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Record W4388457940 · doi:10.15252/emmm.202317928

Paired box 6 gene delivery preserves beta cells and improves islet transplantation efficacy

2023· article· en· W4388457940 on OpenAlexaff
Wing Yan So, Yilie Liao, Wai Nam Liu, Guy A. Rutter, Weiping Han

Bibliographic record

VenueEMBO Molecular Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersBiomedical Research CouncilNational Medical Research CouncilMedical Research CouncilDiabetes UKAgency for Science, Technology and ResearchWellcome Trust
KeywordsPAX6IsletBeta cellTransplantationPDX1Glucose homeostasisBiologyDiabetes mellitusCancer researchBETA (programming language)EndocrinologyInternal medicineCell biologyMedicineImmunologyGeneInsulin resistanceGenetics

Abstract

fetched live from OpenAlex

Loss of pancreatic beta cells is the central feature of all forms of diabetes. Current therapies fail to halt the declined beta cell mass. Thus, strategies to preserve beta cells are imperatively needed. In this study, we identified paired box 6 (PAX6) as a critical regulator of beta cell survival. Under diabetic conditions, the human beta cell line EndoC-βH1, db/db mouse and human islets displayed dampened insulin and incretin signalings and reduced beta cell survival, which were alleviated by PAX6 overexpression. Adeno-associated virus (AAV)-mediated PAX6 overexpression in beta cells of streptozotocin-induced diabetic mice and db/db mice led to a sustained maintenance of glucose homeostasis. AAV-PAX6 transduction in human islets reduced islet graft loss and improved glycemic control after transplantation into immunodeficient diabetic mice. Our study highlights a previously unappreciated role for PAX6 in beta cell survival and raises the possibility that ex vivo PAX6 gene transfer into islets prior to transplantation might enhance islet graft function and transplantation outcome.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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 designBench or experimental
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

Citations7
Published2023
Admission routes1
Has abstractyes

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