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Transcytosis of insulin across microvascular endothelium

2013· article· en· W347943781 on OpenAlexaff
Paymon Azizi, Changsen Wang, Susan Armstrong, Amira Klip, Warren L. Lee

Bibliographic record

VenueThe FASEB Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsSt. Michael's HospitalHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsTranscytosisParacellular transportTranscellularInsulinEndotheliumCell biologyInternal medicineEndocrinologyBiologyChemistryMedicineEndocytosisBiochemistryPermeability (electromagnetism)ReceptorMembrane

Abstract

fetched live from OpenAlex

Circulating plasma insulin concentrations are higher than those of the interstitium, suggesting that transport across the microvascular endothelial layer is the rate‐limiting step. It is hypothesized that the metabolic syndrome, characterized by insulin resistance, is influenced by alterations in the trans‐endothelial transport of insulin. However, little is known about how insulin crosses the microvascular endothelium, although in principle it could occur via a paracellular or transcellular route. To date, technical limitations have limited the study of insulin transcytosis. Here we show the development of transwell and total internal reflectance fluorescence microscopy (TIRFM) assays to quantify transcytosis of insulin across endothelial monolayers. The transwell assay measures only the transcellular permeability of insulin and TIRFM measures the fusion of insulin‐containing vesicles at the basal membrane; neither assay is affected by paracellular leak. Using these complementary methods, we show that insulin transcytosis across tissue microvessels is a receptor‐mediated process. Further, that microtubule depolymerization decreases insulin transcytosis. In conclusion, we have developed methods for quantifying the transcytosis of insulin across the microvascular endothelium. We will thus discuss insulin transcytosis under conditions mimicking the metabolic syndrome.

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 score0.039
Threshold uncertainty score0.628

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)0.0010.000

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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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