Transfer of Antibiotic Resistance Genes from Gram-positive Bacterium to Gram-negative Bacterium
Bibliographic record
Abstract
Abstract The emergence of antibiotic resistance due to uncontrolled use of antibiotics in non-humans, poses a major threat for treating bacterial infections in humans. Added to this is the possibility of transfer of resistance from Gram-positive bacteria to Gram-negative bacteria. Therefore, the possibility of resistance gene transfer from a non-human originated pathogenic bacterium to a pathogenic bacterium infecting humans needs evaluation. In this study, poultry litter samples collected from Tamil Nadu, India were screened for the presence of meropenem- and cefotaxime-resistant Staphylococcus sciuri . Standard microbiological techniques and 16S rRNA analysis were used to confirm S. sciuri . In the resistant isolates, resistance genes such as bla NDM-1 , bla OXA-48-like , bla KPC , bla VIM , bla IMP and bla CTX-M were screened. Transconjugation studies were performed using donor, S. sciuri and recipient, E. coli AB1157 (Str r ). A total of 26 meropenem-resistant and 24 cefotaxime resistant S. sciuri were isolated from poultry litter samples. The presence of bla NDM-1 (n=2), bla IMP (n=8), bla CTX-M-9 (n=5) and bla CTX-M-2 (n=1) was detected. Transconjugation results confirmed that S. sciuri carrying plasmid-borne resistance gene bla NDM-1 conjugated to E. coli AB1157. The transferability of resistance genes from S. sciuri to E. coli could be another possible reason for spread of antibiotic-resistant bacteria in humans.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".