Acetaminophen Modulates the Transcriptional Response to Recombinant Interferon-β
Bibliographic record
Abstract
BACKGROUND: Recombinant interferon treatment can result in several common side effects including fever and injection-site pain. Patients are often advised to use acetaminophen or other over-the-counter pain medications as needed. Little is known regarding the transcriptional changes induced by such co-administration. METHODOLOGY/PRINCIPAL FINDINGS: We tested whether the administration of acetaminophen causes a change in the response normally induced by interferon-beta treatment. CD-1 mice were administered acetaminophen (APAP), interferon-beta (IFN-beta) or a combination of IFN-beta+APAP and liver and serum samples were collected for analysis. Differential gene expression was determined using an Agilent 22 k whole mouse genome microarray. Data were analyzed by several methods including Gene Ontology term clustering and Gene Set Enrichment Analysis. We observed a significant change in the transcription profile of hepatic cells when APAP was co-administered with IFN-beta. These transcriptional changes included a marked up-regulation of genes involved in signal transduction and cell differentiation and down-regulation of genes involved in cellular metabolism, trafficking and the IkappaBK/NF-kappaB cascade. Additionally, we observed a large decrease in the expression of several IFN-induced genes including Ifit-3, Isg-15, Oasl1, Zbp1 and predicted gene EG634650 at both early and late time points. CONCLUSIONS/SIGNIFICANCE: A significant change in the transcriptional response was observed following co-administration of IFN-beta+APAP relative to IFN-beta treatment alone. These results suggest that administration of acetaminophen has the potential to modify the efficacy of IFN-beta treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".