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PKCδ and Pharmacomechanical Coupling in the Cerebral Circulation: Mechanisms to In Vivo Application

2025· article· en· W4411880393 on OpenAlexaffabout
Nadia Haghbin, Mohammed A. El‐Lakany, David Richter, David A. Steven, Keith W. MacDougall, Jonathan C. Lau, Nikolaos M. Tsoukias, Donald G. Welsh

Bibliographic record

VenuePhysiology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsWestern University
Fundersnot available
KeywordsIn vivoCirculation (fluid dynamics)Coupling (piping)Cerebral circulationProtein kinase CChemistryInternal medicineCell biologyBiophysicsCardiologyNeuroscienceBiologyMedicineSignal transductionPhysicsMaterials scienceGeneticsMechanics

Abstract

fetched live from OpenAlex

Introduction: Constrictor stimuli set cerebral arterial blood flow through coupling mechanisms dependent on (electromechanical) and independent of (pharmacomechanical) membrane potential. Each elicit unique “vasomotor signatures”, the latter (via protein kinase C, PKC) allowing small arterial segments to work independently of the broader network. What PKC isoforms drive pharmacomechanical coupling and if focal vasomotor behavior is observable in vivo are questions to be addressed herein. Methods: A multiscale approach from cell-to-live brain, mice-to-humans and which includes western blotting, immunofluorescence, vessel myography, two photon fluorescence microscopy and computational modeling will be employed. Results: A classic GPCR agonist, U46619, was first shown to drive a concentration dependent constriction, with electromechanical coupling preceding pharmacomechanical, the latter coupled to PKC. PKCδ, rather than PKCα mediated the pharmacomechanical response, irrespective of whether the agonist was superfused or applied by pipette to elicit focal constriction. This functional work aligned with cell analysis showing that PKCδ translocates to the membrane upon U46619 stimulation (indicative of activation) and western blot analysis noting that PKCδ blockade attenuated the phosphorylation of key pharmacomechanical regulators (CPI-17, HSP27). Pharmacomechanical coupling was observed in vivo (two photon microscopy) by monitoring the variance in diameter along a penetrating arteriole; this key measure of focal constriction attenuated diminished by PKCδ blockade. Realistic modeling of hemodynamic control revealed that focal constriction will markedly impact blood flow distribution among arteriolar branches. Conclusion: PKCδ was identified as the key isoform mediating pharmacomechanical coupling and focal tone control in cerebral arteries. Discrete phamacomechanical control, as observed in live brain tissue, optimizes blood flow distribution among brain cortical layers. We theorize that augmented pharmacomechanical control underlies brain vascular pathobiology, including arterial vasospasm and transient ischemic attack. Heart and Stroke Foundation of Canada, Natural Science and Engineering Council of Canada, Rorabeck Chair in Neuroscience and Vascular Biology This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.375
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes2
Has abstractyes

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