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Record W2165039690 · doi:10.2174/1574888x113086660068

Immunogenicity and Tumorigenicity of Pluripotent Stem Cells and their Derivatives: Genetic and Epigenetic Perspectives

2013· review· en· W2165039690 on OpenAlexafffund
Yuan Tan, Sarah Ooi, Lisheng Wang

Bibliographic record

VenueCurrent Stem Cell Research & Therapy · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsInduced pluripotent stem cellReprogrammingImmunogenicityEpigeneticsBiologyImmune systemStem cellCell therapyMajor histocompatibility complexCancer researchCellEmbryonic stem cellImmunologyCell biologyGeneticsGene

Abstract

fetched live from OpenAlex

One aim of stem cell-based therapy is to utilize pluripotent stem cells (PSCs) as a supplementary source of cells to repair or replace tissues or organs that have ceased to function due to severe tissue damage. However, PSC-based therapy requires extensive research to ascertain if PSC derivatives are functional without the risk of tumorigenicity, and also do not engender severe immune rejection that threatens graft survival and function. Recently, the suitability of induced pluripotent stem cells applied for patient-tailored cell therapy has been questioned since the discovery of several genetic and epigenetic aberrations during the reprogramming process. Hence, it is crucial to understand the effect of these abnormalities on the immunogenicity and survival of PSC grafts. As induced PSC-based therapy represents a hallmark for the potential solution to prevent and arrest immune rejection, this review also summarizes several up-to-date key findings in the field.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.094
GPT teacher head0.361
Teacher spread0.268 · 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

Citations71
Published2013
Admission routes2
Has abstractyes

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