Antibiotic adjuvants: synergistic tool to combat multi-drug resistant pathogens
Bibliographic record
Abstract
The rise of multi-drug resistant (MDR) pathogens poses a significant challenge to the field of infectious disease treatment. To overcome this problem, novel strategies are being explored to enhance the effectiveness of antibiotics. Antibiotic adjuvants have emerged as a promising approach to combat MDR pathogens by acting synergistically with antibiotics. This review focuses on the role of antibiotic adjuvants as a synergistic tool in the fight against MDR pathogens. Adjuvants refer to compounds or agents that enhance the activity of antibiotics, either by potentiating their effects or by targeting the mechanisms of antibiotic resistance. The utilization of antibiotic adjuvants offers several advantages. Firstly, they can restore the effectiveness of existing antibiotics against resistant strains. Adjuvants can inhibit the mechanisms that confer resistance, making the pathogens susceptible to the action of antibiotics. Secondly, adjuvants can enhance the activity of antibiotics by improving their penetration into bacterial cells, increasing their stability, or inhibiting efflux pumps that expel antibiotics from bacterial cells. Various types of antibiotic adjuvants have been investigated, including efflux pump inhibitors, resistance-modifying agents, and compounds that disrupt bacterial biofilms. These adjuvants can act synergistically with antibiotics, resulting in increased antibacterial activity and overcoming resistance mechanisms. In conclusion, antibiotic adjuvants have the potential to revolutionize the treatment of MDR pathogens. By enhancing the efficacy of antibiotics, adjuvants offer a promising strategy to combat the growing threat of antibiotic resistance. Further research and development in this field are crucial to harness the full potential of antibiotic adjuvants and bring them closer to clinical application.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".