Immunomodulatory Strategies in Herpes Simplex Virus Encephalitis
Bibliographic record
Abstract
Herpes simplex virus 1 (HSV-1) can be responsible for life-threatening HSV encephalitis (HSE). The mortality rate of patients with HSE who do not receive antiviral treatment is 70%, with most survivors suffering from permanent neurological sequelae. The use of intravenous acyclovir together with improved diagnostic technologies such as PCR and magnetic resonance imaging has resulted in a reduction in the mortality rate to close to 20%. However, 70% of surviving patients still do not recover complete neurological functions. Thus, there is an urgent need to develop more effective treatments for a better clinical outcome. It is well recognized that cerebral damage resulting from HSE is caused by viral replication together with an overzealous inflammatory response. Both of these processes constitute potential targets for the development of innovative therapies against HSE. In this review, we discuss recent progress in therapy that may be used to ameliorate the outcome of patients with HSE, with a particular emphasis on immunomodulatory agents. Ideally, the administration of adjunctive immunomodulatory drugs should be initiated during the rise of the inflammatory response, and its duration should be limited in time to reduce undesired effects. This critical time frame should be optimized by the identification of reliable biomarkers of inflammation.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".