Tuning Antibacterial Activity of Cyclodextrin-Attached Cationic Ammonium Surfactants by a Supramolecular Approach
Bibliographic record
Abstract
Two β-cyclodextrin-attached cationic ammonium surfactants bearing a dodecyl chain (APDB) and a hexadecyl chain (APCB) were synthesized to reduce the cytotoxicity of cationic surfactants to mammalian cells and endow the surfactants with host–guest recognition sites, and three kinds of guest molecules were utilized to improve the antibacterial ability of APDB and APCB via host–guest interaction by regulating the electrostatic or hydrophobic interaction of APDB or APCB with bacteria. The guest molecules include AD-NH 3 + carrying one positive charge, DB with a benzene ring group and a dodecyl chain, and single chain cationic ammonium surfactant DTAB or CTAB. Either AD-NH 3 + or DB increases the killing efficacy of APCB against S. aureus at 50 μM from 59% to about 75%, while DTAB or CTAB improves the killing efficacy of APCB to more than 90%. In particular, only a very small amount CTAB can improve the antibacterial activity of APCB to a very high level, but keeps very low cytotoxicity. However, the mixtures of the guest molecules with APDB are devoid of any activity against S. aureus . This is mainly attributed to the fact that APCB and its mixtures with the guest molecules form 100–200 nm spherical aggregates, while the mixtures of APDB with the guest molecules cannot form aggregates at lower concentration. It is revealed that the three kinds of guest molecules trapped in the APCB spherical aggregates lead to diverse interaction modes of the APCB spherical aggregates with S. aureus, accounting for the different killing efficacy of the APCB/guest molecule mixtures. This supramolecular strategy provides an effective approach for the construction of highly efficient antibacterial agents with low cytotoxicity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".