Clinical Isolate of <i>Candida tropicalis</i> from a Patient in North Carolina: Identification, Whole-Genome Sequence Analysis, and Anticandidal Activity of <i>Ganoderma lucidum</i>
Bibliographic record
Abstract
In North Carolina, candida infections are on the rise and pose a significant threat to human health in clinical settings. In addition, the rise of resistance to antifungal drugs has only heightened this concern. Importantly, misidentification of Candida spp. may result in underdiagnosis, patients getting the wrong treatment and incomplete infection prevention measures. The correct and rapid etiological identification of Candida infections is of paramount importance because it provides adequate therapy, reduces mortality, and controls outbreaks. Hence, this study aimed to identify Candida sp. up to species level of a clinical isolate from an infected patient treated in North Carolina using biochemical and molecular techniques. Due to the emergence of resistance, we explored whole genomic analysis to highlight polymorphisms that can impact candida resistance. Exploration for the effectiveness of bioactive compounds in natural products to treat Candida spp. resistant to present-day drugs could provide promising new treatment options for managing infected patients. Thus, this study also investigated anticandida activity of three solvent extracts of Ganoderma lucidum against the clinical isolate of Candida sp. The findings of this study provided evidence that Candida tropicalis MYA-3404 was the only strain present in the clinical isolate. The whole genome sequencing of C. tropicalis identified mutations in genes that most likely underscore drug resistance. All extracts from G. lucidum significantly (P C. tropicalis. Together, this work highlights the enormous potential of biochemical and molecular techniques in identifying clinical isolates of candida to species level and the use of bioactive compounds from extracts of G. lucidum as promising anticandidal agents. Further testing is needed to confirm the phenotypic expression of resistance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".