JAK inhibitors remove innate immune barriers facilitating viral propagation
Bibliographic record
Abstract
Janus kinase (JAK) inhibitors are small-molecule therapeutics that reduce inflammation in autoimmune and inflammatory diseases by modulating the JAK-STAT pathway. While effective in alleviating immune-mediated conditions, JAK inhibitors can impair antiviral defences by suppressing interferon (IFN) responses, potentially increasing susceptibility to viral infections. This study investigates the pro-viral mechanism of JAK inhibitors, focusing on baricitinib, across various cell lines, organoids, and viral strains, including a recombinant Rift Valley fever virus, influenza A virus, SARS-CoV-2, and wild-type adenovirus. Our findings demonstrate that baricitinib suppresses transcription of IFN-stimulated genes in non-infected cells, which is triggered by type I IFNs produced by infected cells, facilitating viral propagation. The pro-viral effect was influenced by viral load, inhibitor concentration, and structural characteristics of the compound. These results underscore the dual effects of JAK inhibitors: reducing inflammation while potentially exacerbating viral infections. Additionally, the findings highlight opportunities to leverage JAK inhibitors for viral research, vaccine production, and drug screening.
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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 | 0.001 | 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".