Ternary Thermosensitive Hydrogel‐Encapsulated Macrolactam Heneicosapeptide Eliminates Epidermal Multidrug‐Resistant Bi‐Microbial Colonization
Bibliographic record
Abstract
Abstract Superbug epidemic has rendered antibiotic therapeutics increasingly ineffective. Worse still, there are few applicable medication regimens for polymicrobial infections caused by two or more multidrug‐resistant bacteria. Herein, a panel of antibacterial cyclic peptides are designed and synthesized and the lead compound cyclo‐zp80r shows favorable activities against a broad spectrum of bacteria. Encouragingly, it exhibited a strong bactericidal effect against two important epidermic species Pseudomonas aeruginosa and Staphylococcus aureus for both single‐ and co‐infections. The peptide cyclo‐zp80r is proposed to destroy the membrane structures of both Gram‐negative and Gram‐positive bacteria, inducing a variety of physiological disorders. To better adapt to topical administration of this novel antibacterial agent, a hydrogel formulation consisting of poloxamer 407, poloxamer 188, and hyaluronic acid is optimized. This ternary hydrogel system is able to form in situ gel at skin temperature. Encapsulated peptide molecules are released steadily in both human skin ex vivo model and mouse wound in vivo model to treat bi‐microbial infection. This work systematically investigates the design, synthesis, antibacterial mechanism of a novel cyclic peptide, and its drug delivery strategy for topical wound infection, offering a promising therapeutics to treat multidrug‐resistant polymicrobial wound 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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".