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Record W4387184444 · doi:10.3389/fphar.2023.1247550

Anti-oxidant effects of cannabidiol relevant to intracerebral hemorrhage

2023· review· en· W4387184444 on OpenAlexafffund
Gaili Yan, Xiangyu Zhang, Hongmin Li, Yan Guo, V. Wee Yong, Mengzhou Xue

Bibliographic record

VenueFrontiers in Pharmacology · 2023
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsCannabidiolOxidative stressMedicinePharmacologyIntracerebral hemorrhageExcitotoxicityContext (archaeology)Reactive oxygen speciesNeuroscienceChemistryBiologyBiochemistryPsychiatryInternal medicineGlutamate receptorReceptorCannabis

Abstract

fetched live from OpenAlex

Intracerebral hemorrhage (ICH) is a subtype of stroke with a high mortality rate. Oxidative stress cascades play an important role in brain injury after ICH. Cannabidiol, a major non-psychotropic phytocannabinoids, has drawn increasing interest in recent years as a potential therapeutic intervention for various neuropsychiatric disorders. Here we provide a comprehensive review of the potential therapeutic effects of cannabidiol in countering oxidative stress resulting from ICH. The review elaborates on the various sources of oxidative stress post-ICH, including mitochondrial dysfunction, excitotoxicity, iron toxicity, inflammation, and also highlights cannabidiol's ability to inhibit ROS/RNS generation from these sources. The article also delves into cannabidiol's role in promoting ROS/RNS scavenging through the Nrf2/ARE pathway, detailing both extranuclear and intranuclear regulatory mechanisms. Overall, the review underscores cannabidiol's promising antioxidant effects in the context of ICH and suggests its potential as a therapeutic option.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.029
GPT teacher head0.369
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2023
Admission routes2
Has abstractyes

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Same venueFrontiers in PharmacologySame topicIntracerebral and Subarachnoid Hemorrhage ResearchFrench-language works237,207