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Cerebrovascular Compliance is Affected by Posture

2018· article· en· W3176081022 on OpenAlexaff
M. Erin Moir, M. Zamir, Stephen A. Klassen, Christopher S. Balestrini, J. Kevin Shoemaker

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineSupine positionHemodynamicsCompliance (psychology)Blood pressureForearmCardiologyMiddle cerebral arteryCerebral blood flowBlood flowCuffBrachial arteryAnesthesiaCerebral circulationInternal medicineSurgeryIschemia

Abstract

fetched live from OpenAlex

The mechanical properties of the arterial vascular wall influence the oscillatory component of flow through a vascular bed. Windkessel modeling has often been used to non‐invasively estimate these mechanical properties. While the systemic and coronary circulation have received considerable attention, the cerebral circulation represents a unique challenge because the oscillatory component of cerebral blood flow occurs within a pressurized and rigid skull. The present investigation evaluated the compliance of the cerebral vascular bed in comparison to that of the forearm vascular bed, a bed without significant extravascular pressure. Six young and healthy adults (24 ± 3 years, 5 females) were instrumented with a 3‐lead electrocardiogram, a finger photoplethysmograph (Finapres Medical Systems), and transcranial Doppler ultrasound (Multigon Industries) for continuous measures of heart rate, arterial blood pressure from the brachial artery, and cerebral blood flow velocity from the middle cerebral artery, respectively. Brachial artery blood flow velocity was also measured (duplex Doppler ultrasound; GE Healthcare). Following stabilization of hemodynamic variables, one to two minutes of data were collected in the supine posture. Measures were repeated after participants transitioned to a sitting posture, following re‐stabilization of variables. A modified Windkessel model was used to calculate the values of vascular compliance, as well as viscoelasticity and inertance effects, that are required to match the measured flow waveform to that predicted from the measured pressure waveform. Estimates of compliance were calculated for the forearm and cerebral vascular beds. In the supine posture, compliance of the cerebral vascular bed was lower than that of the forearm vascular bed by more than one order of magnitude (0.0006 ± 0.0005 vs. 0.002 ± 0.0007 mL/mmHg; P ≤0.001; d= 2.6). However, when seated, cerebrovascular compliance and forearm vascular compliance were not significantly different (0.002 ± 0.001 vs. 0.003 ± 0.002 mL/mmHg; P= 0.10; d= 1.0). The compliance in the brain increased considerably from the supine to sitting posture ( P= 0.03; d= 0.91) but no significant difference was observed in the forearm ( P= 0.13; d= −0.65). The results suggest that intracranial pressure may prevent the cerebral vessels from exercising their compliance in supine. Further, as cerebrovascular compliance is increased with sitting, we speculate that posture‐induced reductions in cerebral venous volume affect cerebrovascular mechanics. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0010.000
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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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