Prospects for cannabinoid therapies in viral encephalitis
Bibliographic record
Abstract
Cannabinoids are promising therapies to support neurogenesis and decelerate disease progression in neuroinflammatory and degenerative disorders. Whether neuroprotective effects of cannabinoids are sustainable during persistent viral infection of the CNS is not known. Using a rodent model of chronic viral encephalitis based on Borna Disease (BD) virus, in which 1 week treatment with the general cannabinoid WIN 55,212-2 has been shown to be neuroprotective (Solbrig et al., 2010), we examine longer term (2 week treatment) effects of a general (CB1 and CB2) cannabinoid receptor agonist WIN55,212-2 (1mg/kg ip twice per day) or a specific (CB2) cannabinoid receptor agonist HU-308 (5mg/kg ip once daily) on histopathology, measures of frontostriatal neurogenesis and gliogenesis, and viral load. We find that WIN and HU-308 differ in their ability to protect new BrdU(+) cells. The selective CB2 agonist HU increases BrdU(+) cells in prefrontal cortex (PFC), significantly increases BrdU(+) cells in striatum, differentially regulates polydendrocytes vs. microglia/macrophages, and reduces immune activation at a time WIN-treated rats appear tolerant to the anti-inflammatory effect of their cannabinoid treatment. WIN and HU had little direct viral effect in PFC and striatum, yet reduced viral signal in hippocampus. Thus, HU-308 action on CB2 receptors, receptors known to be renewed during microglia proliferation and action, is a nontolerizing mechanism of controlling CNS inflammation during viral encephalitis by reducing microglia activation, as well as partially limiting viral infection, and uses a nonpsychotropic cannabinoid agonist.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".