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Topically-administered acetyl-L-carnitine increases sciatic nerve regeneration and improves functional recovery after tubulization of transected short nerve gaps

2017· article· en· W2914150874 on OpenAlexaff
Rahim Mohammadi, Keyvan Amini

Bibliographic record

VenueJournal of Neurosurgical Sciences · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSciatic nerveMedicineSiliconeRegeneration (biology)Gastrocnemius muscleCarnitineSalineAnesthesiaAnatomyInternal medicineChemistrySkeletal muscleBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Peripheral nerve injuries repair is still among the most challenging and concern-raising tasks in neurosurgery. The effect of an acetyl-L-carnitin-loaded silicone tube as an in-situ delivery system in defects bridging was studied using a rat sciatic nerve regeneration model. METHODS: A 10-mm sciatic nerve defect was bridged using a silicone tube (SIL/ALC) filled with 10 µL acetyl-L-carnitine (100 ng/mL). In the control group (SIL), the tube was filled with the same volume of the phosphate-buffered solution. The regenerated fibers were studied 4, 8, 12 and 16 weeks after surgery. RESULTS: The functional study confirmed faster recovery of the regenerated axons in acetyl-L-carnitine treated than control group (P<0.05). The mean ratios of gastrocnemius muscles weight were measured. There was a statistically significant difference between the muscle weight ratios of SIL/ALC and SIL groups (P<0.05). Morphometric indices of regenerated fibers showed that the number and diameter of the myelinated fibers in SIL/ALC were significantly higher than in the control group. In immuohistochemistry, the location of reactions to S-100 in the SIL/ALC group was clearly more positive than in the SIL group. CONCLUSIONS: Acetyl-L-carnitine, when loaded in a silicone tube, can bring to an improvement in functional recovery and quantitative morphometric indices of sciatic nerve.

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)0.001
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 score0.176
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.050
GPT teacher head0.290
Teacher spread0.240 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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