Recent advances: peptides and self-assembled peptide-nanosystems for antimicrobial therapy and diagnosis
Bibliographic record
Abstract
Bacterial infections, especially the refractory treatment of drug-resistant bacteria, are one of the greatest threats to human health. During the past decades, biomedical nanomaterials have been developed in an increasing number of fields, which significantly contribute to our public healthcare systems. Peptide-based drugs, such as antimicrobial peptides, cyclopeptides, and glycopeptides, play important roles in the treatment of drug-resistant bacterial infections, due to their unique lower resistance antibacterial mechanism. Among them, biomimetic nanostructures fabricated by self-assembled peptide nanomaterials have received considerable development in surface protection, tissue engineering, bactericides, etc. Besides, bacterial diagnostic reagents based on self-assembled peptide materials also provide strong support for early detection and infection imaging of bacterial infections. In this review, we have systematically discussed peptide-based self-assembled nanomaterials, including their sequences, subunits, secondary structures, assembled nanostructures, and biomedical applications for antibacterial therapy and diagnosis. We have reviewed and discussed the structure-function relationship, molecular design strategy, and structure effect of antimicrobial peptides. The sequence design of self-assembled peptides and the application of self-assembled peptide nanomaterials in the diagnosis and treatment of bacterial infections are emphasized. Also, we analyzed and summarized the design and development of smart materials, reviewed the innovative "in vivo self-assembly" nanotechnology, and proposed the future design and prospect of smart self-assembly nanomaterials based on peptides in the biological antibacterial field.
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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 | 0.002 | 0.003 |
| 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) | 0.003 | 0.002 |
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".