Effect of Inflammation on Molecular Targets and Drug Transporters
Bibliographic record
Abstract
Inflammation, the host's response to infection and injury, is associated with altered expression of genes such as metabolizing enzymes, transporters, receptors and plasma proteins. The purpose of the present work was to characterize the effect of inflammation on selected molecular targets and transporters that affect drugs' action and disposition. We have used rats with adjuvant arthritis (AA), an animal model of chronic inflammation. The AA group received 0.2 ml of 50 mg ml-1 Mycobacterium butyricum suspended in squalene into the tail base. On day 12, the rats were euthanized and their organs (heart, liver, kidneys and intestine) excised. Expression of Cav1.2, β1-AR, β2-AR, α1A-AR, Nav1.2, Nav1.6, Kv1.5, Kv2.1, Kv3.1, oatp1a1, oatp1a5, oatp1b2, oatp2b1, oatp4a1, oat2, oat3, oct1, mdr1a, bsep, mrp1, mrp3, mrp6, IL-1α, IFN-γ, iNOS, MCP-1, IL-10, Cox-1 and Cox-2 were determined by real time polymerase chain reaction (RT-PCR). Inflammation resulted in a significant reduction of oct1, oatp4a1 and mrp1 gene expression in the liver and oatp2b1, mrp6 and bsep gene expression in the kidney. Oatp4a1 and mdr1a were found to be significantly upregulated in rat heart. In conclusion, inflammation alters the gene expression of some mediators and drug transporters that can influence the behavior of drugs in the body and contribute to therapeutic failure.
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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 | 0.001 |
| 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".