Biofilm-forming capacity of blood–borne Candida albicans strains and effects of antifungal agents
Bibliographic record
Abstract
Infections related to Candida albicans biofilms and subsequent antifungal resistance have become more common with the increased use of indwelling medical devices. Regimens for preventing fungal biofilm formation are needed, particularly in high-risk patients. In this study, we investigated the biofilm formation rate of multiple strains of Candida albicans (n = 162 clinical isolates), their antifungal susceptibility patterns, and the efficacy of certain antifungals for preventing biofilm formation. Biofilm formation was graded using a modified Christensen's 96-well plate method. We further analyzed 30 randomly chosen intense biofilm-forming isolates using the XTT method. Minimum biofilm inhibition concentrations (MBIC) of caspofungin, micafungin, anidulafungin, fluconazole, voriconazole, posaconazole, itraconazole, and amphotericin B were determined using the modified Calgary biofilm method. In addition, the inhibitory effects of antifungal agents on biofilm formation were investigated. Our study showed weak, moderate, and extensive biofilm formation in 29% (n = 47), 38% (n = 61), and 23% (n = 37) of the isolates, respectively. We found that echinocandins had the lowest MBIC values and that itraconazole inhibited biofilm formation in more isolates (26/32; 81.3%) than other tested agents. In conclusion, echinocandins were most effective against formed biofilms, while itraconazole was most effective for preventing biofilm formation. Standardized methods are needed for biofilm antifungal sensitivity tests when determining the treatment and prophylaxis of C. albicans infections. Las infecciones relacionadas con las biopelículas de Candida albicans y la consiguiente resistencia antifúngica se han vuelto fenómenos habituales con el uso creciente de dispositivos médicos permanentes. Son necesarios regímenes para prevenir la formación de biopelículas fúngicas, en especial en los pacientes de alto riesgo. En este estudio se investigó la tasa de formación de biopelículas de numerosas cepas de Candida albicans (162 aislados clínicos), sus patrones de sensibilidad a los antifúngicos y la eficacia de algunos de estos agentes para prevenir la formación de biopelículas. La formación de biopelículas se clasificó utilizando el método de Christensen modificado de 96 pocillos. Posteriormente se analizaron 30 aislados de formación intensa de biopelículas elegidos al azar, utilizando el método XTT. Se calcularon las concentraciones mínimas de inhibición de biopelículas (minimum biofilm inhibition concentrations, MBIC) de la caspofungina, la micafungina, la anidulafungina, el fluconazol, el voriconazol, el posaconazol, el itraconazol y la anfotericina B, utilizando el método modificado de biopelículas de Calgary. Además, se investigaron los efectos inhibitorios de los agentes antifúngicos sobre la formación de biopelículas. Nuestro estudio encontró una formación débil, moderada e intensa de biopelículas en el 29% (n = 47), 38% (n = 61) y 23% (n = 37) de los aislados, respectivamente. Encontramos que las equinocandinas mostraron los menores valores MBIC, y que el itraconazol inhibió la formación de biopelículas en más aislados (26/32; 81,3%) que otros agentes ensayados. En conclusión, las equinocandinas resultaron más eficaces frente a las biopelículas formadas, mientras que el itraconazol resultó más eficaz para prevenir la formación de biopelículas. Se necesita contar con métodos estandarizados para efectuar las pruebas de sensibilidad a los antifúngicos en términos de formación de biopelículas a la hora de determinar el tratamiento y la profilaxis de las infecciones por C. albicans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".