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Record W4407375514 · doi:10.1093/stcltm/szae090

Assessment of immune modulation strategies to enhance survival and integration of human neural progenitor cells in rodent models of spinal cord injury

2025· review· en· W4407375514 on OpenAlexafffund
Zijian Lou, Narihito Nagoshi, James Hong, Nader Hejrati, Jonathon Chon Teng Chio, Mohamad Khazaei, Michael G. Fehlings

Bibliographic record

VenueStem Cells Translational Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchWings for LifeKrembil FoundationUniversität Basel
KeywordsSpinal cord injuryProgenitor cellNeural stem cellImmune systemNeuroscienceImmune modulationRat modelRodent modelMedicineProgenitorSpinal cordBiologyStem cellImmunologyCell biologyInternal medicine

Abstract

fetched live from OpenAlex

Regenerative therapies are currently lacking for spinal cord injury (SCI). Neural progenitor cells (NPCs) have emerged as a promising therapeutic approach. To facilitate translation of NPCs into the clinic, studying human NPCs in rodent models is required. The preclinical study of human NPCs in rodent models of SCI necessitates an optimal selection of immunomodulatory strategies, requiring a balance between modulating the immune system and preserving its functionality.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.104
GPT teacher head0.471
Teacher spread0.367 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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