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White Mountain Expedition 2019: Peaks and Valleys ‐ Oscillatory cerebral blood flow at high altitude

2020· article· en· W3016319246 on OpenAlexaff
Garen K. Anderson, Alexander J. Rosenberg, 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
KeywordsCerebral blood flowMiddle cerebral arteryTranscranial DopplerEffects of high altitude on humansCerebral circulationCerebral perfusion pressureCerebral autoregulationOxygenationBlood flowMedicineOxygen saturationAnesthesiaHemodynamicsBlood pressureCardiologyInternal medicineOxygenChemistryAnatomyAutoregulationIschemia

Abstract

fetched live from OpenAlex

Introduction Cerebral tissue oxygenation can be impaired by decreases in oxygen delivery as a result of reduced cerebral blood flow, and environmental conditions such as ascent to high altitude. Recent evidence suggests that an oscillatory pattern in cerebral blood flow (at ~0.1 Hz) may protect cerebral oxygenation under conditions of cerebral hypoperfusion. In this study, we hypothesized that inducing oscillations in cerebral blood flow at 0.1 Hz would protect cerebral blood flow and cerebral tissue oxygen saturation during exposure to combined simulated hemorrhage and sustained hypobaric hypoxia (ascent and partial acclimatization to high altitude). Methods 8 healthy human subjects (4 M, 24.7 ± 4.1 y; 4 F, 34.3 ± 8.3 y) participated in two experiments at high altitude (White Mountain, California, USA; altitude, 3800 m): 1) a control condition (CTRL) where lower body negative pressure (LBNP) was used to induce central hypovolemia by reducing chamber pressure to −60 mmHg for 10‐min, and 2) oscillatory LBNP (OLBNP) where chamber pressure was reduced to −60 mmHg, then oscillated every 5‐s between −30 mmHg and −90 mmHg for 10‐min (0.1 Hz). Measurements included internal carotid artery (ICA) blood flow via duplex Doppler ultrasound, middle cerebral artery velocity (MCAv) via transcranial Doppler ultrasound, and cerebral tissue oxygen saturation via near‐infrared spectroscopy. Frequency analysis (via fast Fourier transform) was performed to verify that oscillations in mean MCAv were generated at ~0.1 Hz. Data were analyzed with a linear mixed‐model. All data are represented as mean ± SE. Results Low frequency power (0.07–0.15 Hz) in mean MCAv increased during OLBNP vs. CTRL (P = 0.02). OLBNP did not protect ICA flow (OLBNP: −32.5 ± 4.5 Δ%; CTRL: −19.9 ± 8.9 Δ%; P = 0.18) or mean MCAv (OLBNP: −18.5 ± 3.4 Δ%; CTRL: −15.3 ± 5.4 Δ%; P = 0.58), but cerebral tissue oxygenation was protected (OLBNP: −0.67 ± 1.0 Δ%; CTRL: −4.07 ± 2.0 Δ%; P = 0.004). Conclusions These results support our hypothesis that inducing oscillatory blood flow leads to protection of cerebral tissue oxygenation, despite no differences in ICA blood flow or mean MCAv. Overall, these data suggest that therapies using oscillatory perfusion may help preserve cerebral tissue oxygen saturation under conditions of reduced oxygen delivery. 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

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)0.0010.000

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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
Published2020
Admission routes1
Has abstractyes

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