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Record W2278951251 · doi:10.1385/1-59259-046-2:305

Monitoring MMP and TIMP mRNA Expression by RT-PCR

2003· article· en· W2278951251 on OpenAlexaff
Howard Wong, Huong Muzik, Lori Lynne Groft, Marc A. Lafleur, Charles Matouk, Peter Forsyth, Gilbert A. Schultz, Steven J. Wall, Dylan R. Edwards

Bibliographic record

VenueHumana Press eBooks · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsFoothills Medical CentreAlberta Children's HospitalUniversity of CalgaryCalgary Laboratory Services
Fundersnot available
KeywordsReverse transcriptaseGlyceraldehyde 3-phosphate dehydrogenaseMolecular biologyComplementary DNAPrimer (cosmetics)Messenger RNAReal-time polymerase chain reactionReverse transcription polymerase chain reactionPolymerase chain reactionChemistryMatrix metalloproteinaseBiologyBiochemistryGene

Abstract

fetched live from OpenAlex

Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) is a sensitive and rapid method to monitor the expression of specific mRNAs. Here we describe two approaches to quantification of steady-state levels of Matrix Metalloproteinase (MMP) and Tissue Inhibitor of Metalloproteinase (TIMP) mRNAs using RT-PCR. The first is a modified RT-PCR protocol called the “primer-dropping” method (1), which involves the simultaneous amplification of the specific MMP and TIMP target mRNAs (which are expressed in variable amounts in human cells) and that of an internal standard (glyceraldehyde phosphate dehydrogenase, GAPDH) which is expressed at constant levels. The internal standard provides a means to monitor the RT-PCR reaction efficiencies and to normalize the reaction products. The second approach uses coamplification of the specific target with a multi-MMP competitor cDNA within the same tube (2).

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)0.000
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 score0.143
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.250
Teacher spread0.226 · 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

Citations19
Published2003
Admission routes1
Has abstractyes

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