In Situ Hybridization of Polymeric Curcumin to Arginine-Derived Carbon Quantum Dots for Synergistic Treatment of Bacterial Infections
Bibliographic record
Abstract
Effective infectious keratitis treatment must eliminate the pathogen, reduce the inflammatory response, and prevent persistent damage to the cornea. Infectious keratitis is generally treated with broad-spectrum antibiotics; however, they have the risk of causing corneal epithelial cell damage and drug resistance. In this study, we prepared a nanocomposite (Arg-CQDs/pCur) from arginine (Arg)-derived carbon quantum dots (Arg-CQDs) and polymeric curcumin (pCur). Partial carbonization of arginine hydrochloride in the solid state by mild pyrolysis resulted in the formation of CQDs, which exhibited enhanced antibacterial activity. pCur was formed by the polymerization of curcumin, and further crosslinking reduced its cytotoxicity and improved antioxidative, anti-inflammatory, and pro-proliferative activities. The pCur in situ conjugated with Arg-CQDs to form the Arg-CQDs/pCur nanocomposite, which showed a minimum inhibitory concentration of ca. 10 μg mL –1, which was >100-fold and >15-fold lower than that of the precursor arginine and curcumin, respectively, against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa . The Arg-CQDs/pCur nanocomposite with combined antibacterial, antioxidative, anti-inflammatory, pro-proliferative properties, and long-term retention on cornea enabled synergistic treatment of bacterial keratitis. In a rat model, it can effectively treat P. aeruginosa -induced bacterial keratitis at a concentration 4000-fold lower than the commercially used drug, Sulmezole eye drops. Arg-CQDs/pCur nanocomposites have great potential for application in antibacterial and anti-inflammatory nanoformulations for clinical use to treat infectious diseases.
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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 | 0.001 | 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".