Antimicrobial peptides with high bioactivity against MDR isolates: Addressing public health concerns
Bibliographic record
Abstract
Antimicrobial resistance is a rapidly escalating global health concern, largely driven by the overuse and misuse of antibiotics in agriculture and clinical settings. There is an urgent and currently unmet need for effective therapeutics against multi-drug resistant (MDR) pathogens. Antimicrobial peptides (AMPs) are a diverse class of cationic peptides that have the potential to overcome extant resistance mechanisms. We evaluated the antimicrobial efficacy of 13 structurally distinct antimicrobial peptides against a panel of drug-resistant Escherichia coli strains. Toxicity assays, including hemolysis and cell viability tests, were performed to determine therapeutic indices and assess the clinical potential of those select peptides. Our findings indicate that, relative to susceptible reference strains, the tested AMPs retain robust bioactivity against known MDR isolates, exhibiting only marginal or no decrease in antimicrobial efficacy. Among them, TeRu4 emerged as the lead candidate, with a minimum inhibitory concentration of 0.5 μg/mL and a therapeutic index exceeding 256. This study underscores the potential of AMPs to act as powerful alternatives to traditional antibiotics, offering new possibilities to address public health concerns surrounding drug-resistant bacterial infections.
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 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".