MétaCan
Menu
Back to cohort
Record W2899494636 · doi:10.1113/jp276599

UBC‐Nepal expedition: peripheral fatigue recovers faster in Sherpa than lowlanders at high altitude

2018· article· en· W2899494636 on OpenAlexafffund
Luca Ruggiero, Ryan L. Hoiland, Alexander B. Hansen, Philip N. Ainslie, Chris J. McNeil

Bibliographic record

VenueThe Journal of Physiology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaInterior Health
FundersCentre National de la Recherche ScientifiqueNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsEffects of high altitude on humansPeripheralHypoxia (environmental)OxygenationAcclimatizationMedicinePhysical medicine and rehabilitationCardiologyNeuroscienceInternal medicineBiologyAnatomyChemistryOxygen

Abstract

fetched live from OpenAlex

Key points The reduced oxygen tension of high altitude compromises performance in lowlanders. In this environment, Sherpa display superior performance, but little is known on this issue. Sherpa present unique genotypic and phenotypic characteristics at the muscular level, which may enhance resistance to peripheral fatigue at high altitude compared to lowlanders. We studied the impact of gradual ascent and exposure to high altitude (5050 m) on peripheral fatigue in age‐matched lowlanders and Sherpa, using intermittent electrically‐evoked contractions of the knee extensors. Peripheral fatigue (force loss) was lower in Sherpa during the first part of the protocol. Post‐protocol, the rate of force development and contractile impulse recovered faster in Sherpa than in lowlanders. At any time, indices of muscle oxygenation were not different between groups. Muscle contractile properties in Sherpa, independent of muscle oxygenation, were less perturbed by non‐volitional fatigue. Hence, elements within the contractile machinery contribute to the superior physical performance of Sherpa at high altitude. Abstract Altitude‐related acclimatisation is characterised by marked muscular adaptations. Lowlanders and Sherpa differ in their muscular genotypic and phenotypic characteristics, which may influence peripheral fatigability at altitude. After gradual ascent to 5050 m, 12 lowlanders and 10 age‐matched Sherpa (32 ± 10 vs. 31 ± 11 years, respectively) underwent three bouts (separated by 15 s rest) of 75 intermittent electrically‐evoked contractions (12 pulses at 15 Hz, 1.6 s between train onsets) of the dominant leg quadriceps, at the intensity which initially evoked 30% of maximal voluntary force. Trains were also delivered at minutes 1, 2 and 3 after the protocol to measure recovery. Tissue oxygenation index (TOI) and total haemoglobin (tHb) were quantified by a near‐infrared spectroscopy probe secured over rectus femoris. Superficial femoral artery blood flow was recorded using ultrasonography, and delivery of oxygen was estimated (eDO2). At the end of bout 1, peak force was greater in Sherpa than in lowlanders (91.5% vs. 84.5% baseline, respectively; P < 0.05). Peak rate of force development (pRFD), the first 200 ms of the contractile impulse (CI200), and half‐relaxation time (HRT) recovered faster in Sherpa than in lowlanders (percentage of baseline at 1 min: pRFD: 89% vs. 74%; CI200: 91% vs. 80%; HRT: 113% vs. 123%, respectively; P < 0.05). Vascular measures were pooled for lowlanders and Sherpa as they did not differ during fatigue or recovery (P < 0.05). Mid bout 3, TOI was decreased (90% baseline) whereas tHb was increased (109% baseline). After bout 3, eDO2 was markedly increased (1266% baseline). The skeletal muscle of Sherpa seemingly favours repeated force production at altitude for similar oxygen delivery compared to lowlanders.

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 score0.005
Threshold uncertainty score0.010

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)0.0030.001

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 head0.015
GPT teacher head0.260
Teacher spread0.244 · 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".

Quick stats

Citations12
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of PhysiologySame topicHigh Altitude and HypoxiaFrench-language works237,207