Fungal keratitis in Iran: Risk factors, clinical features, and mycological profile
Bibliographic record
Abstract
Introduction This study was intended to investigate the clinical features and predisposing factors of fungal keratitis (FK), as well as molecular identification and antifungal susceptibility of causative agents in Tehran, Iran. Methods This cross-sectional study was carried out from April 2019 to May 2021. All fungi isolates were identified using conventional methods and were confirmed by DNA-PCR-based molecular assays. Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) was used to identify yeast species. Minimum inhibitory concentrations (MIC) of eight antifungal agents were assessed according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) microbroth dilution reference method. Results Fungal etiology was confirmed in 86 (7.23%) of 1189 corneal ulcers. A significant predisposing factor for FK was ocular trauma caused by plant materials. Therapeutic penetrating keratoplasty (PKP) was required in 60.4% of cases. The predominant fungal species isolated was Fusarium spp. (39.5%) followed by Aspergillus spp. (32.5%) and Candida spp. (16.2%). Discussion The MIC results indicate that amphotericin B may be appropriate for treating FK caused by Fusarium species. FK caused by Candida spp. can be treated with flucytosine, voriconazole, posaconazole, miconazole, and caspofungin. In developing countries such as Iran, corneal infection due to filamentous fungi is a common cause of corneal damage. In this region, fungal keratitis is observed primarily within the context of agricultural activity and subsequent ocular trauma. Fungal keratitis can be managed better with understanding the ”local“ etiologies and antifungal susceptibility patterns.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".