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Record W1572158051 · doi:10.1111/imb.12003

Multidrug resistance protein gene expression in <i> <scp>T</scp> </i> <i>richoplusia ni</i> caterpillars

2012· article· en· W1572158051 on OpenAlexafffund
Jason D. Simmons, Olivia D'Souza, Mark R. Rheault, Cam Donly

Bibliographic record

VenueInsect Molecular Biology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British ColumbiaAgriculture and Agri-Food CanadaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyGeneMultiple drug resistanceGene expressionResistance (ecology)Molecular biologyGeneticsCell biologyDrug resistanceEcology

Abstract

fetched live from OpenAlex

Many insect species exhibit pesticide-resistant phenotypes. One of the mechanisms capable of contributing to resistance is the overexpression of multidrug resistance (MDR) transporter proteins. Here we describe the cloning of three genes encoding MDR proteins from Trichoplusia ni: trnMDR1, trnMDR2 and trnMDR3. Real-time quantitative PCR (qPCR) detected trnMDR mRNA in the whole nervous system, midgut and Malpighian tubules of final instar T. ni caterpillars. To test whether these genes are upregulated in response to chemical challenge in this insect, qPCR was used to compare trnMDR mRNA levels in unchallenged insects with those of insects fed the synthetic pyrethroid, deltamethrin. Only limited increases were detected in a single gene, trnMDR2, which is the most weakly expressed of the three MDR genes, suggesting that increased multidrug resistance of this type is not a significant part of the response to deltamethrin exposure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2012
Admission routes2
Has abstractyes

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