Ly6C<sup>high</sup> monocytes facilitate transport of Murid herpesvirus 68 into inflamed joints of arthritic mice
Bibliographic record
Abstract
Abstract Viruses, particularly the Epstein‐Barr virus (EBV) has long been suspected to exacerbate acute arthritic symptoms. However, the cell populations that contribute to import viruses into the inflamed tissues remain to be identified. In the present study, we have investigated the role of monocytes in the transport of Murid herpesvirus 68 (MHV‐68), a mouse virus closely related to EBV, using the serum transfer‐induced arthritis (STIA) model. We found compelling evidence that MHV‐68 infection markedly increased disease severity in NR4A1−/− mice, which are deficient for Ly6Clow monocytes. In contrast, the MHV‐68‐induced enhancement of joint inflammation was lessened in CCR2−/− mice, suggesting the involvement of inflammatory Ly6Chigh monocytes in viral transport. We also observed that following selective depletion of monocyte subsets by administration of clodronate, MHV‐68 transport into the synovium occurs only in the presence of Ly6Chigh monocytes. Tracking of adoptively transferred Ly6Chigh GFP infected monocytes into arthritic CCR2−/− mice by two‐photon intravital microscopy showed that this monocyte subset has the capacity to deliver viruses to inflamed AR joints, as confirmed by the detection of viral DNA in inflamed tissues of recipient mice. We thus conclude that Ly6Chigh monocytes import MHV‐68 when they are mobilized to the inflamed arthritic joint.
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) | 0.001 | 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 | 0.001 |
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; 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".