Construction of Alizarin Conjugated Graphene Oxide Composites for Inhibition of Candida albicans Biofilms
Bibliographic record
Abstract
Biofilm inhibition using nanoparticle-based drug carriers has emerged as a noninvasive strategy to eradicate microbial contaminants such as fungus Candida albicans. In this study, one-step adsorption strategy was utilized to conjugate alizarin (AZ) on graphene oxide (GO) and characterized by ultraviolet-visible spectroscopy (UV-Vis), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), X-ray powder diffraction (XRD), dynamic light-scattering (DLS), and transmission electron microscopy (TEM). Crystal violet assay was performed to evaluate the antibiofilm efficacy of GO-AZs against C. albicans. Different characterizations disclosed the loading of AZ onto GO. Interestingly, TEM images indicated the abundant loading of AZ by producing a unique inward rolling of GO-AZ sheets as compared to GO. When compared to the nontreatment, GO-AZ at 10 µg/mL significantly reduced biofilm formation to 96% almost equal to the amount of AZ (95%). It appears that the biofilm inhibition is due to the hyphal inhibition of C. albicans. The GO is an interesting nanocarrier for loading AZ and could be applied as a novel antibiofilm agent against various microorganisms including C. albicans.
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".