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Record W3124795222 · doi:10.1038/s41467-020-20632-z

PDX1LOW MAFALOW β-cells contribute to islet function and insulin release

2021· article· en· W3124795222 on OpenAlexfundno aff
Daniela Nasteska, Nicholas H. F. Fine, Fiona Ashford, Federica Cuozzo, Katrina Viloria, Gabrielle Smith, Aisha Dahir, Peter W. J. Dawson, Yu‐Chiang Lai, Aimée Bastidas-Ponce, Mostafa Bakhti, Guy A. Rutter, Rémi Fiancette, Rita Nano, Lorenzo Piemonti, Heiko Lickert, Qiao Zhou, İldem Akerman, David J. Hodson

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesEast China Institute of TechnologyInnovative Medicines InitiativeEuropean CommissionMedical Research CouncilUniversity of BirminghamDiabetes UKUniversity of AlbertaWellcome Trust
KeywordsPDX1IsletBiologyInsulinCell biologyPopulationFunction (biology)EndocrinologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

Abstract Transcriptionally mature and immature β-cells co-exist within the adult islet. How such diversity contributes to insulin release remains poorly understood. Here we show that subtle differences in β-cell maturity, defined using PDX1 and MAFA expression, contribute to islet operation. Functional mapping of rodent and human islets containing proportionally more PDX1 HIGH and MAFA HIGH β-cells reveals defects in metabolism, ionic fluxes and insulin secretion. At the transcriptomic level, the presence of increased numbers of PDX1 HIGH and MAFA HIGH β-cells leads to dysregulation of gene pathways involved in metabolic processes. Using a chemogenetic disruption strategy, differences in PDX1 and MAFA expression are shown to depend on islet Ca 2+ signaling patterns. During metabolic stress, islet function can be restored by redressing the balance between PDX1 and MAFA levels across the β-cell population. Thus, preserving heterogeneity in PDX1 and MAFA expression, and more widely in β-cell maturity, might be important for the maintenance of islet function.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.277
Teacher spread0.262 · 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
GenreEmpirical

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

Citations83
Published2021
Admission routes1
Has abstractyes

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