Effect of Hyperoxia and Hypercapnia on Tissue Oxygen and Perfusion Response in the Normal Liver and Kidney
Bibliographic record
Abstract
OBJECTIVE: Inhalation of air with altered levels of oxygen and carbon dioxide to manipulate tissue oxygenation and perfusion has both therapeutic and diagnostic value. These physiological responses can be measured non-invasively with magnetic resonance (MR) relaxation times. However, interpreting MR measurements is not straight-forward in extra-cranial organs where gas challenge studies have only begun to emerge. Inconsistent results have been reported on MR, likely because different organs respond differently. The objective of this study was to elucidate organ-specific physiological responses to gas challenge underlying MR measurements by investigating oxygenation and perfusion changes in the normal liver and kidney cortex. MATERIALS AND METHODS: Gas challenges (100% O(2), 10% CO(2), and carbogen [90% O(2)+10% CO(2)]) interleaved with room air was delivered to rabbits to investigate their effect on tissue oxygenation and perfusion. Real-time fiber-optic measurements of absolute oxygen and relative blood flow were made in the liver and kidney cortex. RESULTS: Only the liver demonstrated a vasodilatory response to CO(2). Perfusion changes to other gases were minimal in both organs. Tissue oxygenation measurements showed the liver responding only when CO(2) was present and the kidney only when O(2) was present. CONCLUSION: This study reveals distinct physiological response mechanisms to gas challenge in the liver and kidney. The detailed characterization of organ-specific responses is critical to improving our understanding and interpretation of MR measurements in various body organs, and will help broaden the application of MR for non-invasive studies of gas challenges.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".