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Record W4396609929 · doi:10.1089/ham.2023.0090

The Potential Interplay Between HIF-1α, Angiogenic, and Autophagic Signaling During Intermittent Hypoxic Exposure and Exercise

2024· review· en· W4396609929 on OpenAlexaff
Quint Berkemeier, Michael R. Deyhle, James J. McCormick, Kurt A. Escobar, Christine M. Mermier

Bibliographic record

VenueHigh Altitude Medicine & Biology · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHypoxia (environmental)AutophagyMedicinePhysical medicine and rehabilitationBiologyChemistryApoptosisBiochemistry

Abstract

fetched live from OpenAlex

25:326-336, 2024.-Environmental hypoxia as a result of decreased barometric pressure upon ascent to high altitudes (>2,500 m) presents increased physiological demands compared with low altitudes, or normoxic environments. Competitive athletes, mountaineers, wildland firefighters, military personnel, miners, and outdoor enthusiasts commonly participate in, or are exposed to, forms of exercise or physical labor at moderate to high altitudes. However, the majority of research on intermittent hypoxic exposure is centered around hematological markers, and the skeletal muscle cellular responses to exercise in hypoxic environments remain largely unknown. Two processes that may be integral for the maintenance of cellular health in skeletal muscle include angiogenesis, or the formation of new blood vessels from preexisting vasculature and autophagy, a process that removes and recycles damaged and dysfunctional cellular material in the lysosome. The purpose of this review is to is to examine the current body of literature and highlight the potential interplay between low-oxygen-sensing pathways, angiogenesis, and autophagy during acute and prolonged intermittent hypoxic exposure in conjunction with exercise. The views expressed in this paper are those of the authors and do not reflect the official policy of the Department of Army, DOD, DOE, ORAU/ORISE or U.S. Government.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.310
Teacher spread0.296 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2024
Admission routes1
Has abstractyes

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