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Record W2895900900 · doi:10.1152/physiol.00026.2018

Pluripotent Stem Cell-Derived Pancreatic Progenitors and β-Like Cells for Type 1 Diabetes Treatment

2018· review· en· W2895900900 on OpenAlexafffund
Rangarajan Sambathkumar, Adriana Migliorini, M. Cristina Nostro

Bibliographic record

VenuePhysiology · 2018
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of TorontoToronto General HospitalUniversity Health Network
FundersToronto General and Western Hospital FoundationJuvenile Diabetes Research Foundation International
KeywordsInduced pluripotent stem cellProgenitor cellStem cellCellCell typeSAFERPancreasBiologyIn vitroCell therapyIn vivoType 2 diabetesCell biologyBioinformaticsDiabetes mellitusComputational biologyEmbryonic stem cellComputer scienceBiotechnologyEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

In this review, we focus on the processes guiding human pancreas development and provide an update on methods to efficiently generate pancreatic progenitors (PPs) and β-like cells in vitro from human pluripotent stem cells (hPSCs). Furthermore, we assess the strengths and weaknesses of using PPs and β-like cell for cell replacement therapy for the treatment of Type 1 diabetes with respect to cell manufacturing, engrafting, functionality, and safety. Finally, we discuss the identification and use of specific cell surface markers to generate safer populations of PPs for clinical translation and to study the development of PPs in vivo and in vitro.

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)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.065
GPT teacher head0.315
Teacher spread0.250 · 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

Citations26
Published2018
Admission routes2
Has abstractyes

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