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Record W2976599586 · doi:10.1021/acsomega.9b02326

Stable, Bioresponsive, and Macrophage-Evading Polyurethane Micelles Containing an Anionic Tripeptide Chain Extender

2019· article· en· W2976599586 on OpenAlexaff
Danxuan Fang, Menghan Pi, Zhicheng Pan, Nijia Song, Xueling He, Jiehua Li, Feng Luo, Hong Tan, Zhen Li

Bibliographic record

VenueACS Omega · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsNanocarriersTripeptideChemistryMicelleMononuclear phagocyte systemBiophysicsDrug deliveryMacrophagePhagocytosisBiochemistryPeptideOrganic chemistryCell biologyIn vitroBiologyImmunology

Abstract

fetched live from OpenAlex

Polymeric nanocarriers have been extensively used in medicinal applications for drug delivery. However, intravenous nanocarriers circulating in the blood will be rapidly cleared from the mononuclear macrophage system. The surface physicochemical characterizations of nanocarriers are the primary factors to determine their fate in vivo, such as evading the reticuloendothelial system, exhibiting long blood circulation times, and accumulating in the targeted site. In this work, we develop a series of polyurethane micelles containing segments of an anionic tripeptide, hydrophilic mPEG, and disulfide bonds. It is found that the long hydrophilic mPEG can shield the micellar surface and have a synergistic effect with the negatively charged tripeptide to minimize macrophage phagocytosis. Meanwhile, the disulfide bond can rapidly respond to the intracellular reduction environment, leading to the acceleration of drug release and improvement of the therapeutic effect. Our results verify that these anionic polyurethane micelles hold great potential in the development of the stealth immune system and controllable intracellular drug transporters.

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)0.001
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 score0.005
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.245
Teacher spread0.232 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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