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Record W2163990653 · doi:10.1093/jac/dki281

Global distribution of TEM-1 and ROB-1 β-lactamases in Haemophilus influenzae

2005· article· en· W2163990653 on OpenAlexaboutno aff
David J. Farrell, Ian Morrissey, Sarah Bakker, Sylvie Buckridge, D. Felmingham

Bibliographic record

VenueJournal of Antimicrobial Chemotherapy · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsnot available
Fundersnot available
KeywordsHaemophilus influenzaeBroth microdilutionMicrobiologyCefaclorAntibioticsBiologyMedicineCephalosporinMinimum inhibitory concentration

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the global distribution of TEM-1 and ROB-1 beta-lactamases in Haemophilus influenzae isolated from patients with community-acquired respiratory tract infection during the first 4 years of the PROTEKT study (1999-2003). To investigate the activities of commonly used antibiotics against these isolates. METHODS: For 14 870 H. influenzae, MIC testing was performed using NCCLS broth microdilution methodology. For 2225 beta-lactamase-positive (BLP) H. influenzae, TEM-1 and ROB-1 genes were detected using a Taqman PCR method. RESULTS: beta-Lactamase positivity was 15.0% overall but varied greatly by country (<5% in several countries to 67.9% in Taiwan). Prevalences of TEM-1 and ROB-1 BLP H. influenzae were 93.7% and 4.6%, respectively, however almost all ROB-1 isolates were found in Canada, the USA and Mexico. ROB-1 isolates (n = 102) were less susceptible against cefaclor (29.4% versus 87.6%) and cefprozil (42.2% versus 91.9%) than TEM-1 (n = 2085) isolates. Differences in susceptibility rates for chloramphenicol, co-trimoxazole and tetracycline were also found between the two groups. CONCLUSIONS: The ROB-1 beta-lactamase was found almost exclusively in North America and was more active against cefaclor and cefprozil than the TEM-1 beta-lactamase.

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 score0.176
Threshold uncertainty score0.448

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 head0.006
GPT teacher head0.251
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

Citations77
Published2005
Admission routes1
Has abstractyes

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