<scp>d</scp>-Amino acids in antimicrobial peptides: a potential approach to treat and combat antimicrobial resistance
Bibliographic record
Abstract
Antimicrobial resistance is one of the leading challenges in the human healthcare segment. Advances in antimicrobial resistance have triggered exploration of natural alternatives to stabilize its seriousness. Antimicrobial peptides are small, positively charged oligopeptides that are as potent as commercially available antibiotics against a wide spectrum of organisms, such as Gram-positive bacteria, Gram-negative bacteria, viruses, and fungal strains. In addition to their antibiotic capabilities, these peptides possess anticancer activity, activate the immune response, and regulate inflammation. Peptides have distinct modes of action and fall into various categories due to their amino acid composition. Although antimicrobial peptides specifically target the bacterial cytoplasmic membrane, they can also target the cell nucleus and protein synthesis. Owing to the increasing demand for novel treatments against the threat of antimicrobial resistance, naturally synthesized peptides are a beneficial development concept. Antimicrobial peptides are pervasive and can easily be modified using de-novo synthesis technology. Antimicrobial peptides can be isolated from natural resources such as humans, plants, bacteria, and fungi. This review gives a brief overview of antimicrobial peptides and their diastereomeric composition. Other current trends, the future scope of antimicrobial peptides, and the role of d-amino acids are also discussed, with a specific emphasis on the design and development of new drugs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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".