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Record W4300089485 · doi:10.3389/fvets.2022.990884

Occurrence of MDR1 1-delta mutation in herding dog breeds in Portugal

2022· article· en· W4300089485 on OpenAlexaboutno aff
Maria Cristina Barroso, Andreia Grilo, Sandra I. Aguiar, Frederico Aires‐da‐Silva, Berta São Bráz

Bibliographic record

VenueFrontiers in Veterinary Science · 2022
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsMutationBreedHerdingGeneticsBiologyVeterinary medicinePortugueseGeneMedicineGeography

Abstract

fetched live from OpenAlex

The impact of drug transporters in veterinary medicine has been recognized in recent years. One of the most well-characterized is the product of the MDR1 gene, P-gp. A 4-bp deletion in the MDR1 gene known since 2001 has been described to affect herding dog breeds. Since many used drugs in veterinary medicine are substrates for P-gp, including the macrocyclic lactones, such as avermectins, this 4-bp deletion causes a pathological condition known as “ivermectin toxicosis.” For this reason, it is important to determine the animal status concerning this mutation. In Portugal, the information of the occurrence of this mutation in our breeds is limited. The aim of the present study was to determine the occurrence of this mutation and evaluate its association with Portuguese and non-Portuguese dog breeds in Portugal. To achieve this, a total of 105 animals were studied for the presence of the MDR1 4-bp deletion, 23 of which were from Barbado da Terceira, 10 from Cão da Serra d'Aires, 55 belonging to breeds known to carry the mutation (Australian Shepperd, Border Collie and others) and 17 to other breeds (Labrador Retriever, Jack Russel, and others). Despite the small sample size, we observed the presence of the MDR1 1—delta mutation in previously described breeds and identified this mutation in Barbado da Terceira breed for the first time.

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)0.001
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 score0.123
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.036
GPT teacher head0.338
Teacher spread0.302 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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