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Record W2032286908 · doi:10.3354/dao075239

Development and validation of an RNA- and DNA-based quantitative PCR assay for determination of Kudoa thyrsites infection levels in Atlantic salmon Salmo salar

2007· article· en· W2032286908 on OpenAlexaff
VA Funk, Mieke Raap, Ken Sojonky, Simon R. M. Jones, John H. Robinson, Cecilia Falkenberg, KM Miller

Bibliographic record

VenueDiseases of Aquatic Organisms · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMyxozoan Parasites in Aquatic Species
Canadian institutionsMinistère de l'Agriculture, des Pêcheries et de l'AlimentationFisheries and Oceans CanadaBC Centre for Aquatic Health Sciences
FundersOregon State University
KeywordsBiologySalmoHistologyRNAReal-time polymerase chain reaction18S ribosomal RNAHousekeeping geneMolecular biologyGeneGene expressionFish <Actinopterygii>FisheryGenetics

Abstract

fetched live from OpenAlex

Quantitative PCR (QPCR) methods targeting the 18S rDNA gene (DNA QPCR) and cathepsin L mRNA (RNA QPCR) from Kudoa thyrsites (Gilchrist) were developed and compared with histology for determination of K. thyrsites infection levels in Atlantic salmon Salmo salar L. Both QPCR tests were specific, reproducible and sensitive down to 3 copies. DNA QPCR was able to detect lower K. thyrsites infection levels than those detected by RNA QPCR and histology. The higher sensitivity of the DNA-based test compared with the RNA-based test appeared to be biological in nature and suggested that when infection levels were low, there were fewer copies of cathepsin L mRNA than 18S rDNA genes. However, all 3 diagnostic methods were highly correlated. Regression analyses comparing DNA QPCR and histology data from 2 distinct groups of fish showed that the relationship between these 2 diagnostic methods was reproducible. A logistic regression analysis comparing diagnostic data with a visual assessment of post-mortem flesh quality indicated that histology was the single best predictor of flesh quality, followed by DNA QPCR and then RNA QPCR.

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)0.001
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 score0.086
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.296
Teacher spread0.276 · 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

Citations41
Published2007
Admission routes1
Has abstractyes

Explore more

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