Immunity Versus Immunopathology in West Nile Virus Induced Encephalitis
Bibliographic record
Abstract
Flavivirus Encephalitis 54 2001).Following the initial subcutaneous or intraperitoneal infection in mice, WNV induces a systemic infection, invades the central nervous system (CNS) and causes death rapidly when encephalitis develops, usually within 1-2 weeks (Beasley et al., 2002;Kramer & Bernard, 2001; Wang et al., 2001b).The severity and symptoms of lethal infection observed in mice mimic the symptoms caused by WNV infection in humans.The murine model has been an effective in vivo experimental model to investigate viral pathogenesis and the host immunity in humans.Based on information obtained from studies on animal models, cell culture and patient samples, this review will be focused on discussion of the role of several important immune factors, including pathogen recognition receptor (PRR) -mediated signaling pathways, cytokines, monocytes/microglia, γδ T cells, CD4 + and CD8 + αβ T cells in protection and pathogenesis of WNV-induced encephalitis. Innate Immunity to WNV infection PRR signaling pathwaysThe key players of the innate immune surveillance are the sensor molecules known as PRRs which recognize specific pathogen associated molecular patterns (PAMPs) and trigger the signaling cascade ultimately leading to the production of type 1 interferon (IFN)s and proinflammatory cytokines.Three classes of PRRs have been implicated for viral PAMPs: tolllike receptors (TLRs), retinoid acid-inducible gene-I (RIG-I) -like receptors (RLRs), and nucleotide oligomerization domain (NOD) -like receptors (NLRs) (Iwasaki & Medzhitov, 2010;Wilkins & Gale, 2010).Of these, several TLRs and RLRs are involved in WNV recognition. TLRsTLRs, a family of thirteen mammalian homologues of Drosophila Toll that recognize PAMPs, play an essential role in the initiation of innate immunity (Qureshi & Medzhitov, 2003).Most TLR signaling pathways (except TLR3) utilize myeloid differentiation factor 88 (MyD88) as the primary adaptor (Akira & Hemmi, 2003).TLR stimulation culminates in the synthesis of antiviral cytokines, such as type 1 IFN and proinflammatory cytokines, which may directly suppress viral replication.WNV is a positive ssRNA virus that produces dsRNA in its life cycle (Samuel, 2002).TLR3 recognizes dsRNA and is expressed in dendritic cells (DCs) and several CNS cell types, including neurons, astrocytes, and microglia (Daffis et al., 2008;Town et al., 2006; Wang et al., 2004).TLRs 7 and 8 are implicated in MyD88dependent recognition of ssRNA and ssRNA-producing viruses.Depending on the virus dose (lethal versus sub-lethal), passage history of the virus (Vero cell-derived versus insect cell-derived) or routes of inoculation, TLRs 3 and 7 are known to play important roles in host immunity to WNV infection, either pathogenic or protective.Following a sub-lethal dose of insect cell derived WNV infection either intraperitoneally or subcutaneously, TLR3 provides a protective effect against WNV infection, partially by restricting replication in neurons (Daffis et al., 2008).WNV NS1 protein plays a role in viral pathogenesis by counteracting TLR3 signaling in in vitro cell culture (Wilson et al., 2008).During an intraperitoneal infection of WNV, TLR7-mediated signaling promoted IL-12/IL-23dependent immune cell homing to infected target cells, thereby contributing to a vital host defense mechanism (Town et al., 2009).MyD88-mediated signaling was reported to restrict www.intechopen.com
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".