MétaCan
Menu
Back to cohort
Record W1970731381 · doi:10.3390/ijms15046086

DNA Comet Giemsa Staining for Conventional Bright-Field Microscopy

2014· article· en· W1970731381 on OpenAlexaff
Andreyan N. Osipov, Ekaterina Arkhangelskaya, Alexei Vinokurov, Н. М. Сметанина, Alex Zhavoronkov, Dmitry Klokov

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsGiemsa stainStainingMicroscopyDNAComet assayDNA damageChemistryBiologyMolecular biologyBiophysicsPhysicsOpticsGenetics

Abstract

fetched live from OpenAlex

This study was undertaken to evaluate the compatibility of Giemsa staining protocol with the comet assay. We showed, for the first time, that DNA comets can be visualized and analyzed using Giemsa staining. We generated DNA damage dose response curves for human peripheral blood lymphocytes exposed to X-ray radiation using the comet assay with either SybrGreen I or Giemsa stain. The dose response curves were fitted by linear regressions (R2>0.977). The SybrGreen I results showed only ~1.2-fold higher slope coefficient (method sensitivity) compared to the Giemsa results. The unexpectedly high sensitivity of Giemsa staining for the comet assay is due to the Romanowsky-Giemsa effect, the stain photo-stability and the higher resolution of bright-field imaging compared to fluorescence imaging. Our results demonstrate that Giemsa staining can effectively be used for measuring DNA damage by the comet assay. The low cost and availability of Giemsa stain makes this method affordable for any low budget research and will facilitate new applications of the comet assay in biology and medicine.

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 score0.186
Threshold uncertainty score0.285

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 head0.013
GPT teacher head0.327
Teacher spread0.314 · 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

Citations21
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Molecular SciencesSame topicCarcinogens and Genotoxicity AssessmentFrench-language works237,207