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Record W4396674959 · doi:10.3390/cimb46050266

Modulation of Oxidative Stress and Neuroinflammation by Cannabidiol (CBD): Promising Targets for the Treatment of Alzheimer’s Disease

2024· review· en· W4396674959 on OpenAlexafffund
Jordan P. Hickey, Andrila E. Collins, Mackayla L. Nelson, Helen Chen, Bettina E. Kalisch

Bibliographic record

VenueCurrent Issues in Molecular Biology · 2024
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsCannabidiolNeuroinflammationNeuroprotectionOxidative stressMedicineDementiaPharmacologyDiseaseNeuroscienceAlzheimer's diseaseBiologyCannabisPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and the most common form of dementia globally. Although the direct cause of AD remains under debate, neuroinflammation and oxidative stress are critical components in its pathogenesis and progression. As a result, compounds like cannabidiol (CBD) are being increasingly investigated for their ability to provide antioxidant and anti-inflammatory neuroprotection. CBD is the primary non-psychotropic phytocannabinoid derived from Cannabis sativa. It has been found to provide beneficial outcomes in a variety of medical conditions and is gaining increasing attention for its potential therapeutic application in AD. CBD is not psychoactive and its lipophilic nature allows its rapid distribution throughout the body, including across the blood–brain barrier (BBB). CBD also possesses anti-inflammatory, antioxidant, and neuroprotective properties, making it a viable candidate for AD treatment. This review outlines CBD’s mechanism of action, the role of oxidative stress and neuroinflammation in AD, and the effectiveness and limitations of CBD in preclinical models of AD.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.431
Teacher spread0.377 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations27
Published2024
Admission routes2
Has abstractyes

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