Antibacterial activities of physiologically stable, self-assembled peptide nanoparticles
Bibliographic record
Abstract
In this study, we report that host defense protein-derived ten amino acid long disulfide-linked peptides self-assemble in the form of β-sheets and β-turns, and exhibit concentration-dependent self-assembly in the form of nanospheres, termed as disulfide linked nanospheres (DSNs). As expected, bare DSNs are prone to aggregation in ionic solutions and in the presence of serum proteins. To yield physiologically stable self-assembled peptide-based materials, DSNs are stabilized in the form of supramolecular assemblies using β-cyclodextrins (β-CD) and fucoidan, as delivery carriers. The inclusion complexes of DSNs with β-CD (β-CD-DSN) and electrostatic complexation of fucoidan with DSNs (FC-DSN) stabilizes the secondary structure of DSNs. Comparison of β-CD-DSNs with FC-DSNs reveals that inclusion complexes of DSNs formed in the presence of β-CD are highly stable under physiological conditions, show high cellular uptake, exhibit bacterial flocculation, and enhance antibacterial efficacies of DSNs in a range of Gram-positive and Gram-negative bacteria.
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) | 0.000 | 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".