Bibliographic record
Abstract
There are many documented examples of altered drug disposition in human conditions that stimulate host cytokine responses. These include viral, bacterial or parasitic infections, tissue injury, surgery, cancer and autoimmune conditions. Interferons, interleukins-1 and-6 and tumour necrosis factor are the central mediators. These cytokines have been traditionally viewed with respect to their ability to suppress hepatic cytochrome P450 (CYP)-mediated drug detoxification. Such aberrant drug handling has placed patients at risk for adverse drug responses to low therapeutic index, CYP-metabolized drugs like theophylline. It is now evident that drug-cytokine interactions are broader than once appreciated. They involve CYPs and drug transporter proteins like ABCB1 (P-glycoprotein) in the liver, intestine, kidney, blood-brain barrier, placenta and even immune cells. The consequences of drug-cytokine interactions are altered absorption, elimination and/or cellular and tissue distribution of drugs. The outcomes can be negative or positive depending on the drug, the anatomical site of the interaction and the therapeutic objectives. This chapter provides a historical overview of drug-cytokine interactions, discuss recent advances and examines the clinical scenarios in which infections or inflammation might lead to abnormal drug handling and drug responses. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.018 |
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".