MétaCan
Menu
Back to cohort
Record W2140551754 · doi:10.3109/07853890108998750

Therapeutic potential of the intestinotropic hormone, glucagon-like peptide-2

2001· review· en· W2140551754 on OpenAlexaff
Marie-Claude L’Heureux, Patricia L. Brubaker

Bibliographic record

VenueAnnals of Medicine · 2001
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGlucagon-like peptide-2Short bowel syndromeIntestinal epitheliumGastrointestinal hormoneHormoneTherapeutic approachEnterocyteMedicineInternal medicineIntestinal DisorderIntestinal mucosaAtrophyEndocrinologyBiologyPeptide hormoneEpitheliumParenteral nutritionPeptidePathologySmall intestineDiseaseBiochemistry

Abstract

fetched live from OpenAlex

Glucagon-like peptide-2 (GLP-2) is a newly discovered growth factor that has been demonstrated to enhance intestinal growth and function in normal rodents and to prevent damage and facilitate intestinal repair in various animal models of intestinal insufficiency. A recent study has demonstrated that GLP-2 also acts as an intestinotropin in humans with short-bowel syndrome. The high degree of specificity of GLP-2 for induction of intestinal growth, without affecting growth of other peripheral tissues, is determined by the highly localized expression of the GLP-2 receptor in the intestinal epithelium. In this article, we review the regulation of GLP-2 in physiology, from synthesis to metabolism, with a particular emphasis on potential targets in this pathway for therapeutic manipulation of GLP-2 actions. We also discuss the various animal models of intestinal insufficiency that have been used to demonstrate the therapeutic potential of this intestinotropic hormone, including short bowel, intestinal atrophy, enteritis and colitis. The results of these studies indicate that GLP-2 is a promising therapeutic agent for the treatment of various forms of intestinal insufficiency in humans.

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 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 score0.954
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
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 head0.108
GPT teacher head0.371
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

Citations20
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of MedicineSame topicDiabetes Treatment and ManagementFrench-language works237,207