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Record W2053269801 · doi:10.1186/s13104-015-1049-4

Cyclooxygenase 1 mRNA expression is undetectable in Madin Darby Canine Kidney cells

2015· article· en· W2053269801 on OpenAlexafffund
Guillaume Pelletier, Bhaja K. Padhi

Bibliographic record

VenueBMC Research Notes · 2015
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsHealth Canada
FundersNatural Resources Canada
KeywordsCyclooxygenaseCell cultureKidneyMessenger RNACell biologyGene expressionBiologyCancer researchMolecular biologyMedicineGeneInternal medicineEnzymeBiochemistryGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Madin Darby Canine Kidney (MDCK) cells form polarized epithelium in vitro and are routinely used in research fields ranging from protein trafficking to influenza. However, the canine origin of these cells also means that compared to man or mouse, genomic resources are more limited and performance of commercially available antibodies often untested. The synthesis of pro-inflammatory prostaglandins in the kidney is mediated by the constitutively expressed cyclooxygenase 1 and the inducible cyclooxygenase 2 (COX-1 and COX-2, respectively). There are conflicting reports on the expression of COX-1 and COX-2 in MDCK cells and this lingering uncertainty about such important pharmacological targets may affect the interpretation of results obtained from this cell line. RESULTS: In order to definitively settle the issue of cyclooxygenase expression in MDCK cells, we designed PCR primers based on dog genomic sequences to probe COX-1 and COX-2 mRNA expression in MDCK cells and dog kidney. We report that while COX-1 and COX-2 genes are both expressed in dog kidney, COX-1 expression is undetectable in MDCK cells. CONCLUSIONS: By improving the characterization of cyclooxygenase expression in MDCK cells, this study will contribute to a better understanding of the properties of this cell line and lead to improved experimental designs and data interpretations.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.238
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.177
GPT teacher head0.405
Teacher spread0.228 · 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

Citations2
Published2015
Admission routes2
Has abstractyes

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