Copper‐Cysteine Nanostructures for Synergetic Photothermal Therapy and Chemodynamic Therapy of Bacterial Skin Abscesses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Skin lesions, including skin bacterial abscesses, have become one of the most important health challenges and usually need systemic high‐dose antibiotics. Therefore, it is of particular importance to develop novel approaches for treating this ever‐growing challenge to human health. To address this challenge, herein a copper nanostructure is developed giving combined photothermal and chemodynamic therapies for focal infection treatment. The Cu‐based nanostructures with intrinsic catalytic properties are prepared by D‐L or L cysteine (Cys) as ligand and copper ions. It is shown that the multifunctional copper‐Cys (Cu‐Cys) nanostructures can produce reactive oxygen species (ROS) and they exhibit near infrared (NIR)‐enhanced catalytic activities to improve ROS production for highly efficient eradication of bacteria. Moreover, the results proved O 2 evolution property of the Cu‐Cys nanoparticles (NPs). The nanostructures show shape‐dependent antibacterial activity where DL‐Cu‐Cys NPs show higher bactericidal performance than L‐Cu‐Cys NPs. In vitro results demonstrate that 2.5 and 1.25 µg mL −1 of DL‐Cu‐Cys NPs is enough to achieve rapid killing of Escherichia coli ( E. coli ) or Staphylococcus aureus (S. aureus) respectively under 808 nm light irradiation in 10 min. This work introduces a unique photoactive nanoagent to efficiently treat subcutaneous abscess by combining NIR light‐triggered photothermal effect and catalytic generation of ROS without using any antibiotic.
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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) | 0.000 | 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 it