Genomic diversity of clinically relevant bacterial pathogens from an acute care hospital in Suva, Fiji
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Objectives Antimicrobial resistance (AMR) is a global health threat, with third-generation cephalosporin–resistant (3GCR) and carbapenem-resistant infections of particular concern. There is currently a lack of genomic data on AMR organisms in the Pacific region. Methods We aimed to address this gap by examining the genetic diversity of a collection of 788 Gram-negative and Gram-positive clinical isolates collected between July 2020 and October 2022 from a single hospital in Suva, Fiji. We sampled sensitive and resistant isolates, focusing on 3GCR and carbapenem-resistant Gram-negatives, and methicillin-resistant Staphylococcus and vancomycin-resistant Enterococcus. Results We detected 29 distinct species across 12 different genera. Amongst Gram-negative genomes, Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii and Pseudomonas aeruginosa were the most common. Carbapenem resistance was mostly detected in A. baumannii ST2 and P. aeruginosa ST773, with both STs carrying NDM-1 and showing evidence of transmission within Fiji. Carbapenem resistance was relatively rare amongst the Enterobacterales; however, we observed evidence of transmission of OXA-232–carrying K. pneumoniae ST395 and NDM-7 E. coli ST410. For Gram-positive bacteria, Staphylococcus aureus ST1 was the dominant clone, and phylogenetic analysis revealed a single clade harbouring the majority of Fijian genomes, with close relationships to genomes from neighbouring Samoa. Enterococcus was relatively rare, with only 22 genomes detected. Conclusions This study provides crucial genomic data on AMR organisms in Fiji, highlighting the diversity of resistant species in the region. Local transmission of four carbapenem-resistant clones within Fiji was observed, underscoring the importance of local spread of these resistant strains.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it