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Record W4307711857 · doi:10.1139/cjc-2022-0075

Preparation of pelargonic acid vesicles and sustained drug release

2022· article· en· W4307711857 on OpenAlexvenueno aff
Zhimei Wang, Yuehuan Zhang, Qing Chen, Xin Xiao, Carl Redshaw

Bibliographic record

VenueCanadian Journal of Chemistry · 2022
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsChemistryVesicleAqueous solutionChromatographyHydrochlorideOrganic chemistryBiochemistryMembrane

Abstract

fetched live from OpenAlex

This study has selected the medium-chain saturated fatty acid pelargonic acid as the raw material for the first time, and added the surface catalyst Tween 80 to the aqueous solution to form pelargonic acid–Tween 80 composite vesicles by a surface tension method. The compound vesicle was used to encapsulate five heterocyclic drugs (cefazolin sodium, chlorpromazine hydrochloride, gemcitabine hydrochloride, metronidazole, and isoniazid), all of which have disadvantages when used directly, and the effects of the vesicles on the five kinds of drugs were evaluated in terms of encapsulation rate and the in vitro sustained release in a simulated artificial intestinal fluid environment. The results revealed that the pelargonic acid–Tween 80 composite vesicles exhibit an encapsulation efficiency above 45%, and that the encapsulated drugs achieve a cumulative release effect of 6–10 h in a simulated artificial intestinal fluid environment, and the cumulative release rate is above 35%. This study proves that pelargonic acid–Tween 80 composite vesicles can be used as a carrier for encapsulated drugs, and this can extend the time of action of the drugs. This research illustrates the potential for application of pelargonic acid in the field of medicine.

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 categoriesInsufficient payload (model declined to judge)
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 score0.027
Threshold uncertainty score0.999

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)0.0020.000

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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.

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
Published2022
Admission routes1
Has abstractyes

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