Chemical Profiling of <i>Candida albicans</i> Growth Phases by Vibrational Spectroscopy
Bibliographic record
Abstract
Candida albicans is an opportunistic fungal pathogen whose growth phase-dependent chemical changes play a critical role in its physiology, pathogenicity, and response to treatment. Here, vibrational spectroscopy combined with chemometric models was applied to investigate differences in spectral signatures of C. albicans yeast cells across different growth phases. C. albicans isolated from different clinical presentations were subjected to spectral analysis at multiple time points over a 24 h period using a Fourier Transform Infrared (FTIR) spectrometer with a built-in Attenuated Total Reflectance (ATR) device. Principal Component Analysis (PCA) and Partial Least Squares Regression analysis (PLS-R) demonstrated that exponential phase and stationary phase time points were spectroscopically distinct based on variations in protein, lipid, polysaccharide, and DNA bands. Across the chemometric methods, the bands associated with glucans, chitin and mixed mannan, and DNA were significant, with the mannan band emerging as a distinctive feature of stationary phase time points in both analytical approaches. Notably, the PLS-R analysis yielded a strong linear correlation between time points and spectral bands, highlighting the reliability of these spectroscopic signatures in characterizing growth phases. This comprehensive analysis highlights the distinct spectroscopic profiles associated with C. albicans growth phases, shedding light on the intraphase chemistry of yeast cells. Our findings underscore the potential of vibrational spectroscopy as a valuable tool for investigating the dynamics of fungal growth and inform future advances in diagnostics and therapeutic strategies for fungal infections.
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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 | 0.001 |
| 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 | 0.001 | 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".