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Record W1995159287 · doi:10.1159/000137442

Influence of Cannabinoids on Somatic Cells in vivo

2008· article· en· W1995159287 on OpenAlexaff
AM. Zimmerman, Y Vishnu Raj

Bibliographic record

VenuePharmacology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsToronto Zoo
Fundersnot available
KeywordsCannabinolMicronucleus testCannabidiolCannabinoidBone marrowMicronucleusBiologyPharmacologyMitotic indexMetaphaseAndrologyCannabisInternal medicineMitosisMedicineToxicityImmunologyGeneticsChromosome

Abstract

fetched live from OpenAlex

Chromosomal and nuclear aberrations in bone marrow cells were studied in hybrid mice of genotype (C57BL x C3H)F1 following treatment with specific cannabinoids. In the subacute series, mice were treated for 5 consecutive days with delta 9-tetrahydrocannabinol (THC), cannabinol or cannabidiol at a dose of 10 mg/kg, the percentage of micronuclei in cannabinoid-treated mice was 3- to 5-fold greater than in the dimethyl sulfoxide controls. In the acute series, polychromatic erythrocytes were scored after a single exposure. In one acute series there was an approximately twofold increase in the incidence of micronuclei in the cannabinoid-treated mice; in the other acute series of cannabinoid-treated animals the micronuclei values were as high as 6 times the control values. Interaction between dose and frequency of exposure was assessed for THC; the number of micronuclei was affected by THC dosage, but not by frequency of exposure. Further confirmation of nuclear aberrations was obtained from assessment of bone marrow mitosis. In this limited study of mitotic cells the incidence of metaphase aberrations in cannabinoid-treated animals was 5-7 times greater than in controls.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.279
Teacher spread0.269 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations79
Published2008
Admission routes1
Has abstractyes

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