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Record W3034114789 · doi:10.1002/mus.26991

Neutralizing <scp>BDNF</scp> and <scp>FGF2</scp> injection into denervated skeletal muscle improve recovery after nerve repair

2020· article· en· W3034114789 on OpenAlexafffund
Svenja Rink, Christina Chatziparaskeva, Luisa Elles, Stoyan Pavlov, Klaus Nohroudi, Habib Bendella, Levent Sarıkçıoğlu, Maria Eleni Μanthou, Sarah A. Dunlop, Tessa Gordon, Doychin N. Angelov

Bibliographic record

VenueMuscle & Nerve · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsHospital for Sick Children
FundersUniversitätsklinikum KölnUniversität zu KölnAkdeniz ÜniversitesiRoyal College of Physicians and Surgeons of Canada
KeywordsWhisking in animalsReinnervationMedicineDenervationNeuromuscular junctionBrain-derived neurotrophic factorNeurotrophic factorsFacial nerveFacial musclesNerve injuryNeurotrophinInternal medicineAnesthesiaEndocrinologyNeuroscienceAnatomyPsychologySensory systemReceptor

Abstract

fetched live from OpenAlex

BACKGROUND: After facial nerve injury and surgical repair in rats, recovery of vibrissal whisking is associated with a high proportion of mono-innervated neuro-muscular junctions (NMJs). Our earlier work with Sprague Dawley (SD)/Royal College of Surgeons (RCS) rats, which are blind and spontaneously restore NMJ-monoinnervation and whisking, showed correlations between functional recovery and increase of fibroblast growth factor-2 (FGF2) and brain-derived neurotrophic factor (BDNF) in denervated vibrissal muscles. METHODS: We used normally sighted rats (Wistar), in which NMJ-polyinnervation is highly correlated with poor whisking recovery, and injected the vibrissal muscle levator labii superioris (LLS) with combinations of BDNF, anti-BDNF, and FGF2 at different postoperative periods after facial nerve injury. RESULTS: Rats receiving anti-BDNF+FGF2 showed low NMJ-polyinnervation and best recovery of whisking amplitude. CONCLUSIONS: Restoration of target reinnervation after peripheral nerve injury requires a complex mixture of trophic factors with a specific time course of availability for each of them.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.021
GPT teacher head0.240
Teacher spread0.219 · 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 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
Published2020
Admission routes2
Has abstractyes

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