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Record W2300010261 · doi:10.1111/jdi.12479

Engineering the gut for insulin replacement to treat diabetes

2016· review· en· W2300010261 on OpenAlexaff
Majid Mojibian, Maria M. Glavas, Timothy J. Kieffer

Bibliographic record

VenueJournal of Diabetes Investigation · 2016
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEnteroendocrine cellDiabetes mellitusMedicineInsulinPopulationEndocrine systemBioinformaticsHormoneCellEndocrinologyBiology

Abstract

fetched live from OpenAlex

The gut epithelium's large surface area, its direct exposure to ingested nutrients, its vast stem cell population and its immunotolerogenic environment make it an excellent candidate for therapeutic cells to treat diabetes. Thus, several attempts have been made to coax immature gut cells to differentiate into insulin-producing cells by altering the expression patterns of specific transcription factors. Furthermore, because of similarities in enteroendocrine and pancreatic endocrine cell differentiation pathways, other approaches have used genetically engineered enteroendocrine cells to produce insulin in addition to their endogenous secreted hormones. Several studies support the utility of both of these approaches for the treatment of diabetes. Converting a patient's own gut cells into meal-regulated insulin factories in a safe and immunotolerogenic environment is an attractive approach to treat and potentially cure diabetes. Here, we review work on these approaches and indicate where we feel further advancements are required.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.940
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.302
Teacher spread0.263 · 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.

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

Citations10
Published2016
Admission routes1
Has abstractyes

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