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Record W3196747987 · doi:10.3390/cancers13174353

Cannabinoids and Endocannabinoid System Changes in Intestinal Inflammation and Colorectal Cancer

2021· review· en· W3196747987 on OpenAlexafffund
Viktoriia Cherkasova, Olga Kovalchuk, Igor Kovalchuk

Bibliographic record

VenueCancers · 2021
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Lethbridge
FundersMitacs
KeywordsEndocannabinoid systemColorectal cancerInflammationMedicineCancerInternal medicineReceptor

Abstract

fetched live from OpenAlex

Despite the multiple preventive measures and treatment options, colorectal cancer holds a significant place in the world's disease and mortality rates. The development of novel therapy is in critical need, and based on recent experimental data, cannabinoids could become excellent candidates. This review covered known experimental studies regarding the effects of cannabinoids on intestinal inflammation and colorectal cancer. In our opinion, because colorectal cancer is a heterogeneous disease with different genomic landscapes, the choice of cannabinoids for tumor prevention and treatment depends on the type of the disease, its etiology, driver mutations, and the expression levels of cannabinoid receptors. In this review, we describe the molecular changes of the endocannabinoid system in the pathologies of the large intestine, focusing on inflammation and cancer.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.344
Teacher spread0.312 · 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

Citations16
Published2021
Admission routes2
Has abstractyes

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