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White Mountain Expedition 2019: The Impact of Sustained Hypoxia on Cerebral Blood Flow Responses and Tolerance to Simulated Hemorrhage

2020· article· en· W3016820187 on OpenAlexaffabout
Alexander J. Rosenberg, Garen K. Anderson, Haley Barnes, Jordan D. Bird, Brandon Pentz, Britta R. M. Byman, Nicholas Jendzjowsky, Richard J. A. Wilson, Trevor A. Day, Caroline A. Rickards

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsUniversity of CalgaryMount Royal University
Fundersnot available
KeywordsPresyncopeMedicineEffects of high altitude on humansBlood pressureHypoxia (environmental)Heart rateAnesthesiaCerebral blood flowCardiologyVascular resistanceCerebral circulationInternal medicineOxygenAnatomyChemistry

Abstract

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Introduction Trauma‐induced hemorrhage can occur at high altitude from a variety of causes, including battlefield injuries, motor vehicle accidents, air accidents, and major falls. The hypoxic environment of high altitude can limit the ability of the cardiovascular system to compensate for blood loss injuries, due, in part, to the reduced arterial oxygen content. In humans, the effect of sustained hypoxia on tolerance to hemorrhage and the cardiovascular and cerebrovascular responses to this stress are unknown. Based on the known compensatory increases in cerebral blood flow that occur with exposure to hypoxia, we hypothesized that tolerance to simulated hemorrhage (via application of lower body negative pressure, LBNP) at high altitude would be similar compared to low altitude due to increased cerebral blood flow and oxygen delivery, and the subsequent preservation of cerebral tissue oxygenation. Methods Healthy human subjects (N=8; 4F, 4M) participated in LBNP protocols to presyncope at low altitude (1045 m, Calgary, Canada) and at high altitude (3800 m, White Mountain, California) following 4–5 days of acclimatization. LBNP chamber pressure was initially reduced to −60 mmHg for 10‐min followed by decreases every 5‐min to −70, −80, −90 and −100 mmHg, until the onset of presyncopal symptoms. Arterial pressure, heart rate, internal carotid artery blood flow, and cerebral oxygen saturation were measured continuously. Stroke volume was derived from the arterial pressure waveform, and systemic vascular resistance was calculated from cardiac output and mean arterial pressure. Time to presyncope and cardiovascular responses were compared between the low and high altitude conditions. Results Time to presyncope was similar between conditions (low altitude: 1276 ± 108 s vs. high altitude: 1208± 108 s; P=0.58). Similar responses to LBNP were observed between low and high altitudes in mean arterial pressure (low altitude: −16±2 % vs. high altitude: −16±2 %; P=0.85), stroke volume (low altitude: −57±5 % vs. high altitude: −60±5 %; P=0.39), systemic vascular resistance (low altitude: +23±9 % vs. high altitude: +38±12 %; P=0.21), and heart rate (low altitude: +69±12 % vs. high altitude: +65±8 %; P=0.71). Internal carotid artery blood flow was higher at high altitude vs. low altitude (condition effect, P=0.01), and decreased with LBNP under both conditions (P≤0.005). There was no effect of high altitude on cerebral oxygen saturation at baseline and presyncope (altitude Effect, P=0.73). Conclusion These findings suggest that the hypoxia induced by ascent to high altitude (3800 m) does not affect tolerance to simulated hemorrhage in young healthy adults, which may be due to 1) similar cardiovascular reflex responses to central hypovolemia, and/or 2) the compensatory increase in cerebral blood flow and subsequent maintenance of oxygen delivery to the tissues, resulting in the preservation of cerebral oxygen saturation. Support or Funding Information AHA 17GRNT33671110

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 score0.010
Threshold uncertainty score0.021

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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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Citations1
Published2020
Admission routes2
Has abstractyes

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