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Record W4391781867 · doi:10.1113/jp285555

Severe hypoxaemic hypercapnia compounds cerebral oxidative–nitrosative stress during extreme apnoea: Implications for cerebral bioenergetic function

2024· article· en· W4391781867 on OpenAlexafffund
Damian M. Bailey, Anthony R. Bain, Ryan L. Hoiland, Otto Barak, Ivan Drviš, Benjamin S. Stacey, Angelo Iannetelli, Gareth W. Davison, Rasmus Holmboe Dahl, Ronan M. G. Berg, David B. MacLeod, Željko Dujić, Philip N. Ainslie

Bibliographic record

VenueThe Journal of Physiology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsInterior HealthUniversity of British ColumbiaVancouver General HospitalUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaHrvatska Zaklada za ZnanostHigher Education Funding Council for Wales
KeywordsHypercapniaBioenergeticsOxidative stressMedicineAnesthesiaChemistryInternal medicineMitochondrionBiochemistryAcidosis

Abstract

fetched live from OpenAlex

We examined the extent to which apnoea-induced extremes of oxygen demand/carbon dioxide production impact redox regulation of cerebral bioenergetic function. Ten ultra-elite apnoeists (six men and four women) performed two maximal dry apnoeas preceded by normoxic normoventilation, resulting in severe end-apnoea hypoxaemic hypercapnia, and hyperoxic hyperventilation designed to ablate hypoxaemia, resulting in hyperoxaemic hypercapnia. Transcerebral exchange of ascorbate radicals (by electron paramagnetic resonance spectroscopy) and nitric oxide metabolites (by tri-iodide chemiluminescence) were calculated as the product of global cerebral blood flow (by duplex ultrasound) and radial arterial (a) to internal jugular venous (v) concentration gradients. Apnoea duration increased from 306 ± 62 s during hypoxaemic hypercapnia to 959 ± 201 s in hyperoxaemic hypercapnia (P ≤ 0.001). Apnoea generally increased global cerebral blood flow (all P ≤ 0.001) but was insufficient to prevent a reduction in the cerebral metabolic rates of oxygen and glucose (P = 0.015-0.044). This was associated with a general net cerebral output (v > a) of ascorbate radicals that was greater in hypoxaemic hypercapnia (P = 0.046 vs. hyperoxaemic hypercapnia) and coincided with a selective suppression in plasma nitrite uptake (a > v) and global cerebral blood flow (P = 0.034 to <0.001 vs. hyperoxaemic hypercapnia), implying reduced consumption and delivery of nitric oxide consistent with elevated cerebral oxidative-nitrosative stress. In contrast, we failed to observe equidirectional gradients consistent with S-nitrosohaemoglobin consumption and plasma S-nitrosothiol delivery during apnoea (all P ≥ 0.05). Collectively, these findings highlight a key catalytic role for hypoxaemic hypercapnia in cerebral oxidative-nitrosative stress. KEY POINTS: Local sampling of blood across the cerebral circulation in ultra-elite apnoeists determined the extent to which severe end-apnoea hypoxaemic hypercapnia (prior normoxic normoventilation) and hyperoxaemic hypercapnia (prior hyperoxic hyperventilation) impact free radical-mediated nitric oxide bioavailability and global cerebral bioenergetic function. Apnoea generally increased the net cerebral output of free radicals and suppressed plasma nitrite consumption, thereby reducing delivery of nitric oxide consistent with elevated oxidative-nitrosative stress. The apnoea-induced elevation in global cerebral blood flow was insufficient to prevent a reduction in the cerebral metabolic rates of oxygen and glucose. Cerebral oxidative-nitrosative stress was greater during hypoxaemic hypercapnia compared with hyperoxaemic hypercapnia and coincided with a lower apnoea-induced elevation in global cerebral blood flow, highlighting a key catalytic role for hypoxaemia. This applied model of voluntary human asphyxia might have broader implications for the management and treatment of neurological diseases characterized by extremes of oxygen demand and carbon dioxide production.

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 score0.099
Threshold uncertainty score0.474

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 science0.0010.000
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 head0.066
GPT teacher head0.302
Teacher spread0.236 · 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

Citations16
Published2024
Admission routes2
Has abstractyes

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