Cannabinoid 1 receptors are critical for the innate immune response to TLR4 stimulation
Bibliographic record
Abstract
Sickness behaviors are host defense adaptations that arise from integrated autonomic outputs in response to activation of the innate immune system. These behaviors include fever, anorexia, and hyperalgesia intended to promote survival of the host when encountering pathogens. Cannabinoid (CB) receptor activation can induce hypothermia and attenuate LPS-evoked fever. The aim of the present study was to examine the role of CB1 receptors in the LPS-evoked febrile response. CB1 receptor-deficient (CB1(-/-)) mice did not display LPS-evoked fever; likewise, pharmacological blockade of CB1 receptors in wild-type mice blocked LPS-evoked fever. This unresponsiveness is not limited to thermogenesis, as the animals were not hyperalgesic after LPS administration. A Toll-like receptor (TLR)3 agonist and viral mimetic polyinosinic:polycytidylic acid evoked a robust fever in CB1(-/-) mice, suggesting TLR3-mediated responses are functional. LPS-evoked c-Fos activation in areas of the brain associated with the febrile response was evident in wild-type mice but not in CB1(-/-) mice. Liver and spleen TLR4 mRNA were significantly lower in CB1(-/-) mice compared with wild-type mice, and peritoneal macrophages from CB1(-/-) mice did not release proinflammatory cytokines in response to LPS. These data indicate that CB1 receptors play a critical role in LPS-induced febrile responses through inhibiting TLR4-mediated cytokine production.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".