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Record W3159099664 · doi:10.2337/dbi20-0034

Altered β-Cell Prohormone Processing and Secretion in Type 1 Diabetes

2021· review· en· W3159099664 on OpenAlexafffund
Teresa Rodríguez-Calvo, Yi‐Chun Chen, C. Bruce Verchere, Leena Haataja, Peter Arvan, Pia Leete, Sarah J. Richardson, Noel G. Morgan, Weijun Qian, Alberto Pugliese, Mark A. Atkinson, Carmella Evans‐Molina, Emily K. Sims

Bibliographic record

VenueDiabetes · 2021
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesJuvenile Diabetes Research Foundation United KingdomLeona M. and Harry B. Helmsley Charitable TrustCanadian Institutes of Health ResearchU.S. Department of Veterans Affairs
KeywordsProhormoneType 2 diabetesMedicineDiabetes mellitusInternal medicineBioinformaticsEndocrinologyBiologyHormone

Abstract

fetched live from OpenAlex

Analysis of data from clinical cohorts, and more recently from human pancreatic tissue, indicates that reduced prohormone processing is an early and persistent finding in type 1 diabetes. In this article, we review the current state of knowledge regarding alterations in islet prohormone expression and processing in type 1 diabetes and consider the clinical impact of these findings. Lingering questions, including pathologic etiologies and consequences of altered prohormone expression and secretion in type 1 diabetes, and the natural history of circulating prohormone production in health and disease, are considered. Finally, key next steps required to move forward in this area are outlined, including longitudinal testing of relevant clinical populations, studies that probe the genetics of altered prohormone processing, the need for combined functional and histologic testing of human pancreatic tissues, continued interrogation of the intersection between prohormone processing and autoimmunity, and optimal approaches for analysis. Successful resolution of these questions may offer the potential to use altered prohormone processing as a biomarker to inform therapeutic strategies aimed at personalized intervention during the natural history of type 1 diabetes and as a pathogenic anchor for identification of potential disease-specific endotypes.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.036
GPT teacher head0.300
Teacher spread0.264 · 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 designOther design
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

Citations55
Published2021
Admission routes2
Has abstractyes

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