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Record W6887744679 · doi:10.17863/cam.114417

Smart control lipid-based nanocarriers for fine-tuning gut hormone secretion.

2024· article· en· W6887744679 on OpenAlexfundno aff

Bibliographic record

VenueApollo (University of Cambridge) · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsnot available
FundersUniversité Catholique de LouvainCanadian Institutes of Health ResearchWallonie-Bruxelles InternationalMedical Research CouncilFundamental Research Funds for the Central UniversitiesNovo NordiskWellcome TrustDepartment of Science and Technology of Sichuan ProvinceBanting and Best Diabetes Centre, University of TorontoFonds De La Recherche Scientifique - FNRSEuropean CommissionNational Natural Science Foundation of China
KeywordsEnteroendocrine cellNanocarriersHormoneEndogenyGastrointestinal hormoneKnockout mouseEndocrine systemGlucagon-like peptide-1

Abstract

fetched live from OpenAlex

Modulating the endogenous stores of gastrointestinal hormones is considered a promising strategy to mimic gut endocrine function, improving metabolic dysfunction. Here, we exploit mouse and human knock-in and knockout intestinal organoids and show that agents used as commercial lipid excipients can activate nutrient-sensitive receptors on enteroendocrine cells (EECs) and, when formulated as lipid nanocarriers, can bestow biological effects through the release of GLP-1, GIP, and PYY from K and L cells. Studies in wild-type, dysglycemic, and gut Gcg knockout mice demonstrated that the effect exerted by lipid nanocarriers could be modulated by varying the excipients (e.g., nature and quantities), the formulation methodology, and their physiochemical properties (e.g., size and composition). This study demonstrates the therapeutic potential of using nanotechnology to modulate release of multiple endogenous hormones from the enteroendocrine system through a patient-friendly, inexpensive, and noninvasive manner.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.660

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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
Published2024
Admission routes1
Has abstractyes

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