Bibliographic record
Abstract
The effects of viral immune evasion proteins can occur outside host cells, in the case of chemokines, cytokines, or cell surface receptors, or inside cells, in the case of signal transduction and antigen presentation pathways. This chapter attempts to summarize examples of viral immune evasion strategies, especially the facets of the immune system frequently targeted by viruses. Human cytomegalovirus (HCMV), human retroviruses, and vaccinia virus incorporate host complement control proteins (CCPs), CD55 and CD59, into the virion envelopes, mediating resistance to complement. The poxviruses variola virus, vaccinia virus (VV), and cowpox virus (CPV) all express CCPs, which were discovered based on sequence similarity to human and mouse CCPs. The ability of viruses to induce IFN gene expression via dsRNA varies greatly, and the viral proteins which have evolved to intercept this dsRNA-dependent activation can function either to block IFN-induced transcription or by neutralizing IFN-induced molecules that establish an antiviral state. The discovery of virus-encoded homologs of IFN regulatory factors (vIRFs) within the genome of human herpesvirus 8 (HHV-8) suggested a mechanism whereby the viral homolog could outcompete cellular IRFs needed for the transcriptional activation of host cell IFN response genes. Inactivation occurs only after direct contact between herpes simplex virus (HSV)-infected fibroblasts and the lymphocytes, not when the lymphocytes are incubated with high concentrations of cell-free virus.
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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".