Inhibition of human cytomegalovirus replication by interferon alpha can involve multiple anti-viral factors
Bibliographic record
Abstract
The shortcomings of current direct-acting anti-viral therapy against human cytomegalovirus (HCMV) has led to interest in host-directed therapy. Here we re-examine the use of interferon proteins to inhibit HCMV replication utilizing both high and low passage strains of HCMV. Pre-treatment of cells with interferon alpha (IFNα) was required for robust and prolonged inhibition of both low and high passage HCMV strains, with no obvious toxicity, and was associated with an increased anti-viral state in HCMV-infected cells. Pre-treatment of cells with IFNα led to poor expression of HCMV immediate-early proteins from both high and low passage strains, which was associated with the presence of the anti-viral factor SUMO-PML. Inhibition of HCMV replication in the presence of IFNα involving ZAP proteins was HCMV strain-dependent, wherein a high passage HCMV strain was obviously restricted by ZAP and a low passage strain was not. This suggested that strain-specific combinations of anti-viral factors were involved in inhibition of HCMV replication in the presence of IFNα. Overall, this work further supports the development of strategies involving IFNα that may be useful to inhibit HCMV replication and highlights the complexity of the anti-viral response to HCMV in the presence of IFNα.
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) | 0.000 | 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) | 0.001 | 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".