Effects of Adjunctive Human Transferrin on Susceptibility and Emergence of Resistance in Gram-negative Pathogens
Bibliographic record
Abstract
This study investigates the problem of ineffective antibacterial treatment against multi-drug resistant pathogens. The number of antibiotic-resistant pathogens has increased over time as a result of bacterial evolution due to antibiotic selective pressure. The goal of this study was to characterize a novel antimicrobial drug combination which causes minimal selective pressure and suppresses the emergence of resistance. We suggested the combination of antibiotics with human transferrin and studied its effects on antibiotic susceptibility and resistance emergence in vitro. First, the in vitro pharmacodynamics of the monotherapy or combination therapy groups were studied over 24 hours. It was found that in most cases there is no evidence of antagonism or synergy between transferrin and antibiotics. Transferrin was characterized to possess a mostly bacteriostatic mode of action. Then, we discovered that transferrin decreases and sometimes prevents the emergence of resistance. For both a high inoculum 24 hours and and lower inoculum 20-days passage experiments, more antibiotic resistant mutants were selected when bacteria were cultured in antibiotic alone vs antibiotic + transferrin. Highly resistant strains with increased virulence resulted from 20 days passage. Virulence was tested by a phagocytosis assay using a murine macrophage cell line (RAW264.7), and the macrophage uptake was significantly lower for monotherapy passaged strains. Thus, adjunctive transferrin can improve the antibacterial therapy and decrease the emergence of resistance in Gram-negative pathogens.
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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) | 0.001 | 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) | 0.001 | 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".