MétaCan
Menu
Back to cohort
Record W4411549971 · doi:10.1039/d4na01085j

Protein corona composition modulates uptake of polymeric micelles by colorectal cancer cells

2025· article· en· W4411549971 on OpenAlexafffund
Munira Sirazum, Ahmed Abdelfattah, Prashant Pandey, Aliakbar Ashkarran, Soheyl Tadjiki, Shahriar Sharifi, Hassan Gharibi, Amir Ata Saei, Morteza Mahmoudi, Afsaneh Lavasanifar

Bibliographic record

VenueNanoscale Advances · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Alberta
FundersFaculty of Pharmacy and Pharmaceutical Sciences, University of AlbertaNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsMicelleCorona (planetary geology)Colorectal cancerChemistryBiophysicsComposition (language)Cell biologyCancerBiologyGenetics

Abstract

fetched live from OpenAlex

-poly(α-benzyl carboxylate-ε-caprolactone) (PEO-PBCL) copolymers with varying degrees of polymerization after incubation in human plasma, and explores their relationship with cellular uptake by colorectal cancer cells. Traceable block copolymers were synthesized, self-assembled into PMs (44-99 nm, slightly negative zeta potentials), and characterized. Protein coronas were formed by incubating PMs with human plasma; protein-coated micelles were separated and analyzed. Uptake of selected PMs, with and without human plasma pre-incubation, by colorectal cancer cells was assessed. PEO-PCL micelles exhibited higher cellular uptake than PEO-PBCL micelles. Human plasma significantly reduced the uptake of PEO-PCL micelles, while PEO-PBCL micelles' uptake remained low. Proteomic analysis identified 23 distinct proteins among the combined top 20 most abundant proteins from each PM corona, with 18 common across all micelle types. In the top 10 proteins, PEO-PCL micelles shared an identical profile, whereas PEO-PBCL micelles had two unique proteins not present in PEO-PCL coronas. Protein corona composition in both PMs was shown to influence their cellular uptake behavior.

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 score0.012
Threshold uncertainty score0.680

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 head0.005
GPT teacher head0.238
Teacher spread0.233 · 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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueNanoscale AdvancesSame topicNanoparticle-Based Drug DeliveryFrench-language works237,207