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Record W2131985503 · doi:10.1016/j.ijsu.2013.01.008

Improvement of functional recovery of transected peripheral nerve by means of artery grafts filled with diclofenac

2013· article· en· W2131985503 on OpenAlexaff
Rahim Mohammadi, Mohammad-Adel Hirsaee, Keyvan Amini

Bibliographic record

VenueInternational Journal of Surgery · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineSciatic nerveDiclofenacSalineGastrocnemius muscleRegeneration (biology)AnesthesiaPeripheral nerveArterySurgeryAnatomySkeletal muscle

Abstract

fetched live from OpenAlex

The objective was to assess the effect of topically administered diclofenac on peripheral nerve regeneration and functional recovery. Eighty male healthy white Wistar rats were divided into four experimental groups (n = 20), randomly: In transected group (TC), left sciatic nerve was transected and stumps were fixed in the adjacent muscle. In treatment group defect was bridged using an artery graft (AG/DICLO) filled with 10 μL diclofenac (0.1 mg/kg). In artery graft group (AG), the graft was filled with phosphate-buffered saline alone. In sham-operated group (SHAM), sciatic nerve was exposed and manipulated. Each group was subdivided into four subgroups of five animals each and regenerated nerve fibers were studied 4, 8, 12 and 16 weeks after surgery. Behavioral testing, sciatic nerve functional study, gastrocnemius muscle mass and morphometric indices confirmed faster recovery of regenerated axons in AG/DICLO than AG group (p < 0.05). In immunohistochemistry, location of reactions to S-100 in AG/DICLO was clearly more positive than that in AG group. When loaded in an artery graft, diclofenac improved functional recovery and morphometric indices of sciatic nerve. Topical application of this readily available agent offers the benefit of cost savings as well as avoiding the complications associated with systemic administration.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.577

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)0.0010.000

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 head0.023
GPT teacher head0.227
Teacher spread0.204 · 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

Citations24
Published2013
Admission routes1
Has abstractyes

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