Effect of Acute and Chronic Moderate Hypoxia on Diltiazem Kinetics and Metabolism in the Dog
Bibliographic record
Abstract
The aim of this study was to assess whether moderate hypoxia affects the disposition of diltiazem. Six male beagle dogs received diltiazem (0.6 mg/kg) on three occasions: (1) while breathing air, i.e. during normoxia; (2) 1 h after initiating exposure to a FiO2 of 0.08, a FiCO2 of 0.035 and a FiN2 of 0.885, i.e. during acute hypoxia and normocapnia, and (3) during chronic hypoxia, i.e. after 120 h of exposure to a FiO2 of 0.08. Multiple blood samples were withdrawn and urine was collected to assay diltiazem and metabolites [N-desmethyl diltiazem (MA), deacetyl diltiazem (DAD) and N-desmethyl deacetyl diltiazem (M2)]. Breathing air, mean arterial partial pressure of oxygen was 83.2 +/- 3.2; during acute hypoxia 42.2 +/- 0.7; and during chronic hypoxia, 41.9 +/- 0.6 mm Hg. Acute hypoxia did not alter diltiazem disposition. Compared to dogs with normoxia, chronic hypoxia reduced diltiazem metabolic clearance, from 64 +/- 3 to 51 +/- 5 ml/min/kg (p < 0.05), as well as its volume of distribution, from 11.4 +/- 1.2 to 9.1 +/- 0.3 liters/kg (p < 0.05). Chronic hypoxia decreased the fraction of diltiazem metabolic clearance, normalized by the glomerular filtration rate, generating the M2 metabolite, although this experimental condition did not affect the formation of MA or DAD. It is concluded that chronic moderate hypoxia reduced diltiazem systemic clearance because it decreased selected pathways of biotransformation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".