Founding of the culture collection of antibiotic-resistant strains of zoonotic bacteria in the Russian Federation
Bibliographic record
Abstract
Background and Aim: The main purpose of a national bioresource center is to standardize, centralize, preserve, and ensure accessibility of microbial bioresources that accumulate there because of state research programs. The establishment of national bioresource centers for antibiotic-resistant microorganisms allows to solve practical problems in the field of veterinary service, as well as to develop effective chemotherapeutic and disinfectant drugs to overcome the mechanisms of resistance. This study aimed to outline the process of forming a national culture collection of antibiotic-resistant strains of zoonotic bacteria in the Russian Federation using two microbial strains. Materials and Methods: The object of research was isolates of Salmonella spp., Escherichia coli, Enterococcus spp., Campylobacter spp., Listeria monocytogenes, and Staphylococcus spp., all of which were obtained from biomaterials of farm animals, feed samples, bedding, water from livestock buildings, washouts from environmental objects, and food products. The resistance of bacterial isolates was determined using microbiological and molecular-genetic research methods. Results: During monitoring studies, 1489 bacterial isolates were isolated. In total, 408 bacterial isolates were tested for sensitivity to antimicrobial agents, including E. coli (47.6%), Salmonella spp. (30.4%), Enterococcus spp. (11.3%), and Campylobacter spp. (10.8%). For genetic characterization, 95 isolates of Salmonella enterica, E. coli, Campylobacter spp., L. monocytogenes, Staphylococcus spp., Enterococcus spp. were chosen from the research collection, which was formed as part of the monitoring program for antibiotic resistance. Conclusion: Deposited isolates that underwent whole-genome analysis can be used as positive control samples both in the development and use of methods or test systems for the detection of various resistance genes in zoonotic bacteria. In addition, such isolates can also be used for microbiological studies related to determining the sensitivity of microorganisms to antibacterial drugs, for phenotypic studies in the diagnosis of various bacterial infections in animals and birds, and retrospective analysis of strains from numerous collections. Keywords: antibiotics, bioresource centers, genes, microorganisms, prevalence, resistance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".