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Record W2510677199 · doi:10.1111/trf.13756

Effect of texture of platelet bags on bacterial and platelet adhesion

2016· article· en· W2510677199 on OpenAlexafffund
Narges Hadjesfandiari, Peter Schubert, Salma Fallah Toosi, Zhongming Chen, Brankica Culibrk, Sandra Ramírez‐Arcos, Dana V. Devine, Donald E. Brooks

Bibliographic record

VenueTransfusion · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsCanadian Blood ServicesUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanadian Blood Services
KeywordsAdhesionBiofilmStaphylococcus epidermidisPlateletPlatelet adhesionChemistryBacteriaPlatelet adhesivenessMicrobiologyBiophysicsMaterials scienceStaphylococcus aureusPlatelet aggregationComposite materialBiologyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Missed detection of Staphylococcus epidermidis contamination in platelet (PLT) storage bags by the standard 24-hour-postcollection BacT/ALERT screening test has been documented. A slow growth rate and the strong tendency of this bacterium to adhere to surfaces can contribute to missed detection of the pathogen. STUDY DESIGN AND METHODS: Topography of two different PLT storage bag surfaces, textured (rough) and smooth surfaces of Terumo 80440 bags (designated A15), was studied. Adhesion of biofilm-positive and -negative S. epidermidis strains on these surfaces was evaluated under static conditions. Quality of stored PLTs in A15 bags under blood bank conditions was compared for two different bag orientations (rough vs. smooth surface down) on Days 2, 5, and 7 of storage. PLT adhesion on the surfaces was evaluated after 7 days of storage. RESULTS: Bacterial adhesion and biofilm formation were significantly higher on the rough surfaces of A15 bags compared to the smooth surfaces. After 7 days of storage in A15 bags, PLTs showed similar metabolite levels, pH, and response capacity in the bags with different orientation and more PLT adhesion and aggregation was observed on rough surfaces. CONCLUSION: Higher bacterial adhesion on rough surfaces can contribute to missed detection of bacterial strains that tend to adhere on surfaces. PLT adhesion and aggregation on rough surfaces can affect the quality and safety of PLTs by promoting more bacterial adhesion and biofilm formation on surfaces.

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 score0.270
Threshold uncertainty score0.589

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)0.0010.000

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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

Citations16
Published2016
Admission routes2
Has abstractyes

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