A Metal-Based Heterojunction for Controlled Release of Multiple Cations and Reactive Oxygen Species Inhibiting Multidrug-Resistant Bacteria <i>In Vitro</i> and <i>In Vivo</i>
Bibliographic record
Abstract
Spherical heterojunction nanocomposite materials are utilized to treat wound infections caused by drug-resistant bacteria by generating reactive oxygen species (ROS) and multiple cations (multiple inorganic or organic ions with positive points). However, there is an ongoing debate on the relative contributions of ROS and multiple cations toward antibacterial activity. In this study, the CuFe 2 O 4 /Cu@PEI/Ag (ZPA) nanocomposites were synthesized for releasing abundant • O 2–, • OH, Fe 3+, Cu 2+, Ag +, and polyethylenimine (PEI), and studied the contribution of the released ions to the bacteriostatic activity against drug sensitive Staphylococcus aureus (ATCC25923) and drug-resistant S. aureus (ATCC43360). The results revealed that the antibacterial activity is attributed in the following order: multiple cations > • O 2– > • OH > single cation. The antibacterial mechanism of the material involved leakage of the cytoplasmic content by damaging the bacterial cell wall, and the alteration of the secondary structure of the cell wall by multiple cations bound to the bacterial cell wall via electrostatic attraction. By healing drug-resistant S. aureus -induced wound infection and completely eliminating bacterial burden after 11 days, in addition, ZPA also effectively polarized M1 type macrophages to M2 type in vivo to promote wound healing. Thus, our findings elucidate that multiple cations occupy an important position on the antibacterial properties of composite nanomaterials. Moreover, The ZPA represent a promising strategy for addressing drug-resistant S. aureus -induced 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) | 0.000 | 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".