Bibliographic record
Abstract
Strains of pathogens are typically described as virulent or non-virulent. However, in the majority of pathogens, strains often vary continuously and quantitatively in their virulence and pathogencity. Biofilm formation is one of the recently recognized virulence factors in many human pathogens and little is known about the variation and evolution of biofilms among natural strains. In this study, I examined quantitative variation of biofilms among natural strains of the human pathogenic yeast Candida albicans. A total of 115 natural strains of C. albicans from three sources (vaginal, oral and environmental) were quantified by two mebods: (i) the XTT tetrazolium reduction assay, and (ii) optical density following staining by crystal violet dye. Mature biofilm was confirmed by observation using confocal laser scanning microscopy. My analyses indicated that strains from each of the three scurces varied widely in biofilm formation abilities and that biofilm formation ability was positively correlated to cell surface hydrophobicity (CSH). For each strain, multilocus genotypes were determined by PCR-RFLP, my comparative genotype and biofilm analyses denonstrated that natural clones and clonal lineages of C. albicans exhibited extensive quantitative variation for biofilm formation. I also examined potential interactions among strains within C. albicans and between different Candida species. My preliminary results suggest significant variation and complex patterns of strains or species interaction during Candida biofilm development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".