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PEGylation prevents bacteria‐induced platelet activation and biofilm formation in platelet concentrates

2010· article· en· W1602764125 on OpenAlexaff
Carey Greco, Elisabeth Maurer‐Spurej, Mark D. Scott, M. Kaláb, Nobu Nakane, Sandra Ramírez‐Arcos

Bibliographic record

VenueVox Sanguinis · 2010
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of British ColumbiaCanadian Blood Services
Fundersnot available
KeywordsBiofilmStaphylococcus epidermidisPEGylationPlateletBacteriaChemistryMicrobiologyContaminationPlatelet activationStaphylococcus aureusBiochemistryPolyethylene glycolImmunologyBiology

Abstract

fetched live from OpenAlex

Bacterial contamination of platelet concentrates represents the greatest post-transfusion infectious risk. Biofilm formation in this environment resulting from platelet-bacteria interactions can lead to non-uniform contaminant distribution and thus missed detection. As formation of platelet-bacteria aggregates is largely based on receptor-ligand interactions, we examined whether shielding these events would result in reduced biofilm formation by contaminant bacteria. We introduced methoxypoly(ethylene glycol) to covalently modify the platelet surface using a process termed 'PEGylation'. In the first study of its kind, we demonstrate that PEGylated platelet concentrates inoculated with Staphylococcus epidermidis display a significant reduction in bacterial binding and biofilm formation.

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 score0.251
Threshold uncertainty score0.411

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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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

Citations15
Published2010
Admission routes1
Has abstractyes

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