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Record W4388648455 · doi:10.3803/enm.2023.1738

Partial Deletion of Perk Improved High-Fat Diet-Induced Glucose Intolerance in Mice

2023· article· en· W4388648455 on OpenAlexfundno aff
Jooyeop Lee, Min Joo Kim, Seoil Moon, Ji Yoon Lim, Kyong Soo Park, Hye Seung Jung

Bibliographic record

VenueEndocrinology and Metabolism · 2023
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
FundersMinistry of Science and ICT, South KoreaSeoul National University HospitalNational Research Foundation of KoreaSeoul National UniversityUniversity of Ottawa
KeywordsInternal medicineEndocrinologyIsletInsulinMedicineType 2 diabetesDiabetes mellitus

Abstract

fetched live from OpenAlex

Although pancreatic endoplasmic reticulum kinase (PERK) is indispensable to beta cells, low-dose PERK inhibitor improved glucose- stimulated insulin secretion (GSIS) and hyperglycemia in diabetic mice. Current study examined if partial deletion of Perk (Perk+/-) recapitulated the effects of PERK inhibitor, on the contrary to the complete deletion. Perk+/- mice and wild-type controls were fed with a high-fat diet (HFD) for 23 weeks. Glucose tolerance was evaluated along with serum insulin levels and islet morphology. Perk+/- mice on normal chow were comparable to wild-type mice in various metabolic features. HFD-induced obesity was not influenced by Perk reduction; however, HFD-induced glucose intolerance was significantly improved since 15-week HFD. HFD-induced compromises in GSIS were relieved by Perk reduction, accompanied by reductions in phosphorylated PERK and activating transcription factor 4 (ATF4) in the islets. Meanwhile, HFD-induced islet expansion was not significantly affected. In summary, partial deletion of Perk improved glucose tolerance and GSIS impaired by diet-induced obesity, without changes in body weights or islet mass.

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 score0.373
Threshold uncertainty score0.399

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 head0.017
GPT teacher head0.264
Teacher spread0.248 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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