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Record W4310490737 · doi:10.3389/fped.2022.1020986

Amniotic fluid stem cells: A novel treatment for necrotizing enterocolitis

2022· review· en· W4310490737 on OpenAlexafffund
Felicia Balsamo, Yina Tian, Agostino Pierro, Bo Li

Bibliographic record

VenueFrontiers in Pediatrics · 2022
Typereview
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsNecrotizing enterocolitisMedicineContext (archaeology)Regenerative medicineIntensive care medicineStem cellAmniotic fluidRegeneration (biology)Internal medicineBiologyPregnancy

Abstract

fetched live from OpenAlex

Necrotizing enterocolitis (NEC) is a gastrointestinal disease frequently prevalent in premature neonates. Despite advances in research, there is a lack of accurate, early diagnoses of NEC and the current therapeutic approaches remain exhausted and disappointing. In this review, we have taken a close look at the regenerative medical literature available in the context of NEC treatment. Stem cells from amniotic fluid (AFSC) administration may have the greatest protective and restorative effects on NEC. This review summarizes the potential protection and restoration AFSCs have on NEC-induced intestinal injury while comparing various components within AFSCs like conditioned medium (CM) and extracellular vesicles (EVs). In addition to therapeutic interventions that focus on targeting intestinal epithelial damage and regeneration, a novel discovery that AFSCs act in a Wnt-dependent manner provides insight into this mechanism of protection. Finally, we have highlighted the most important aspects that remain unknown that should be considered to guide future research on the translational application of AFSC-based therapy. We hope that this will be a beneficial frame of reference for the guidance of future studies and towards the clinical application of AFSC and/or its derivatives as a treatment against NEC.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
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
Bibliometrics0.0010.001
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 head0.082
GPT teacher head0.336
Teacher spread0.255 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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