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Record W3112335398 · doi:10.1186/s12917-020-02696-7

The use of induced pluripotent stem cells in domestic animals: a narrative review

2020· review· en· W3112335398 on OpenAlexafffund
Rachel A. Scarfone, Samantha M. Pena, Keith A. Russell, Dean H. Betts, Thomas G. Koch

Bibliographic record

VenueBMC Veterinary Research · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsWestern UniversityUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInduced pluripotent stem cellRegenerative medicineStem cellNarrative reviewBiotechnologyNeuroscienceBiologyMedicineEmbryonic stem cellIntensive care medicineCell biology

Abstract

fetched live from OpenAlex

Induced pluripotent stem cells (iPSCs) are undifferentiated stem cells characterized by the ability to differentiate into any cell type in the body. iPSCs are a relatively new and rapidly developing technology in many fields of biology, including developmental anatomy and physiology, pathology, and toxicology. These cells have great potential in research as they are self-renewing and pluripotent with minimal ethical concerns. Protocols for their production have been developed for many domestic animal species, which have since been used to further our knowledge in the progression and treatment of diseases. This research is valuable both for veterinary medicine as well as for the prospect of translation to human medicine. Safety, cost, and feasibility are potential barriers for this technology that must be considered before widespread clinical adoption. This review will analyze the literature pertaining to iPSCs derived from various domestic species with a focus on iPSC production and characterization, applications for tissue and disease research, and applications for disease treatment.

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)0.001
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.400
GPT teacher head0.466
Teacher spread0.066 · 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

Citations32
Published2020
Admission routes2
Has abstractyes

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