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Record W4281675432 · doi:10.1126/sciadv.abm8478

Development of oil-based gels as versatile drug delivery systems for pediatric applications

2022· article· en· W4281675432 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Toronto
FundersKoch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyPharmaceutical Research and Manufacturers of America FoundationNational Cancer InstituteBill and Melinda Gates Foundation
KeywordsDrug deliveryDrugNanotechnologyComputer scienceMedicineMaterials sciencePharmacology

Abstract

fetched live from OpenAlex

Administering medicines to 0- to 5-year-old children in a resource-limited environment requires dosage forms that circumvent swallowing solids, avoid on-field reconstitution, and are thermostable, cheap, versatile, and taste masking. We present a strategy that stands to solve this multifaceted problem. As many drugs lack adequate water solubility, our formulations used oils, whose textures could be modified with gelling agents to form "oleogels." In a clinical study, we showed that the oleogels can be formulated to be as fluid as thickened beverages and as stiff as yogurt puddings. In swine, oleogels could deliver four drugs ranging three orders of magnitude in their water solubilities and two orders of magnitude in their partition coefficients. Oleogels could be stabilized at 40°C for prolonged durations and used without redispersion. Last, we developed a macrofluidic system enabling fixed and metered dosing. We anticipate that this platform could be adopted for pediatric dosing, palliative care, and gastrointestinal disease applications.

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.

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 score0.565
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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