MétaCan
Menu
Back to cohort
Record W2159120324 · doi:10.3109/21691401.2014.896372

Effect of active Notch signaling system on the early repair of rat sciatic nerve injury

2014· article· en· W2159120324 on OpenAlexaff
Jin Wang, Keyu Ren, Yanhua Wang, Peixun Zhang, Jianping Peng, Lei Deng, Hongbo Zhang, Baoguo Jiang

Bibliographic record

VenueArtificial Cells Nanomedicine and Biotechnology · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsNotch signaling pathwayRegeneration (biology)Sciatic nerveCell biologyPeripheral nerve injuryNeurotrophic factorsNeurotrophinPeripheral nervous systemActivator (genetics)Nerve injuryChimera (genetics)Signal transductionBiologyNeuroscienceAnatomyMedicineInternal medicineReceptorCentral nervous system

Abstract

fetched live from OpenAlex

It is all known that dedifferentiated Schwann cells (SCs) play an important role in neural regeneration, and Notch signaling has complex and extensive regulatory functions in dedifferentiated SCs. So studies have focused on how to improve peripheral nerve repair by regulating proliferation and dedifferentiation in SCs with Notch signaling meloculars.We have found SCs can be activated when adding Recombinant rat jagged1/FC chimera (an activator of the Notch signaling system) in vivo. Compared with that of the control groups, at 4 weeks post-surgery nerve regeneration and functional rehabilitation in the Recombinant rat jagged1/FC chimera group were advanced significantly, and the expression of neurotrophic factors in the regenerated nerves was elevated largely. These results indicated that SCs activated by Notch signaling could promote nerve repair effectively in the early regenerative stage, suggesting the possible clinical application for the treatment of peripheral nerve defects.

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 score0.022
Threshold uncertainty score0.316

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 studies0.0000.001
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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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

Citations18
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueArtificial Cells Nanomedicine and BiotechnologySame topicNerve injury and regenerationFrench-language works237,207