The Influence of the Seasonal Variability of Candida spp. Bloodstream Infections and Antifungal Treatment: A Mediterranean Pilot Study
Bibliographic record
Abstract
Background/Objectives: Various factors associated with seasonality, including temperature, humidity, geographical composition, and seasonal fluctuations, can influence the trends of microbes responsible for hospital infections, such as Candida spp. This study evaluates the seasonal variability of Candida spp. bloodstream infections and antifungal resistance in hospitalized patients in Sicily. Methods: We retrospectively analyzed the demographic and epidemiological characteristics of 175 patients with blood cultures positive for Candida spp. Who were hospitalized at University Hospital Paolo Giaccone (A.U.O.P.), University of Palermo, Italy, from 1 January 2022 to 31 December 2024. Data on Candida species and antifungal resistance were also collected from the hospital’s database system to prevent and control hospital infections in A.U.O.P. Results: A total of 175 patients, 57.7% males, with a mean age of 68.3 years, were included in this study. Candida parapsilosis, Candida albicans, and Candida glabrata were more frequent in ICU (54.5%, p = 0.0001), medical (72.5%, p = 0.0003), and surgical settings (24%, p = 0.0161), respectively. C. parapsilosis was more frequent in dead patients (53.2%, p = 0.005). Among the seasons, we observed a significantly higher presence of C. glabrata in Autumn (20%, p = 0.0436). From the analysis of the seasons, C. parapsilosis and C. albicans were more frequent for each season, except in Spring, where the most frequent isolates were C. glabrata (5.1%, p = 0.0237) and C. parapsilosis (9.7%, p < 0.0001). The antifungal with the most resistance to Candida spp. was fluconazole in all seasons. Conclusions: Our study highlights the seasonal trends in Candida spp. and antifungal resistance, emphasizing climate change’s challenges on fungal diseases. These findings may contribute to improving prevention and treatment strategies for candidemia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".