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Record W4324088613 · doi:10.3389/fendo.2023.1128622

Regulation of the immune system by the insulin receptor in health and disease

2023· review· en· W4324088613 on OpenAlexaff
Priya Makhijani, Paulo José Basso, Yi Tao Chan, Nan Chen, Jordan J. Baechle, Saad Khan, David Furman, Sue Tsai, Daniel A. Winer

Bibliographic record

VenueFrontiers in Endocrinology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsUniversity Health NetworkToronto General HospitalUniversity of AlbertaUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on AgingNational Institutes of HealthBuck Institute for Research on Aging
KeywordsImmune systemInsulin receptorBiologySignal transductionCell metabolismCell signalingInsulinEffectorNutrient sensingCell biologyReceptorImmunologyCellInsulin resistanceEndocrinologyGenetics

Abstract

fetched live from OpenAlex

The signaling pathways downstream of the insulin receptor (InsR) are some of the most evolutionarily conserved pathways that regulate organism longevity and metabolism. InsR signaling is well characterized in metabolic tissues, such as liver, muscle, and fat, actively orchestrating cellular processes, including growth, survival, and nutrient metabolism. However, cells of the immune system also express the InsR and downstream signaling machinery, and there is increasing appreciation for the involvement of InsR signaling in shaping the immune response. Here, we summarize current understanding of InsR signaling pathways in different immune cell subsets and their impact on cellular metabolism, differentiation, and effector versus regulatory function. We also discuss mechanistic links between altered InsR signaling and immune dysfunction in various disease settings and conditions, with a focus on age related conditions, such as type 2 diabetes, cancer and infection vulnerability.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.257
Teacher spread0.247 · 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
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

Citations61
Published2023
Admission routes1
Has abstractyes

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