Viral Infections of the Central Nervous System: Pathogenic and Protective Effects of Neuroinflammation
Bibliographic record
Abstract
Inflammation is a cardinal feature of viral infections of the central nervous system (CNS) and is defined by activation of both innate and adaptive immune processes that not only exert antiviral actions, but also mediate pathogenic off-target effects. Indeed, systemic inflammation often exacerbates local inflammation within the CNS during viral infections, in part by enhancing the trafficking of virus-infected (and -activated) cells across the blood–brain barrier. Human immunodeficiency virus (HIV) type CNS entry and infection of microglia and macrophages occurs soon after primary infection and establishes a long-lasting viral reservoir. Innate immune activation during HIV-1 infection is characterized by type 1 interferon-related gene expression as well as proinflammatory cytokines, proteases, and reactive oxygen species (ROS) that contribute to synaptic injury and eventual neuronal death. This chronic innate immune activation results in a low-grade encephalitis and the eventual development of the clinical spectrum disorder, HIV-associated neurological disorder (HAND), years after primary infection. In contrast, West Nile virus (WNV) causes a rapid onset of encephalitis, meningitis, and myelitis, which is characterized by infection of neurons with associated lymphocyte and macrophage CNS infiltration that underlie acute innate and adaptive immune responses leading to tissue damage. Herein, we compare inflammatory aspects of these viral infections of the CNS, highlighting the associated pathogenic and protective mechanisms.
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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 | 0.000 | 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 | 0.000 |
| 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".