Surface fouling by Candida albicans on steel modified with various surface-assembled monolayers
Bibliographic record
Abstract
Fungal biofilms are a growing issue in human health and disease, increasing patient morbidity, while causing a large economic burden for treatment of biofilm derived infections. Candida albicans is a well known fungus, which forms biofilms through initial cell attachment, followed by proliferation, and finally biofilm formation. Various surface coatings have been employed to reduce biofilm formation from fungi such as C. albicans , including polyethylene glycol, anti-fungal coatings, SLIPS, and nanoparticles. In this study C. albicans biofilms were grown on steel surfaces with various coatings including APTES, OTS, MEG-Cl, and MEG-OH to determine which surface functionalities C. albicans is best able to form a biofilm on. The surface roughness of the steel coupons was also varied to see if this had any effect on biofilm formation. Coatings that were found to reduce biofilm formation were also subjected to time studies of C. albicans biofilm formation to determine at which stage of biofilm formation was the process interrupted. It was found that MEG-Cl was able to halt biofilm formation at the proliferation stage, preventing the formation of mature biofilms. Meanwhile, MEG-OH was able to almost entirely eliminate C. albicans surface fouling on steel regardless of the surface roughness. • C. albicans is able to form a consistent biofilm on steel surfaces, regardless of surface roughness. • Hydrophobic (OTS) and positively charged (APTES) coatings had no significant effect on biofilm formation. • The negatively charged coating (MEG-Cl) was able to halt biofilm formation before the maturation stage. • The hydrophilic coating (MEG-OH) was able to almost entirely eliminate biofilm formation by 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 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 | 0.001 | 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 | 0.001 | 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 teacher head, 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".