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Record W2283190641 · doi:10.1152/jappl.2000.88.3.981

Pulmonary vascular response of the coati to chronic hypoxia

2000· article· en· W2283190641 on OpenAlexaff
W. L. Hanson, Dona F. Boggs, J. M. Kày, S. E. Hofmeister, Osamu Okada, Wiltz W. Wagner

Bibliographic record

VenueJournal of Applied Physiology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsMcMaster University
FundersNational Heart, Lung, and Blood Institute
KeywordsHypoxic pulmonary vasoconstrictionPulmonary hypertensionCardiologyHypoxia (environmental)Effects of high altitude on humansInternal medicineRight ventricular hypertrophyVasoconstrictionLungVascular resistanceMedicineAnatomyHemodynamicsChemistryOxygen

Abstract

fetched live from OpenAlex

The unusually muscular pulmonary arteries normally present in cattle and swine residing at low altitude are associated with a rapid development of severe pulmonary hypertension when those animals are moved to high altitude. Because these species lack collateral ventilation, they appear to have an increased dependence on hypoxic vasoconstriction to maintain normal ventilation-perfusion balance, which, in turn, maintains thickened arterial walls. The only other species known to lack collateral ventilation is the coati, which, similarly, has thick-walled pulmonary arteries. We tested the hypothesis that coatis will develop severe high-altitude pulmonary hypertension by exposing six of these animals (Nasua narica) to a simulated altitude of 4,900 m for 6 wk. After the exposure, pulmonary arterial pressures were hardly elevated, right ventricular hypertrophy was minimal, there was no muscularization of pulmonary arterioles, and, most surprising of all, there was a decrease in medial thickness of muscular pulmonary arteries. These unexpected results break a consistent cross-species pattern in which animals with thick muscular pulmonary arteries at low altitude develop severe pulmonary hypertension at high altitude.

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 score0.279
Threshold uncertainty score0.316

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 science0.0000.000
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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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

Citations12
Published2000
Admission routes1
Has abstractyes

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