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Record W1534672288 · doi:10.3171/2014.5.jns132132

Sensory recovery after cell therapy in peripheral nerve repair: effects of naïve and skin precursor-derived Schwann cells

2014· article· en· W1534672288 on OpenAlexafffund
Antos Shakhbazau, Chandan B. Mohanty, Ranjan Kumar, Rajiv Midha

Bibliographic record

VenueJournal of neurosurgery · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsMedicineRegeneration (biology)Schwann cellPeripheral nerve injurySciatic nerveSensory systemNerve injuryCell therapyPeripheralPeripheral neuropathySensory nerveNeuroscienceAnesthesiaPathologyStem cellInternal medicineEndocrinologyCell biologyBiology

Abstract

fetched live from OpenAlex

OBJECT: Cell therapy is a promising candidate among biological or technological innovations sought to augment microsurgical techniques in peripheral nerve repair. This report describes long-term functional regenerative effects of cell therapy in the rat injury model with a focus on sensory recovery. METHODS: Schwann cells were derived from isogenic nerve or skin precursor cells and injected into the transected and immediately repaired sciatic nerve distal to the injury site. Sensory recovery was assessed at weeks 4, 7, and 10. Axonal regeneration was assessed at Week 11. RESULTS: By Week 10, thermal sensitivity in cell therapy groups returned to a level indistinguishable from the baseline (p > 0.05). Immunohistochemistry at 11 weeks after injury showed improved regeneration of NF+ and IB4+ axons. CONCLUSIONS: The results of this study show that cell therapy significantly improves thermal sensation and the number of regenerated sensory neurons at 11 weeks after injury. These findings contribute to the view of skin-derived stem cells as a reliable source of Schwann cells with therapeutic potential for functional recovery in damaged peripheral 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)0.000
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 score0.015
Threshold uncertainty score0.638

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 integrity0.0000.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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

Citations46
Published2014
Admission routes2
Has abstractyes

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