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Record W4229449566 · doi:10.3390/antibiotics11050635

Temporal Variation of Meropenem Resistance in E. coli Isolated from Sewage Water in Islamabad, Pakistan

2022· article· en· W4229449566 on OpenAlexfundno aff
Saba Yasmin, Asad Mustafa Karim, Sang Hee Lee, Rabaab Zahra

Bibliographic record

VenueAntibiotics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsnot available
FundersMinistry of Science and ICT, South KoreaInternational Development Research Centre
KeywordsMeropenemFosfomycinMicrobiologyBiologyEscherichia coliMultiple drug resistanceAntibiotic resistanceCefotaximeCiprofloxacinColistinSewageAmpicillinMCR-1Veterinary medicineDrug resistanceEnterobacteriaceaeAntibioticsGeneMedicineGenetics

Abstract

fetched live from OpenAlex

The WHO has classified carbapenem-resistant Enterobacteriaceae in most critical priority pathogens that pose a threat to human health. The present study investigated the prevalence of meropenem-resistant Escherichia coli (E. coli) in relation to its temporal variation in different seasons along with its resistance markers in sewage water. E. coli was selected on MacConkey agar containing meropenem (3 µg/mL). There were 27% of sites/sewage samples carrying meropenem-resistant E. coli. All E. coli were confirmed through the amplification of the uidA gene. All isolated E. coli were multidrug-resistant (MDR), and among them, 51% were extensively drug-resistant (XDR). An antibiogram determined against 15 antibiotics showed the highest resistance to ampicillin and cefotaxime (98% each) and lowest resistance to fosfomycin (2%). Phylogenetic groups and resistance gene analysis through PCR showed a significant co-occurrence of carbapenemases with extended spectrum beta lactamases (ESBLs), plasmid encoded quinolone, and colistin resistance genes. The higher number of resistance genes in E. coli isolates in community sewage indirectly indicate that these isolates circulate abundantly in the community.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.666

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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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
Published2022
Admission routes1
Has abstractyes

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