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Record W2907635517 · doi:10.1126/sciadv.aau2956

Peripheral nerve transfers change target muscle structure and function

2019· article· en· W2907635517 on OpenAlexaff
Konstantin D. Bergmeister, Martin Aman, Silvia Muceli, Ivan Vujaklija, Krisztina Manzano-Szalai, Ewald Unger, Ruth A. Byrne, Clemens Scheinecker, O Riedl, Stefan Salminger, Florian Frommlet, Gregory H. Borschel, Dario Farina, Oskar C. Aszmann

Bibliographic record

VenueScience Advances · 2019
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsHospital for Sick Children
FundersEuropean Research CouncilChristian Doppler Forschungsgesellschaft
KeywordsReinnervationMotor unitMotor unit recruitmentNeurophysiologyAnatomyNeurosciencePeripheralPopulationMotor neuronElectromyographyMedicineBiologySpinal cord

Abstract

fetched live from OpenAlex

Selective nerve transfers surgically rewire motor neurons and are used in extremity reconstruction to restore muscle function or to facilitate intuitive prosthetic control. We investigated the neurophysiological effects of rewiring motor axons originating from spinal motor neuron pools into target muscles with lower innervation ratio in a rat model. Following reinnervation, the target muscle's force regenerated almost completely, with the motor unit population increasing to 116% in functional and 172% in histological assessments with subsequently smaller muscle units. Muscle fiber type populations transformed into the donor nerve's original muscles. We thus demonstrate that axons of alternative spinal origin can hyper-reinnervate target muscles without loss of muscle force regeneration, but with a donor-specific shift in muscle fiber type. These results explain the excellent clinical outcomes following nerve transfers in neuromuscular reconstruction. They indicate that reinnervated muscles can provide an accurate bioscreen to display neural information of lost body parts for high-fidelity prosthetic control.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.261

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 communication0.0000.001
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 head0.008
GPT teacher head0.215
Teacher spread0.206 · 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 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

Citations81
Published2019
Admission routes1
Has abstractyes

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