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Record W2067891932 · doi:10.1517/14712598.2013.849690

Stem cells as a potential therapy for necrotizing enterocolitis

2013· review· en· W2067891932 on OpenAlexaff
Simon Eaton, Augusto Zani, Agostino Pierro, Paolo De Coppi

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2013
Typereview
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsNecrotizing enterocolitisStem cellMedicineDiseaseStem-cell therapyEnterocolitisMesenchymal stem cellIntensive care medicineClinical trialCell therapyBioinformaticsImmunologyInternal medicinePathologyBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: Necrotizing enterocolitis (NEC) is a severe gastrointestinal disease of neonates, especially those born prematurely, that remains an important cause of morbidity and mortality. Although current treatments such as inotropes, antibiotics and ventilation are supportive, there is an urgent need for novel therapies that specifically target the affected intestine. AREAS COVERED: We briefly introduce the disease and the effects on intestinal epithelia. We provide a brief description of amniotic fluid stem (AFS) cells, and then describe some recent data in which AFS cells were beneficial in an animal model of NEC and a potential mechanism is described. The effects of AFS cells are compared with data on bone marrow mesenchymal stem cells. The potential implications of these findings for therapy are discussed. EXPERT OPINION: The current data are promising and demonstrate that stem cells do have an effect in rodent models of NEC. However, the short timescale, limited ability for longitudinal evaluation and uncertain clinical relevance of these models means that there are considerable challenges to be overcome before attempting stem cell therapy in clinical trials. Nevertheless, these data open up novel areas of research into a prevention or therapy for this devastating disease.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.219
GPT teacher head0.432
Teacher spread0.213 · 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 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

Citations29
Published2013
Admission routes1
Has abstractyes

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