Multiple Immune Factors Are Involved in Controlling Acute and Chronic Chikungunya Virus Infection
Bibliographic record
Abstract
The recent epidemic of the arthritogenic alphavirus, chikungunya virus (CHIKV) has prompted a quest to understand the correlates of protection against virus and disease in order to inform development of new interventions. Herein we highlight the propensity of CHIKV infections to persist long term, both as persistent, steady-state, viraemias in multiple B cell deficient mouse strains, and as persistent RNA (including negative-strand RNA) in wild-type mice. The knockout mouse studies provided evidence for a role for T cells (but not NK cells) in viraemia suppression, and confirmed the role of T cells in arthritis promotion, with vaccine-induced T cells also shown to be arthritogenic in the absence of antibody responses. However, MHC class II-restricted T cells were not required for production of anti-viral IgG2c responses post CHIKV infection. The anti-viral cytokines, TNF and IFNγ, were persistently elevated in persistently infected B and T cell deficient mice, with adoptive transfer of anti-CHIKV antibodies unable to clear permanently the viraemia from these, or B cell deficient, mice. The NOD background increased viraemia and promoted arthritis, with B, T and NK deficient NOD mice showing high-levels of persistent viraemia and ultimately succumbing to encephalitic disease. In wild-type mice persistent CHIKV RNA and negative strand RNA (detected for up to 100 days post infection) was associated with persistence of cellular infiltrates, CHIKV antigen and stimulation of IFNα/β and T cell responses. These studies highlight that, secondary to antibodies, several factors are involved in virus control, and suggest that chronic arthritic disease is a consequence of persistent, replicating and transcriptionally active CHIKV RNA.
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) | 0.002 | 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".