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Record W4213422518 · doi:10.1021/acsabm.1c01210

Blood Compatibility of Hydrophilic Polyphosphoesters

2022· article· en· W4213422518 on OpenAlexafffund
Chiara Pelosi, Iren Constantinescu, Helena H. Son, Maria Rosaria Tinè, Jayachandran N. Kizhakkedathu, Frederik R. Wurm

Bibliographic record

VenueACS Applied Bio Materials · 2022
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchDeutsche Forschungsgemeinschaft
KeywordsNanocarriersEthylene glycolPolymerBiocompatible materialMaterials scienceCopolymerNanomedicineChemistryNanotechnologyBiomedical engineeringDrug deliveryNanoparticleOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Polyphosphoesters (PPEs) are a class of versatile degradable polymers. Despite the high potential of this class of polymers in biomedical applications, little is known about their blood interaction and compatibility. We evaluated the hemocompatibility of water-soluble PPEs (with different hydrophilicities and molar masses) and PPE-coated model nanocarriers. Overall, we identified high hemocompatibility of PPEs, comparable to poly(ethylene glycol) (PEG), currently used for many applications in nanomedicine. Hydrophilic PPEs caused no significant changes in blood coagulation, negligible platelet activation, the absence of red blood cells lysis, or aggregation. However, when a more hydrophobic copolymer was studied, some changes in the whole blood clot strength at the highest concentration were detected, but only concentrations above that are typically used for biomedical applications. Also, the PPE-coated model nanocarriers showed high hemocompatibility. These results contribute to defining hydrophilic PPEs as a promising platform for degradable and biocompatible materials in the biomedical field.

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)0.001
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
Metaresearch0.0000.001
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 head0.017
GPT teacher head0.205
Teacher spread0.189 · 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

Citations16
Published2022
Admission routes2
Has abstractyes

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