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Record W2029756717 · doi:10.1159/000138596

Effect of Noradrenaline on Rubidium ( <sup>86</sup> Rb) Efflux in the Rat Isolated Seminal Vesicle

2008· article· en· W2029756717 on OpenAlexaff
F. A. WALI, E. GREENIDGE

Bibliographic record

VenuePharmacology · 2008
Typearticle
Languageen
FieldMedicine
TopicNeonatal Health and Biochemistry
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPapaverinePhentolamineRubidiumEffluxChemistryVesicleDepolarizationSodiumEndocrinologyInternal medicinePotassiumBiologyBiochemistryMembraneMedicine

Abstract

fetched live from OpenAlex

The effect of noradrenaline (NA) on the efflux of rubidium (86Rb) from the rat isolated seminal vesicle was studied in the absence and presence of phentolamine and papaverine. Strips of spirally cut seminal vesicles were incubated in radioactive 86Rb (10 microCi.ml-1) for 2 h. Radioactivity was measured using an autogamma spectrometer, and the flux data were expressed in terms of rate constants per minute, of the reactive isotope efflux. A concentration-effect curve for the effect of NA on rat seminal vesicles was constructed, alone and in the presence of phentolamine (1 mumol/l) or papaverine (5 mumol/l). The results showed that NA (10-1,000 mumol/l) produced concentration-dependent contractions in the rat seminal vesicle. These responses were greatly reduced by phentolamine but markedly enhanced by papaverine. NA significantly increased 86Rb efflux from rat seminal vesicles (control rate constant 0.0042 +/- 0.001; test 0.0064 +/- 0.002, means +/- SE; n = 8 rats; p less than 0.001). This increase (52%) occurred within an exposure of 7 +/- 1 min to NA. Phentolamine decreased the NA-induced increase in 86Rb efflux, by 75 +/- 2%, whereas papaverine enhanced it by 86 +/- 5% of the control value. The mechanism of NA-induced increase in 86Rb efflux was not further investigated but was interpreted in terms of an increase in intracellular K+ (here represented by 86Rb), which will leave the cell (efflux) after initial membrane depolarization.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.406

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 head0.014
GPT teacher head0.335
Teacher spread0.321 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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