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Record W2810578166 · doi:10.1039/c8fo00784e

Influence of thermal processing on the phase behavior of a novel acylglyceride-alkylglyceride lipid-based delivery system

2018· article· en· W2810578166 on OpenAlexafffund
Pablo Arranz‐Martínez, Marta Corzo‐Martínez, Luis Vázquez, Carlos F. Torres, Alejandro G. Marangoni

Bibliographic record

VenueFood & Function · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Guelph
FundersEuropean Social FundNatural Sciences and Engineering Research Council of CanadaMinisterio de Economía y Competitividad
KeywordsPhase (matter)Delivery systemChemistryComputer scienceEngineeringBiomedical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Lipid-based delivery systems have been broadly utilized to enhance bioavailability and bioaccessibility properties of drugs. Loading capacity and drug stability within the lipid matrix is influenced by the phase behavior of the lipid components that make up the vehicle. In this study we have investigated the influence of thermal processing at 50 °C on the subsequent phase behavior of a lipid-based delivery system comprised of a blend of monoacylglycerols, diacylglycerols, monoalkylglycerols and dialkylglycerols obtained by enzymatic glycerolysis of ratfish liver oil. Differential scanning calorimetry revealed that crystallization and melting points decreased, while powder X-ray diffraction suggested changes in the crystal structure of the product when heated to 50 °C and held for longer than 180 min. Samples heated for a short time, rapidly underwent a polymorphic transition from the α to the β polymorphic phase, whereas samples heated for long times remained in the α phase for 3 days. Polarized light microscopy also showed changes in the microstructure of the material due to the thermal treatment. These results suggest that the thermal history of lipidic encapsulation materials, above their melting point, can affect eventual encapsulation efficiency and product performance.

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 score0.120
Threshold uncertainty score0.231

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 head0.041
GPT teacher head0.243
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
Published2018
Admission routes2
Has abstractyes

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