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Record W1906828763 · doi:10.1111/trf.12655

Antimicrobial activity in cord blood units: occurrence and levels of antibiotics

2014· article· en· W1906828763 on OpenAlexaff
Marie‐Pierre Cayer, M Girard, Diane Fournier, Gilles Delage, Louis Thibault

Bibliographic record

VenueTransfusion · 2014
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsHéma-Québec
Fundersnot available
KeywordsAntibioticsAntimicrobialCord bloodMedicineMicrobiologyImmunologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Antibiotic prophylaxis treatment at delivery is highly recommended for reducing the risk of infection for mothers positive for group B streptococcus. It is therefore expected that some cord blood (CB) products will contain residual antibiotics. This study aimed to determine the incidence and level of β-lactam antibiotics in CB products. STUDY DESIGN AND METHODS: The antimicrobial activity of 60 CB plasma by-products was evaluated using disk diffusion assays on 10 bacteria species. Plasma samples showing antimicrobial activity were either treated with β-lactamase enzyme to inhibit β-lactam antibiotics or heated to 56°C for 30 minutes to inhibit complement proteins. β-Lactam antibiotic concentrations were determined by comparison with a standard curve obtained with known concentrations of antibiotics. RESULTS: Antimicrobial activity against mostly Gram-positive microorganisms was observed in 33% of CB units. The β-lactamase enzyme abolished the antimicrobial activity in the majority of these CB products. Up to 5 μg/mL penicillin and 14 μg/mL ampicillin were measured in these products. CONCLUSION: Approximately one-third of CB products contain significant amounts of plasma with residual antibiotics, which can affect the survival and growth of bacterial contaminants when performing the sterility test and potentially lead to false-negative results. Additional work is required to better understand whether residual antibiotics in CB affect penicillin-allergic patients.

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 score0.212
Threshold uncertainty score0.201

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 head0.027
GPT teacher head0.273
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

Citations6
Published2014
Admission routes1
Has abstractyes

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