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Record W2026486130 · doi:10.1002/jcp.21995

Are induced pluripotent stem cells the future of cell‐based regenerative therapies for spinal cord injury?

2009· review· en· W2026486130 on OpenAlexaff
Ryan P. Salewski, Eftekhar Eftekharpour, Michael G. Fehlings

Bibliographic record

VenueJournal of Cellular Physiology · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity Health NetworkToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsInduced pluripotent stem cellRegenerative medicineSpinal cord injuryEmbryonic stem cellStem cellMedicineNeuroscienceTransplantationCell therapyStem-cell therapyNeural stem cellRegeneration (biology)BioinformaticsSpinal cordBiologySurgeryCell biology

Abstract

fetched live from OpenAlex

Despite advances in medical and surgical care, current clinical therapies for spinal cord injury (SCI) are limited. During the last two decades, the search for new therapies has been revolutionized by the discovery of stem cells, inspiring scientists and clinicians to search for stem cell-based reparative approaches for many disorders, including neurotrauma. Cell-based therapies using embryonic and adult stem cells in animal models of these disorders have provided positive outcome results. However, the availability of clinically suitable cell sources for human application has been hindered by both technical and ethical issues. The recent discovery of induced pluripotent stem (iPS) cells holds the potential to revolutionize the field of regenerative medicine by offering the option of autologous transplantation, thus eliminating the issue of host rejection. Herein, we will provide the rationale for the use of iPS cells in SCI therapies. In this review, we will evaluate the recent advancements in the field of iPS cells including their capacity for differentiation toward neural lineages that may allow iPS cells transplantation in cell-based therapy for spinal cord repair.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.601
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.049
GPT teacher head0.337
Teacher spread0.288 · 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 designBench or experimental
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

Citations53
Published2009
Admission routes1
Has abstractyes

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