The Critical Role of Type I Interferons in Murine Hepatitis Virus Proliferation and Liver Pathology 9168
Bibliographic record
Abstract
Abstract Description Type I Interferons (IFN-I) are crucial in restricting the proliferation and pathology of the murine hepatitis virus (MHV). Macrophages in the liver highly express the MHV receptor, CEACAM-1, thus we were interested in understanding how macrophages mediate the antiviral effects of IFN-I during MHV infection. So, wildtype (WT) and IFNAR-/- C57BL/6 mice were infected with MHV-A58 via i.p., intranasal, and oral routes. Compared to WT mice, IFNAR-/- mice developed an acute infection and hepatitis within 3 days following i.p. injection of MHV-A58, while they were less susceptible to intranasal and oral routes of administration. The susceptibility was dependent on peritoneal macrophages, as their depletion in IFNAR-/- mice completely prevented acute liver pathology and significantly reduced viral load in both the serum and liver. Peritoneal macrophages in IFNAR-/-mice were depleted using Clodronate liposomes, followed by intraperitoneal administration of MHV. Cytokine array analysis revealed reduced inflammatory response cytokine profiles in both the serum and liver of macrophage-depleted IFNAR-/- mice, similar to those observed in WT mice. In vitro apoptosis studies using poly I:C stimulation showed that macrophages from IFNAR-/- mice exhibit greater resistance to apoptosis and viral replication. These findings clearly show Type I IFN signalling is crucial in preventing liver pathology against MHV through controlling MHV replication and regulating macrophage apoptosis. Funding Sources CIHR Topic Categories Viral Immunology (VIR)
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".