MétaCan
Menu
Back to cohort
Record W2050234484 · doi:10.2746/042516408x278184

Distribution of the neurokinin‐1 receptor in equine intestinal smooth muscle

2008· article· en· W2050234484 on OpenAlexaff
Nicole E. Solinger, Ioana M. Sonea

Bibliographic record

VenueEquine Veterinary Journal · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsReceptorTachykinin receptorBiologyGastrointestinal tractMotilitySubstance PNeurokinin ASmall intestineMessenger RNAVasoactive intestinal peptideTachykinin receptor 1AnatomyNeuropeptideInternal medicineEndocrinologyCell biologyMedicineBiochemistryGene

Abstract

fetched live from OpenAlex

Summary Reason for performing study : Tachykinins have profound effects on equine intestinal motility, but the distribution of the neurokinin receptors (NKRs) through which they act is unknown. This study reports the distribution of one of these receptors, the neurokinin‐1 receptor (NK1R), in smooth muscle throughout the equine intestinal tract. Objectives : To quantify the distribution of the NK1R, based upon mRNA expression, in smooth muscle of different regions of the equine intestinal tract. Methods : Nine regions of the intestinal tract were sampled in 5 mature horses. Total RNA was isolated from smooth muscle and reverse transcribed; NK1R mRNA was then quantified using real‐time PCR. Results : NK1R mRNA was found at all levels of the sampled intestinal tract. The smooth muscle of the proximal small intestine and the ventral colon exhibited the highest level of NK1R mRNA expression in the equine intestinal tract. Conclusions : Tachykinins probably affect intestinal contractility and propulsion in the proximal small intestine and in the ventral colon. Potential relevance : Understanding the distribution of the receptors for these peptides allows the design of experiments using tachykinin analogues or antagonists to modulate intestinal motility in disorders such as ileus.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.403

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 integrity0.0000.001
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 head0.079
GPT teacher head0.284
Teacher spread0.205 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueEquine Veterinary JournalSame topicNeuropeptides and Animal PhysiologyFrench-language works237,207