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Record W2900078893 · doi:10.1002/mus.26379

Effects of treadmill training on microvascular remodeling in the rat after spinal cord injury

2018· article· en· W2900078893 on OpenAlexfundno aff
Roger W. P. Kissane, Oliver Wright, Yazi D. Al'Joboori, Paulina Marczak, Ronaldo M. Ichiyama, Stuart Egginton

Bibliographic record

VenueMuscle & Nerve · 2018
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
FundersMedical Research CouncilMedical Research Council CanadaInternational Spinal Research Trust
KeywordsMedicineSpinal cordSpinal cord injurySkeletal muscleAnatomyGlycolysisStimulationEndocrinologyAnesthesiaInternal medicineMetabolism

Abstract

fetched live from OpenAlex

INTRODUCTION: The morphological characteristics of skeletal muscles innervated caudal to a spinal cord injury (SCI) undergo dramatic phenotypic and microvascular changes. METHOD: Female Sprague-Dawley rats received a severe contusion at thoracic level 9/10 and were randomly assigned to locomotor training, epidural stimulation, or a combination of the treatment groups (CB). Fiber type composition and capillary distribution were assessed in phenotypically distinct compartments of the tibialis anterior. RESULTS: Spinal cord injury induced a shift in type II fiber phenotype from oxidative to glycolytic (P < 0.05) as well as capillary loss within the oxidative core and glycolytic cortex; the CB treatment best maintained capillary supply within both compartments. DISCUSSION: The angiogenic response of CB training improved capillary distribution across the muscle; capillary distribution became spatially more homogeneous and mean capillary supply area decreased, potentially improving oxygenation. There is an important role for weight-bearing training in maintaining the oxidative phenotype of muscle after SCI. Muscle Nerve 59:370-379, 2019.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.053
GPT teacher head0.366
Teacher spread0.313 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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