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Record W3006599309 · doi:10.1002/chem.202000773

Smart Protein‐Based Formulation of Dendritic Mesoporous Silica Nanoparticles: Toward Oral Delivery of Insulin

2020· article· en· W3006599309 on OpenAlexaff
Estelle Juère, Romain Caillard, Doris Marko, Giorgia Del Favero, Freddy Kleitz

Bibliographic record

VenueChemistry - A European Journal · 2020
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsCégep de Lévis
FundersUniversität Wien
KeywordsInsulinMesoporous silicaChemistryEndocytosisPermeationNanoparticleMesoporous materialNanotechnologyMaterials scienceInternal medicineBiochemistryMedicineMembrane

Abstract

fetched live from OpenAlex

Oral insulin administration still represents a paramount quest that almost a century of continuous research attempts did not suffice to fulfill. Before pre-clinical development, oral insulin products have first to be optimized in terms of encapsulation efficiency, protection against proteolysis, and intestinal permeation ability. With the use of dendritic mesoporous silica nanoparticles (DMSNs) as an insulin host and together with a protein-based excipient, succinylated β-lactoglobulin (BL), pH-responsive tablets permitted the shielding of insulin from early release/degradation in the stomach and mediated insulin permeation across the intestinal cellular membrane. Following an original in vitro cellular assay based on insulin starvation, direct cellular fluorescent visualization has evidenced how DMSNs could ensure the intestinal cellular transport of insulin.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.115
GPT teacher head0.354
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations47
Published2020
Admission routes1
Has abstractyes

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