Bibliographic record
Abstract
The expression of cytochrome P450 and P-glycoprotein is altered during the operation of host defense mechanisms. The basis for this interaction is predominantly through cytokine-mediated pathways. Most of the major cytokines, including interleukin (IL)-1α, IL-1β, IL-2β, IL-6, tumor necrosis factor-α, inteferon-α, inteferon-γ, and transforming growth factor-β, are known to downregulate the major forms of cytochrome P450 and P-glycoprotein. In most cases individual cytochrome P450 forms are downregulated at the level of gene transcription, with a resulting decrease in the corresponding messenger ribonucleic acid, protein, and enzyme activity. The cytokine-mediated loss in drug metabolism is channeled predominantly through the modification of specific transcription factors. Similar pathways appear to alter the expression of P-glycoprotein. In clinical medicine, there are numerous examples of a decreased capacity to handle drugs during infections and disease states that involve an inflammatory component and the production of cytokines. The direct administration of cytokines to humans depresses the levels of several cytochrome P450-mediated pathways. The production of cytokines in humans often results in altered drug responses and increased toxicities, which has major implications in inflammation and infection when the capacity of the liver and other organs to handle drugs is severely compromised. Changes in drug-handling capacity during inflammation/infection will continue to be one of the many factors that complicate therapeutics.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".