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Record W2395835435 · doi:10.1039/c6tb00529b

An amphipathic lytic peptide for enhanced and selective delivery of ellipticine

2016· article· en· W2395835435 on OpenAlexaff
Sheng Lu, Yong Ding, Yan Wu, Rong Wang, Pan Ran, Zizhen Wan, Wen Xu, Lei Zhang, Yongfang Yuan, P. Chen

Bibliographic record

VenueJournal of Materials Chemistry B · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersShanghai Municipal Education Commission
KeywordsLytic cyclePeptideCationic polymerizationAmphiphileDrug deliveryChemistryBiophysicsNanotechnologyBiochemistryMaterials scienceVirologyBiologyVirusPolymer chemistry

Abstract

fetched live from OpenAlex

Cationic lytic peptides (CLPs) have shown promise in treating bacterial infection and cancer via selective disruption of bacterial or cancer cell membranes. In this work, we used a CLP, C6, as a nanocarrier for a hydrophobic anticancer agent, ellipticine (EPT). The size of the resulting C6-EPT complex was ∼190 nm. The in vitro studies using A549 lung cancer cells showed an enhanced anticancer activity of the C6-EPT complex compared to that of C6 or the EPT control. This enhancement was found to correlate with the membrane disruption induced by C6, which facilitated the entry of EPT into cells. More importantly, the C6-EPT complex showed a higher selectivity than that of C6 towards cancer cells upon comparison of their cytotoxicities against A549 cells and NIH-3T3 fibroblast cells. The enhanced therapeutic activity was also found in in vivo studies using an A549 tumor-bearing BALB/c nude mice model. This study provides a new CLP strategy for the development of multifunctional 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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.008
GPT teacher head0.227
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

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

Citations6
Published2016
Admission routes1
Has abstractyes

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