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Record W4409311797 · doi:10.1016/j.biopha.2025.118044

Cannabinoid Receptor 2 (CB2R) as potential target for the pharmacological treatment of neurodegenerative diseases

2025· review· en· W4409311797 on OpenAlexaff
Ramona Meanti, Elena Bresciani, Laura Rizzi, Laura Molteni, Silvia Coco, Robert J. Omeljaniuk, Antonio Torsello

Bibliographic record

VenueBiomedicine & Pharmacotherapy · 2025
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsLakehead University
FundersUniversità degli Studi di Milano-Bicocca
KeywordsCannabinoid receptorEndocannabinoid systemCannabinoidMedicineNeurosciencePharmacologyReceptorInternal medicineBiologyAntagonist

Abstract

fetched live from OpenAlex

The endocannabinoid system (ECS) is a ubiquitous physiological system that plays a crucial role in maintaining CNS homeostasis and regulating its functions. It includes cannabinoid receptors (CBRs), endogenous cannabinoids (eCBs), and the enzymes responsible for their synthesis and degradation. In recent years, growing evidence has highlighted the therapeutic potential of the ECS and CBRs, in a wide range of severe diseases and pathological conditions, including Alzheimer's and Parkinson's diseases, Amyotrophic Lateral Sclerosis, Multiple Sclerosis, Huntington's Disease, HIV-1 associated neurocognitive disorders, neuropathic pain and migraine. Targeting the cannabinoid type 2 receptor (CB2R) has gained attention due to its ability to (i) mitigate neuroinflammatory responses, (ii) regulate mitochondrial function and (iii) provide trophic support, all without eliciting the psychotropic actions associated with CB1R activation. This review aims to explore the potential of CB2R modulation as a strategy for the prevention and treatment of neurologic disorders, exploring both preclinical and clinical findings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.045
GPT teacher head0.429
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2025
Admission routes1
Has abstractyes

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