Super-Cationic Carbon Quantum Dots Synthesized from Spermidine as an Eye Drop Formulation for Topical Treatment of Bacterial Keratitis
Bibliographic record
Abstract
We have developed a one-step method to synthesize carbon quantum dots (CQD PAs ) from biogenic polyamines (PAs) as an antibacterial agent for topical treatment of bacterial keratitis (BK). CQDs synthesized by direct pyrolysis of spermidine (Spd) powder through a simple dry heating treatment exhibit a solubility and yield much higher than those from putrescine and spermine. We demonstrate that CQDs obtained from Spds (CQD Spds ) possess effective antibacterial activities against non-multidrug-resistant Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Salmonella enterica serovar Enteritidis bacteria and also against the multidrug-resistant bacteria, methicillin-resistant S. aureus . The minimal inhibitory concentration (MIC) of CQD Spds is ∼2500-fold lower than that of spermidine alone, demonstrating their strong antibacterial capabilities. Investigation of the possible mechanisms behind the antibacterial activities of the as-synthesized CQD Spds indicates that the super-cationic CQD Spds with small size (diameter ca. 6 nm) and highly positive charge (ζ-potential ca. +45 mV) cause severe disruption of the bacterial membrane. In vitro cytotoxicity, hemolysis, hemagglutination, genotoxicity, and oxidative stress and in vivo morphologic and physiologic cornea change evaluations show the good biocompatibility of CQD Spds . Furthermore, topical ocular administration of CQD Spds can induce the opening of the tight junction of corneal epithelial cells, thereby leading to great antibacterial treatment of S. aureus -induced BK in rabbits. Our results suggest that CQD Spds are a promising antibacterial candidate for clinical applications in treating eye-related bacterial infections and even persistent bacteria-induced infections.
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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 | 0.000 | 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 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".