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Role of Ca2+-Sensitive Protein Kinase C in Phenylephrine Enhancement of Ca2+ Sensitivity in Rat Tail Artery

2001· article· en· W2318411583 on OpenAlexaff
Koichi Sato, Yasuaki Dohi, Shin Suzuki, Koichi Miyagawa, Hiroyuki Takase, Masayoshi Kojima, Cornelis van Breemen

Bibliographic record

VenueJournal of Cardiovascular Pharmacology · 2001
Typearticle
Languageen
FieldMedicine
TopicNitric Oxide and Endothelin Effects
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhenylephrineSensitivity (control systems)Protein kinase CInternal medicineChemistryMedicineCardiologyBiophysicsPharmacologyEndocrinologyKinaseBiologyBiochemistryBlood pressure

Abstract

fetched live from OpenAlex

We investigated the role of protein kinase C (PKC) isoforms on changes in sensitivity of contractile mechanisms to intracellular Ca(2+) (force /[Ca(2+)]i) by phenylephrine (0.1-100 microM) in rat tail arterial helical strips using simultaneous measurements of force and [Ca(2+)]i. Force/[Ca(2+)]Ii induced by phenylephrine was greater than that induced by 80 mM K+. Force/[Ca(2+)]i induced by phenylephrine in physiologic saline solution or low Ca(2+) solution was dependent on the agonist concentration. Removal of Ca(2+) completely abolished the phenylephrine-induced contraction. The PKC inhibitors staurosporine and calphostin C inhibited the increase in force/[Ca(2+)]i induced by phenylephrine to a much greater extent than that induced by 80 mM K+. LY379196, a specific PKCbeta inhibitor, did not inhibit the increase of calcium sensitivity due to phenylephrine. The classic PKC isoforms, alpha, betaI, and II not gamma were demonstrated in the artery by immunohistochemistry. These results suggest that in rat tail arterial smooth muscle, PKCalpha, and not beta or gamma, mediates the increase of changes in sensitivity of contractile mechanisms to intracellular Ca(2+) to high dose of alpha1 receptor stimulation (phenylephrine 100 microM) on nonphysiologic conditions.

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)0.003
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 score0.015
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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
Published2001
Admission routes1
Has abstractyes

Explore more

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