FUNCTIONAL ACTIVITY IN HUMAN HEART EXPRESSING CYP1A1
Bibliographic record
Abstract
Background Drug metabolism by CYP450s expressed in cardiac myocytes can modulate cardiac drug levels and actions. CYP1A1 and 2C8 are importantly expressed in the human heart. A cocktail approach with 7‐Ethoxyresorufin (7‐ET) and repaglinide (REP) were chosen to study selective metabolism of CYP1A1 and 2C8, respectively. Methods Incubations were first performed in Supersome™ of each CYP450s founded in the heart. Then, cytosolic (S1) and human heart microsomes (HHM) were isolated by differential centrifugations from one human heart. Resorufin and REP metabolite were quantified by LC‐MS/MS. Western blot analysis was performed using a monoclonal antibody raised against CYP1A1. Results REP metabolism was selective towards 2C8 and 7‐ET for 1A1 in Supersome™. For 7‐ET, Km was 0.36 vs. 0.4μM and Vmax was 1640 vs. 1944 pmoles of resorufin/mg of protein/min when incubated alone vs. with REP, respectively. For REP, Km was 3.7 vs. 3.35μM and Vmax was 1283 vs. 1120 pmoles REP metabolite/mg of protein/min when incubated alone vs. with 7‐ET, respectively. No activity was observed when 7‐ET and REP were incubated in HHM but there was a low activity in the S1 fraction (0.3 pmoles of resorufin/mg prot/min). This was confirmed by Western blot analysis where CYP1A1 protein was found in S1 but not in HHM. Conclusion This cocktail approach allows us to study the CYP450 activities in the heart. Research funded by the CIHR, HSF and FRSQ.
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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) | 0.003 | 0.001 |
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".