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Record W2526301738 · doi:10.11159/icnfa16.109

pH-Mediated Release in a Model Drug Delivery System

2016· article· en· W2526301738 on OpenAlexvenueno aff
Marianne Robison, Mary R. Warmin, Clifford E. Larrabee

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsnot available
FundersUniversity of Cincinnati
KeywordsDrug deliveryDrugComputer scienceChemistryPharmacologyNanotechnologyMaterials scienceMedicine

Abstract

fetched live from OpenAlex

Targeted drug delivery systems protect healthy tissue in the patient's body while carrying the therapeutic agent to the diseased tissue.For cancer therapy, the enhanced permeability and retention (EPR) effect takes advantage of porosity of the tumor blood vessels and negligible lymphatic drainage.Nanoparticle drug carriers will accumulate in tumors without external intervention.The general problem is how best to release the drug once it has reached the targeted area.Here we show a model micellar drug delivery system that is stable and secure at a normal blood pH of 7.4, but disperses and releases its substrate as the pH drops below 7.0.The micelle is formed from the ionic surfactant, 10-undecenoate, with the hydrophobic tetrabutylammonium counterion.The pH-dependent structure and function differ markedly from other micellar solutions and suggests a key role of the counterion in both stability and solubilization power of the micelle.Our results indicate that tetrabutylammonium counterions can bring about the pH-mediated release of therapeutic agents from micellar drug delivery systems.

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 score0.004

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on New TechnologiesSame topicNanoparticle-Based Drug DeliveryFrench-language works237,207