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Record W1981111109 · doi:10.1007/s12274-009-9049-0

Carbon nanotubes inhibit the hemolytic activity of the pore-forming toxin pyolysin

2009· article· en· W1981111109 on OpenAlexafffund
Zhengding Su, Himadri Mandal, Xu Jin, Xiaowu Tang

Bibliographic record

VenueNano Research · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsHemolysisCircular dichroismCarbon nanotubeChemistryBiocompatibilityBiophysicsHeLaCovalent bondMaterials scienceNanotechnologyBiochemistryOrganic chemistryCellBiology

Abstract

fetched live from OpenAlex

Functionalized carbon nanotubes have already demonstrated great biocompatibility and potential for drug delivery. We have synthesized acid oxidized and non-covalently PEGlyated single-walled carbon nanotubes (SWNTs), which were previously prepared for drug delivery purposes, and explored their potential for detoxification in the bloodstream. Our investigations of the binding of SWNTs to a pore-forming toxin pyolysin show that SWNTs prevented toxin-induced pore formation in the cell membrane of human red blood cells. Quantitative hemolysis assay and scanning electron microscopy were used to evaluate the inhibition of hemolytic activity of pyolysin. According to Raman spectroscopy data, human red blood cells, unlike HeLa cells, did not internalize oxidized SWNTs. Molecular modeling and circular dichroism measurements were used to predict the 3-D structure of pyolysin (domain 4) and its interaction with SWNTs. The tryptophan-rich hydrophobic motif in the membrane-binding domain of pyolysin, a common construct in a large family of cholesterol-dependent cytolysins, shows high affinity for SWNTs.

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 score0.002

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 head0.028
GPT teacher head0.326
Teacher spread0.298 · 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

Citations18
Published2009
Admission routes2
Has abstractyes

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