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Record W2994799575 · doi:10.1113/jp279237

A mathematical model of cerebral blood flow control in anaemia and hypoxia

2019· article· en· W2994799575 on OpenAlexafffund
James Duffin, Gregory M. T. Hare, Joseph A. Fisher

Bibliographic record

VenueThe Journal of Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsSt. Michael's HospitalThornhill Medical (Canada)University of Toronto
FundersUniversity of Toronto
KeywordsHypoxia (environmental)Cerebral blood flowBlood flowMedicineCardiologyPhysiologyChemistryOxygen

Abstract

fetched live from OpenAlex

KEY POINTS: The control of cerebral blood flow in hypoxia, anaemia and hypocapnia is reviewed with an emphasis on the links between cerebral blood flow and possible stimuli. A mathematical model is developed to examine the changes in the partial pressure of oxygen in brain tissue associated with changes in cerebral blood flow regulation produced by carbon dioxide, anaemia and hypoxia. The model demonstrates that hypoxia, anaemia and hypocapnia, alone or in combination, produce varying degrees of cerebral hypoxia, an effect exacerbated when blood flow regulation is impaired. The suitability of brain hypoxia as a common regulator of cerebral blood flow in hypoxia and anaemia was explored, although we failed to find support for this hypothesis. Rather, cerebral blood flow appears to be related to arterial oxygen concentration in both anaemia and hypoxia. ABSTRACT: A mathematical model is developed to examine the changes in the partial pressure of oxygen in brain tissue associated with changes in cerebral blood flow regulation produced by carbon dioxide, anaemia and hypoxia. The model simulation assesses the physiological plausibility of some currently hypothesized cerebral blood flow control mechanisms in hypoxia and anaemia, and also examines the impact of anaemia and hypoxia on brain hypoxia. In addition, carbon dioxide is examined for its impact on brain hypoxia in the context of concomitant changes associated with anaemia and hypoxia. The model calculations are based on a single compartment of brain tissue with constant metabolism and perfusion pressure, as well as previously developed equations describing oxygen and carbon dioxide carriage in blood. Experimental data are used to develop the control equations for cerebral blood flow regulation. The interactive model illustrates that there are clear interactions of anaemia, hypoxia and carbon dioxide in the determination of cerebral blood flow and brain tissue oxygen tension. In both anaemia and hypoxia, cerebral blood flow increases to maintain oxygen delivery, with brain hypoxia increasing when cerebral blood flow control mechanisms are impaired. Hypocapnia superimposes its effects, increasing brain hypoxia. Hypoxia, anaemia and hypocapnia, alone or in combination, produce varying degrees of cerebral hypoxia, and this effect is exacerbated when blood flow regulation is degraded by conditions that negatively impact cerebrovascular control. Differences in brain hypoxia in anaemia and hypoxia suggest that brain oxygen tension is not a plausible sensor for cerebral blood flow control.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations33
Published2019
Admission routes2
Has abstractyes

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