MétaCan
Menu
Back to cohort
Record W1633530489 · doi:10.3109/01480545.2015.1012211

Evaluation of cytotoxic and genotoxic effects of Benodanil by using Allium and Micronucleus assays

2015· article· en· W1633530489 on OpenAlexfundno aff
Dilek Akyıl, Arzu Özkara, Sevim Feyza Erdoğmuş, Yasin Eren, Muhsin Konuk, Esra Sağlam

Bibliographic record

VenueDrug and Chemical Toxicology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsnot available
FundersMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsMitotic indexEC50AlliumMicronucleus testMicronucleusGenotoxicityChemistryBiologyToxicologyMitosisBotanyIn vitroToxicityBiochemistryGenetics

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the potential cytotoxic effects of Benodanil fungicide by employing both mitotic index (MI) and mitotic phases on the root meristem cells of Allium cepa and genotoxic effects by using in vitro micronucleus assay (MN) in human peripheral blood lymphocyte. In the Allium root growth inhibition test, the EC50 value was first determined as 25 ppm. Then, 2 × EC50 value (50 ppm), EC50 value (25 ppm), and 1/2 × EC50 value (12.5 ppm) were tested with different treatment periods (24, 48, and 72 h). Both negative and positive controls were also used in parallel experiments. We obtained that mitotic index and prophase index decreased when compared with the control in all concentrations. In the micronucleus assay, lymphocytes were treated with various concentrations (250, 500, 750, and 1000 µg/ml) of Benodanil for 24 and 48 h. The results showed that Benodanil did not induce MN frequency in all concentrations of both treatment periods. Additionally, it was determined that this pesticide decreased nuclear division index (NDI) significantly. It was concluded that Benodanil has a cytotoxic effects depending on decreasing of MI and NDI.

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 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 score0.006
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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.

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

Citations8
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueDrug and Chemical ToxicologySame topicCarcinogens and Genotoxicity AssessmentFrench-language works237,207