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Record W7113193256

Primer relevamiento y caracterización de los mecanismos de resistencia a fluoroquinolonas (FQ) circulantes en Latinoamérica y desarrollo de una prueba de tamizaje para la detección de sensibilidad disminuida a FQ en aislamientos de Salmonella Entérica

2024· article· es· W7113193256 on OpenAlexaboutno aff

Bibliographic record

VenueRepositorio Digital Institucional de la Universidad de Buenos Aires (Universidad de Buenos Aires) · 2024
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsDNA gyraseSalmonella entericaNalidixic acidSalmonellaCiprofloxacinFosfomycinAntibiotic resistanceSerotype
DOInot available

Abstract

fetched live from OpenAlex

Salmonella, belonging to the Enterobacterales family, is responsible for infections with high morbidity and mortality worldwide. S. enterica is classified into non-typhoidal, which causes gastroenteritis, while typhoidal, restricted to humans, can cause typhoid fever with a mortality rate of up to 30% without treatment. It is estimated that S. enterica causes approximately 100 million infections and over 200,000 deaths annually, with 85% of cases linked to the consumption of animal-derived foods.\nFluoroquinolones (FQ) are broad-spectrum antibiotics that inhibit the DNA gyrase and topoisomerase IV enzymes, essential for bacterial replication. They are effective against Gram-negative bacteria, such as Salmonella and Escherichia coli, and some Gram-positive bacteria. Their extensive use in Salmonella infections has increased resistance, leading to therapeutic failures. The cut-off points established by guidelines like CLSI do not always adequately detect resistance mechanisms, such as mutations in gyrA, parC, and the presence of qnr, underestimating resistance. This has highlighted the need to adjust the cut-off points to improve diagnostic accuracy and ensure effective treatments.\nGiven this background, this doctoral thesis aims to: (a) characterize the FQ resistance mechanisms in Salmonella spp. from different regions of Latin America and their distribution, and (b) develop a rapid and accessible screening test to detect decreased FQ susceptibility in S. enterica isolates.\nA total of 334 isolates from 16 countries in Latin America and Canada, collected between December 2012 and July 2013 through ReLAVRA, were studied. Serotypes were identified, and antimicrobial susceptibility tests were performed using standardized methodologies. Strains with inhibition zones for ciprofloxacin (CIP) ?30 mm or nalidixic acid (NAL) ?21 mm were included. Susceptibility to ciprofloxacin (CIP), nalidixic acid (NAL), norfloxacin (NOR), pefloxacin (PEF), levofloxacin (LEV), and pipemidic acid (PI) was evaluated, along with the phenotypic-genotypic correlation using MIC50, MIC90, geometric mean, and range. Genomes of isolates with discrepancies were sequenced and analyzed using open-access bioinformatics programs. The diagnostic accuracy of phenotypic tests was determined by calculating sensitivity, specificity, and predictive values using CLSI cut-off points and alternative ones where CLSI guidelines were unavailable.\nOf the 17 participating countries, 12 sent between 20 and 25 isolates, while the remaining sent between 8 and 17. The most frequent serotypes were S. Enteritidis, S. Typhimurium, and S. Typhi, representing 73% of the total sample. The analysis of quinolone resistance genes showed that 25% of the isolates exhibited a wild-type phenotype. Among the remaining 75%, gyrA mutations were the most frequent (39.5%, 132/334), followed by qnr (22%, 74/334). Three percent of the strains had gyrB mutations, and 11% showed combinations of mechanisms. MIC50 and MIC90 analyses showed that the described resistance mechanisms did not cause significant increases in MICs for FQs such as LEV and CIP, varying from moderate to low. gyrA mutations increased NAL MICs over 100-fold, while qnr caused less than a 10-fold increase, with no significant changes in CIP and LEV. The accumulation of mechanisms led to high resistance levels (MIC50 of 0.5 ?g/ml and MIC90 of 8 ?g/ml for CIP), especially for NAL (MICs >256 ?g/ml).\nThe most common amino acid change in gyrA was D87N, followed by S83F and S83Y. In gyrB, 10 isolates showed unique mutations (L451F, Q465L, S464F). Regarding Qnr proteins, qnrB (96.6%) and qnrS (3.4%) were identified. The NAL disk was the most efficient for distinguishing strains with gyrA mutations from wild-type strains. gyrB mutations affected the phenotype variably, without a specific quinolone discriminating well between them. For qnr, the NAL disk performed well but may not be recommended in regions with high prevalence of qnrS. NAL inhibition zones ?17 mm and PI ?16 mm allowed the distinction of all wild-type isolates from those with DNA gyrase mutations combined with qnrB, qnrS, oqxAB, etc. Overall, the NAL (30 ?g) disk with a cut-off point of 22 mm is the most suitable for laboratories in the Americas, given its superior performance in the region.\nThe complete genome sequencing of 17 isolates helped to understand the discrepancies between phenotype and genotype, confirming the species and serovar of each isolate. Three S. Enteritidis were identified as ST11 and seven of eight S. Typhi as ST2, while other isolates presented various STs. WGS corroborated the genotype detected by PCR and Sanger in cases with high MICs, where the phenotype was explained by the sum of mechanisms. In other cases, a mutation in gyrB (S464F) and the absence of additional mechanisms were confirmed. The qnrB19 allele was detected in isolates from four countries, indicating its wide distribution. As a finding, mcr-5 was identified in an S. Typhimurium ST19 isolate from Colombia.\nThe data obtained were shared with the CLSI Subcommittee on Susceptibility Testing, contributing to the update of screening methods and breakpoints for fluoroquinolones in Salmonella. Additionally, they had an immediate impact as ReLAVRA+ used them to improve the diagnostic strategies for AMR in Salmonella in the region, enhancing resistance detection, patient management, and strengthening One Health surveillance systems in Latin America.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0030.002
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 head0.009
GPT teacher head0.265
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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