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Record W2123531430 · doi:10.15283/ijsc.2008.1.1.27

Human Nerual Stem Cells for Brain Repair

2008· review· en· W2123531430 on OpenAlexaff
Seung Up Kim, Hong J. Lee, In H. Park, Kon Chu, Soon T. Lee, Manho Kim, Jae Kyu Roh, Seung K. Kim, Kyu C. Wang

Bibliographic record

VenueInternational Journal of Stem Cells · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsAmyotrophic lateral sclerosisMedicineNeural stem cellStem cellStroke (engine)DiseaseNeuroscienceMultiple sclerosisTransplantationStem-cell therapySpinal cord injurySpinal cordPathologyBiologyImmunologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Cell replacement therapy and gene transfer to the diseased or injured brain have provided the basis for the development of potentially powerful new therapeutic strategies for a broad spectrum of human neurological diseases including Parkinson disease, Huntington disease, amyotrophic lateral sclerosis (ALS), Alzheimer disease, multiple sclerosis (MS), stroke, spinal cord injury and brain cancer. In recent years, neurons and glial cells have successfully been generated from neural stem cells, and extensive efforts by investigators to develop neural stem cell-based transplantation therapies have been carried out. We review here notable experimental and pre-clinical studies we have previously conducted involving human neural stem cell-based cell- and gene-therapies for Parkinson disease, Huntington disease, ALS, stroke and brain cancer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.006

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 head0.357
Teacher spread0.307 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations7
Published2008
Admission routes1
Has abstractyes

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