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Record W2304321059 · doi:10.1021/acs.langmuir.6b00511

Ferrocene-Modified Phospholipid: An Innovative Precursor for Redox-Triggered Drug Delivery Vesicles Selective to Cancer Cells

2016· article· en· W2304321059 on OpenAlexafffund
Tomer Noyhouzer, Chloé L’Homme, Isabelle Beaulieu, Stephanie Mazurkiewicz, Sabine Kuss, Heinz‐Bernhard Kraatz, Sylvain Canesi, Janine Mauzeroll

Bibliographic record

VenueLangmuir · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsThe Scarborough HospitalUniversity of TorontoUniversité du Québec à MontréalMcGill University
FundersFonds de recherche du Québec – Nature et technologiesFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Foundation for InnovationRotary Foundation
KeywordsLiposomeDrug deliveryChemistryVesicleScanning electrochemical microscopyPhospholipidFerroceneRedoxNanotechnologyCombinatorial chemistryMembraneBiophysicsElectrochemistryMaterials scienceBiochemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Controlled payload release is one of the key elements in the creation of a reliable drug delivery system. We report the discovery of a drug delivery vessel able to transport chemotherapeutic agents to target cancer cells and selectively trigger their release using the electrochemical activity of a ferrocene-modified phospholipid. Supported by in vitro assays, the competitive advantages of this discovery are (i) the simple one step scalability of the synthetic process, (ii) the stable encapsulation of toxic drugs (doxorubicin) during transport, and (iii) the selective redox triggering of the liposomes to harness their cytotoxic payload at the cancer site. Specifically, the redox-modified giant unilamellar vesicle and liposomes were characterized using advanced methods such as scanning electrochemical microscopy (SECM), transmission electron microscopy (TEM), dynamic light scattering (DLS), and fluorescent imaging.

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 score0.022
Threshold uncertainty score0.592

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 head0.013
GPT teacher head0.296
Teacher spread0.282 · 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

Citations75
Published2016
Admission routes2
Has abstractyes

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