MétaCan
Menu
Back to cohort
Record W4402759265 · doi:10.3390/insects15100737

Genome-Wide Search for Gene Mutations Likely Conferring Insecticide Resistance in the Common Bed Bug, Cimex lectularius

2024· article· en· W4402759265 on OpenAlexfundno aff
Kouhei Toga, Fumiko Kimoto, Hiroki Fujii, Hidemasa Bono

Bibliographic record

VenueInsects · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatological diseases and infestations
Canadian institutionsnot available
FundersCo-creation place formation support programInstitute of GeneticsJapan Science and Technology Agency
KeywordsCimex lectulariusBiologyBed bugGeneticsInsecticide resistanceGenomeGeneKnockdown resistanceHemipteraToxicologyPesticideZoologyEcology

Abstract

fetched live from OpenAlex

that exhibits a higher pyrethroid resistance ratio compared to many previously discovered strains. We sequenced the genomes of the pyrethroid-resistant and susceptible strains using long-read sequencing, resulting in the construction of highly contiguous genomes (N50 of the resistant strain: 2.1 Mb and N50 of the susceptible strain: 1.5 Mb). Gene prediction was performed by BRAKER3, and the functional annotation was performed by the Fanflow4insects workflow. Next, we compared their amino acid sequences to identify gene mutations, identifying 729 mutated transcripts that were specific to the resistant strain. Among them, those defined previously as resistance genes were included. Additionally, enrichment analysis implicated DNA damage response, cell cycle regulation, insulin metabolism, and lysosomes in the development of pyrethroid resistance. Genome editing of these genes can provide insights into the evolution and mechanisms of insecticide resistance. This study expanded the target genes to monitor allele distribution and frequency changes, which will likely contribute to the assessment of resistance levels. These findings highlight the potential of genome-wide approaches to understand insecticide resistance in bed bugs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.364

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 head0.049
GPT teacher head0.330
Teacher spread0.281 · 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 designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInsectsSame topicDermatological diseases and infestationsFrench-language works237,207