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Record W3034676265 · doi:10.1038/s41467-020-16772-x

Blood circulation of soft nanomaterials is governed by dynamic remodeling of protein opsonins at nano-biointerface

2020· article· en· W3034676265 on OpenAlexafffund
Srinivas Abbina, Lily E. Takeuchi, Parambath Anilkumar, Kai Yu, Jason C. Rogalski, Rajesh A. Shenoi, Iren Constantinescu, Jayachandran N. Kizhakkedathu

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of British Columbia HospitalCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGenome CanadaCanadian Institutes of Health ResearchGenome British ColumbiaMichael Smith Health Research BCGovernment of Canada
KeywordsNanomaterialsOpsoninSystemic circulationChemistryBiointerfaceIn vivoBiophysicsNanotechnologyMaterials scienceMedicineBiochemistryBiologyInternal medicineIn vitro

Abstract

fetched live from OpenAlex

Abstract Nanomaterials in the blood must mitigate the immune response to have a prolonged vascular residency in vivo. The composition of the protein corona that forms at the nano-biointerface may be directing this, however, the possible correlation of corona composition with blood residency is currently unknown. Here‚ we report a panel of new soft single molecule polymer nanomaterials (SMPNs) with varying circulation times in mice (t 1/2β ~ 22 to 65 h) and use proteomics to probe protein corona at the nano-biointerface to elucidate the mechanism of blood residency of nanomaterials. The composition of the protein opsonins on SMPNs is qualitatively and quantitatively dynamic with time in circulation. SMPNs that circulate longer are able to clear some of the initial surface-bound common opsonins, including immunoglobulins, complement, and coagulation proteins. This continuous remodelling of protein opsonins may be an important decisive step in directing elimination or residence of soft nanomaterials in vivo.

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 score0.013
Threshold uncertainty score0.704

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 science0.0010.001
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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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

Citations93
Published2020
Admission routes2
Has abstractyes

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