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Record W3002808143 · doi:10.1002/stem.3107

Unlocking the paradoxical endogenous stem cell response after spinal cord injury

2019· review· en· W3002808143 on OpenAlexaff
Laureen D. Hachem, Andrea J. Mothe, Charles H. Tator

Bibliographic record

VenueStem Cells · 2019
Typereview
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsOntario Brain InstituteToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsBiologySpinal cord injuryProgenitor cellNeural stem cellStem cellNeuroscienceSpinal cordEndogenyRegeneration (biology)PopulationProgenitorCell biologyEpendymal CellMultipotent Stem CellMedicineEndocrinology

Abstract

fetched live from OpenAlex

Nearly a century ago, the concept of the secondary injury in spinal cord trauma was first proposed to explain the complex cascade of molecular and cellular events leading to widespread neuronal and glial cell death after trauma. In recent years, it has been established that the ependymal region of the adult mammalian spinal cord contains a population of multipotent neural stem/progenitor cells (NSPCs) that are activated after spinal cord injury (SCI) and likely play a key role in endogenous repair and regeneration. How these cells respond to the various components of the secondary injury remains poorly understood. Emerging evidence suggests that many of the biochemical components of the secondary injury cascade which have classically been viewed as deleterious to host neuronal and glial cells may paradoxically trigger NSPC activation, proliferation, and differentiation thus challenging our current understanding of secondary injury mechanisms in SCI. Herein, we highlight new findings describing the response of endogenous NSPCs to spinal cord trauma, redefining the secondary mechanisms of SCI through the lens of the endogenous population of stem/progenitor cells. Moreover, we outline how these insights can fuel novel stem cell-based therapeutic strategies to repair the injured spinal cord.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.004

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 head0.152
GPT teacher head0.325
Teacher spread0.173 · 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 designOther design
Domainnot available
GenreReview

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

Citations25
Published2019
Admission routes1
Has abstractyes

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