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Record W4386986858 · doi:10.1016/j.mex.2023.102395

Determining superoxide dismutase content and catalase activity in mammalian cell lines

2023· article· en· W4386986858 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMethodsX · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsUniversity of Saskatchewan
FundersWater Research CommissionDepartment of Science and Technology, Ministry of Science and Technology, IndiaNational Research Foundation
KeywordsSuperoxide dismutaseCatalaseCuvetteAbsorbanceEnzymeChemistryCell cultureBiochemistryFood scienceMolecular biologyChromatographyBiology

Abstract

fetched live from OpenAlex

Traditional methods for determining superoxide dismutase (SOD) content and catalase (CAT) activity rely on measuring the absorbance of individual tissue (biological) samples using a cuvette and spectrophotometer, rather than cell cultures. Although there are kits available for SOD and CAT assays, these allow for high-throughput analysis of samples and might be too expensive for research laboratories in countries from the Global South, such as South Africa. This paper describes a simple and cost-effective method to determine SOD content and CAT activity in mammalian cell cultures following exposure to environmental chemical mixtures by measuring absorbance in 96-well microplates. Moreover, the equipment used for this method is considered standard for cell culture laboratories, while the reagents and consumables are easily obtainable.•Antioxidant enzyme levels can be measured in vitro in cell cultures.•The supernatant obtained can be used to determine protein concentration, SOD content, and CAT activity.•This method is simple and affordable, allowing for the analysis of multiple samples (up to 32 samples per microplate).

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.501

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 head0.427
GPT teacher head0.461
Teacher spread0.034 · 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