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Genetic determinants of antibiotic resistance in enterobacteria isolated during microbiological monitoring in the perinatal center

2023· article· en· W4386967256 on OpenAlexaff
A. V. Ustyuzhanin, G. N. Chistyakova, И. И. Ремизова, A. A. Makhanyok

Bibliographic record

VenueEpidemiology and Vaccinal Prevention · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsAntibiotic resistanceAntibioticsChristian ministryBiologyPopulationClinical significanceDrug resistanceMicrobiologyMedicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Relevance. Currently, studies of the prevalence of antibiotic resistance and its genetic characteristics are focused primarily on the adult population, although infection with multiple drug infection has been registered as etiological agents of a general infection in obstetric and gynecological and pediatric institutions. The study of the prevalence of genetic determinants of antibiotic resistance is an important area of scientific research. Aim. To analyze the results of the studies carried out to identify the genetic determinants of antibiotic resistance of enterobacteria isolated during microbiological monitoring in the perinatal center. Materials and methods. The genetic profile of antibiotic resistance was studied in ESBL-producing strains isolated from 45 women and 35 children examined at the departments of the Federal State Budgetary Institution «NII OMM» of the Ministry of Health of Russia. To determine the determinants of antibiotic resistance, 80 non-duplicate strains of 7 species of the Enterobacteriaceae family were studied. DNA of bacterial cells was isolated from a daily culture of microorganisms using the PROBA-NK kit, detection of the tem, ctx-M-1, shv genes; oxa-40-like, oxa-48-like, oxa-23-like, oxa-51-like, imp, kpc, ges, ndm, vim were carried out using the diagnostic kit «BacResista GLA» on the detecting amplifier DT-48 (DNA -technology, Russia). To assess the statistical significance of differences in the frequency of occurrence of genes, Pearson's c2 test with Yates' correction was used. Results and discussion. When analyzing the results of studies on the molecular genetic detection of antibiotic resistance determinants, which we conducted in 2022, it was found that 8 genovariants were found in bacterial strains isolated from patients of the departments of the Research Institute of OMM in Yekaterinburg, providing resistance to beta-lactam antibiotics. The dominant genome, as in 2021, remains blaCTX-M-1, found in 29 cases. The blaTEM gene was identified both in association with other genes and as a single variant in Escherichiae coli and Klebsiella pneumoniae strains. Of the eight strains of K. pneumoniae, 4 were found to have three antibiotic resistance genes blaCTX-M, blaTEM, blaSHV, strains with a genetic profile of blaCTX-M, blaTEM, blaSHV, blaNDM were isolated once; and blaTEM, blaSHV, blaKPC. In one strain of K. pneumoniae, phenotypically showing resistance to AB, no genetic determinants of AB resistance were found. In addition to resistance to beta-lactam antibiotics, the strains demonstrate resistance to such groups of antibacterial drugs as fluoroquinolones, phosphonic acid derivatives (fosfomycin), and aminoglycosides. The data obtained indicate that the intestines of newborns during their stay at the stationary stage of nursing in some cases are colonized by strains of enterobacteria with multidrug resistance. Consequently, children are a reservoir of resistant microorganisms and can be sources of pathogens of infectious diseases in families and children's organized groups.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.317
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations2
Published2023
Admission routes1
Has abstractyes

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