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Record W3047964401 · doi:10.3390/v12080875

Cannabinoid-Induced Immunomodulation during Viral Infections: A Focus on Mitochondria

2020· review· en· W3047964401 on OpenAlexaff
Cherifa Beji, Hamza Loucif, Roman Telittchenko, David Olagnier, Xavier Dagenais-Lussier, Julien van Grevenynghe

Bibliographic record

VenueViruses · 2020
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsNeuroprotectionCannabinoid receptorImmune systemContext (archaeology)BiologyCannabinoidMitochondrionReceptorNeuroscienceImmunologyCell biologyGenetics

Abstract

fetched live from OpenAlex

This review examines the impact of cannabinoids on viral infections, as well as its effects on the mitochondria of the nervous and immune system. The paper conveys information about the beneficial and negative impacts of cannabinoids on viral infections, especially HIV-1. These include effects on the inflammatory response as well as neuroprotective effects. We also explore non-apoptotic mitochondrial pathways modulated by the activity of cannabinoids, resulting in modifications to cellular functions. As a large part of the literature derives from studies of the nervous system, we first compile the information related to mitochondrial functions in this system, particularly through the CB1 receptor. Finally, we reflect on how this knowledge could complement what has been demonstrated in the immune system, especially in the context of the CB2 receptor and Ca2+ uptake. The overall conclusion of the review is that cannabinoids have the potential to affect a broad range of cell types through mitochondrial modulation, be it through receptor-specific action or not, and that this pathway has a potential implication in cases of viral infection.

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 score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.058
GPT teacher head0.368
Teacher spread0.310 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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