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Record W2884339835 · doi:10.1007/s10238-018-0519-0

Dipeptidyl peptidase-4(DPP-4) inhibitors: promising new agents for autoimmune diabetes

2018· review· en· W2884339835 on OpenAlexaff
Xia Wang, Peilin Zheng, Gan Huang, Lin Yang, Zhiguang Zhou

Bibliographic record

VenueClinical and Experimental Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMinistry of Education and Child Care
FundersNational Science and Technology Infrastructure ProgramNational Key Research and Development Program of ChinaXiangya Hospital, Central South UniversityCentral South UniversityNational Natural Science Foundation of China
KeywordsMedicineDiabetes mellitusDipeptidyl peptidase-4Dipeptidyl peptidaseAlogliptinPharmacologyType 1 diabetesClinical trialHematologyIn vivoAutoimmune diabetesType 2 diabetesImmunologyInternal medicineEndocrinologyBiologyEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Dipeptidyl peptidase-4 (DPP-4) inhibitors constitute a novel class of anti-diabetic agents confirmed to improve glycemic control and preserve β-cell function in type 2 diabetes. Three major large-scale studies, EXAMINE, SAVOR-TIMI 53, and TECOS, have confirmed the cardiovascular safety profile of DPP-4 inhibitors. Based on these results, DPP-4 inhibitors have gained widespread use in type 2 diabetes treatment. It is currently unknown, however, whether DPP-4 inhibitors have similar therapeutic efficacy against autoimmune diabetes. Several in vitro and in vivo studies have addressed this issue, but the results remain controversial. In this review, we summarize experimental findings and preliminary clinical trial results, and identify potentially effective immune modulation targets of DPP-4 inhibitors for autoimmune diabetes.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.144
GPT teacher head0.446
Teacher spread0.302 · 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

Citations47
Published2018
Admission routes1
Has abstractyes

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