MétaCan
Menu
Back to cohort
Record W3185289368 · doi:10.1002/cnr2.1475

Cannabinoids, their cellular receptors, and effects on the invasive phenotype of carcinoma and metastasis

2021· review· en· W3185289368 on OpenAlexafffund
Judah Glogauer, Jonathan Blay

Bibliographic record

VenueCancer Reports · 2021
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsDalhousie UniversityUniversity of WaterlooMcMaster UniversityRegional Municipality of Waterloo
FundersMcMaster UniversityBeatrice Hunter Cancer Research InstituteCancer Research Institute
KeywordsPhenotypeMetastasisReceptorCancer researchBiologyNeuroscienceCancerGeneGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: The morbidity and mortality of cancer are significantly impacted by the invasive and metastatic potential of particular subgroups of malignant cells within a tumor. The particular pre-metastatic properties of cancerous cells are thus a critical target for novel therapeutics in the oncology field. Cannabinoid molecules have been investigated in recent years in the context of invasion and metastasis of various malignancies, with varying effects reported in the literature. RECENT FINDINGS: There was substantial variability in the findings reported by the literature of the effects of cannabinoid molecules on cancer cell invasion and metastasis. These effects varied depending on which ligand and which of the CB1, CB2, or GPR55 receptors were investigated. These findings suggest a role for the phenomenon of biased signaling in explaining the diversity of effects of cannabinoid molecules on cancer cell invasion. CONCLUSION: While substantially more investigation is required into the effects of cannabinoid molecules on cancer cell invasion and metastasis, we describe in this review the significant diversity in the responses of cancer cells to cannabinoid molecules in terms of their invasive and metastatic capacities.

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 score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.040
GPT teacher head0.321
Teacher spread0.281 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCancer ReportsSame topicCannabis and Cannabinoid ResearchFrench-language works237,207