doppler Flowmetry in Rats
Bibliographic record
Abstract
PURPOSE: A new noninvasive laser Doppler flowmetry (LDF) probe (one emitting fiber surrounded by a ring of eight collecting fibers, 1-mm interaxis distance) was tested for its sensitivity to assess the retinal/choroidal blood flow variations in response to hypercapnia, hyperoxia, diverse vasoactive agents and following retinal arteries photocoagulation in the rat. MATERIALS AND METHODS: After pupil dilation, a LDF probe was placed in contact to the cornea of anesthetized rats in the optic axis. Hypercapnia and hyperoxia were induced by inhalation of CO(2) (8% in medical air) and O(2) (100%) while pharmacological agents were injected intravitreously. The relative contribution of the choroidal circulation to the LDF signal was estimated after retinal artery occlusion by photocoagulation. RESULTS: Blood flow was significantly increased by hypercapnia (18%), adenosine (14%) and sodium nitroprusside (16%) as compared to baseline values while it was decreased by hyperoxia (-8%) and endothelin-1 (-11%). Photocoagulation of retinal arteries significantly decreased blood flow level (-45%). CONCLUSIONS: Although choroidal circulation most likely contributes to the LDF signal in this setting, the results demonstrate that LDF represents a suitable in vivo noninvasive technique to monitor online relative reactivity of retinal perfusion to metabolic or pharmacological challenge. This technique could be used for repeatedly assessing blood flow reactivity in rodent models of ocular diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".