MétaCan
Menu
Back to cohort
Record W2098779039 · doi:10.2174/138955711795906905

Therapeutic Potential of Cannabinoids in the Treatment of Neuroinflammation Associated with Parkinsons Disease

2011· review· en· W2098779039 on OpenAlexaff
Jonathan P. Little, Erika B. Villanueva, Andis Klegeris

Bibliographic record

VenueMini-Reviews in Medicinal Chemistry · 2011
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsNeuroinflammationMedicineDiseasePharmacologyNeuroscienceInternal medicinePsychology

Abstract

fetched live from OpenAlex

The cannabinoid system is represented by two principal receptor subtypes, termed CB1 and CB2, along with several endogenous ligands. In the central nervous system it is involved in several processes. CB1 receptors are mainly expressed by neurons and their activation is primarily implicated in psychotropic and motor effects of cannabinoids. CB2 receptors are expressed by glial cells and are thought to participate in regulation of neuroimmune reactions. This review aims to highlight several reported properties of cannabinoids that could be used to inhibit the adverse neuroinflammatory processes contributing to Parkinson's disease and possibly other neurodegenerative disorders. These include anti-oxidant properties of phytocannabinoids and synthetic cannabinoids as well as hypothermic and antipyretic effects. However, cannabinoids may also trigger signaling cascades leading to impaired mitochondrial enzyme activity, reduced mitochondrial biogenesis, and increased oxidative stress, all of which could contribute to neurotoxicity. Therefore, further pharmacological studies are needed to allow rational design of new cannabinoid-based drugs lacking detrimental in vivo effects.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.346
Teacher spread0.287 · 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

Citations30
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueMini-Reviews in Medicinal ChemistrySame topicCannabis and Cannabinoid ResearchFrench-language works237,207