Effect of Cannabinoid 2 Receptor Modulation on the Peripheral Immune Response in Central Nervous System Injury-Induced Immunodeficiency Syndrome
Bibliographic record
Abstract
Introduction: Acute central nervous system (CNS) injury, such as stroke, spinal cord injury, or traumatic brain injury can result in dysregulated immune response, and the condition is known as CNS injury-induced immunodeficiency syndrome (CIDS). The endocannabinoid system is an important homeostatic regulator in the CNS and immune system. Activation of cannabinoid 2 receptors (CB2R) on immune cells has been reported to dampen inflammation, suggesting a potential role of CB2R in the peripheral immune response following CNS injury. In this study, we have investigated the effect of CB2R modulation on the peripheral immune response during CIDS. Materials and Methods: Experimental CNS injury was induced in C57BL/6 mice through intracerebral injection of the vasopressor peptide, endothelin-1. A selective CB2R agonist (HU308) was used as an early treatment before the onset of CIDS and AM630, a selective CB2R antagonist, was administered as a later-phase therapy to combat the systemic immunodeficiency following the CNS injury. The peripheral immune response to endotoxin was studied 24 h after the CNS injury using intravital microscopy to examine leukocyte activation within the intestinal microcirculation in mice. Brain infarct size, and plasma levels of cytokines and soluble adhesion molecules were measured as additional parameters for the assessment of treatment outcomes. Results: Our results showed that early CB2R activation with HU308 reduced brain injury size and restored leukocyte response to endotoxin in the peripheral microcirculation. Late CB2R inhibition with AM630 also improved the peripheral leukocyte response to endotoxin and did not exacerbate the extent of brain injury. Discussion: CB2R activation has the potential to mitigate CNS injury as an early treatment by limiting neuroinflammation and preventing the development of CIDS. At the later stage with already-established CIDS, treatment may require dampening CB2R activation to improve the patient's outcome.
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) | 0.000 | 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) | 0.001 | 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".