Relationship between breathing gas mixtures and retinal vessel regulation
Bibliographic record
Abstract
Purpose It is known that components of oxygen transport pathway communicate with vascular endothelium. However, oxygen‐induced vessel constriction results in a decrease in retinal blood flow. Because the delivery and removal of metabolites and gases is proportional to blood flow, a decrease in blood flow could have deleterious effects. Due to the systemic nature of the endothelium, this should be taken into account wherever breathing gas mixture is changed (anesthesia). This work aims to clarify how the constriction of the retinal vessels is modulated by breathing gas mixtures using dynamic vessel analysis (DVA). Methods The baseline diameters of 15 volunteers (9 m, 26.3 ± 3.3 y) were measured using DVA (Imedos Systems GmbH). We investigated four primary vessel segments (superior as well as inferior temporal artery (STa/ITa) and vein (STv/ITv). For each volunteer, the DVA was performed 5 times (each: duration 15 min [1 min baseline, 8 min gas mixture, 6 min recovery], rest period: 10 min) in a randomized order using different gas mixtures (21%, 30%, 40%, 50%, 60% O 2 ) provided by a ventilator (Bellavista 1000, imtmedical AG). For statistical analysis, the t‐test and the Wilcoxon test for paired samples (Bonferroni corrected) were used. Results For all vessel segments, a stronger constriction could be observed with an increase in the oxygen concentration. The maximum constriction was always measured at the end of the 8 min period. Here, when comparing the constriction values of the 21% O 2 concentration, already the 30% O 2 concentration showed significant changes (30%/40%/50%/60% STa: p = 0.002/0.001/0.000/0.000, ITa: p = 0.001/0.001/0.001/0.000, STv: p = 0.000/0.000/0.000/0.000, ITv: p = 0.001/0.000/0.000/0.000). Conclusions The presented work revealed that even a small change in breathing gas mixture causes a significant change in vessel constriction. These effect is not only to be expected at the retina, but also in the brain as an example. This should be taken into account, amongst others, for longer anesthesia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".