Insights into the pathogenesis of herpes simplex encephalitis from mouse models
Bibliographic record
Abstract
A majority of the world population is infected with herpes simplex viruses (HSV; human herpesvirus types 1 and 2). These viruses, perhaps best known for their manifestation in the genital or oral mucosa, can also cause herpes simplex encephalitis, a severe and often fatal disease of the central nervous system. Antiviral therapies for HSV are only partially effective since the virus can establish latent infections in neurons, and severe pathological sequelae in the brain are common. A better understanding of disease pathogenesis is required to develop new strategies against herpes simplex encephalitis, including the precise viral and host genetic determinants that promote virus invasion into the central nervous system and its associated immunopathology. Here we review the current understanding of herpes simplex encephalitis from the host genome perspective, which has been illuminated by groundbreaking work on rare herpes simplex encephalitis patients together with mechanistic insight from single-gene mouse models of disease. A complex picture has emerged, whereby innate type I interferon-mediated antiviral signaling is a central pathway to control viral replication, and the regulation of immunopathology and the balance between apoptosis and autophagy are critical to disease severity in the central nervous system. The lessons learned from mouse studies inform us on fundamental defense mechanisms at the interface of host-pathogen interactions within the central nervous system, as well as possible rationales for intervention against infections from severe neuropathogenic viruses.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".