Molecular Identification of Etiological Agents in Fungal and Bacterial Skin Infections: United States, 2020–2024
Bibliographic record
Abstract
Background/Objectives: Cutaneous infections of fungal and bacterial origins are common. An accurate diagnosis—especially concerning pathogens that are difficult to isolate on culture—can be achieved using molecular methods (PCR) with a short turnaround time. Methods: We reviewed records of skin specimens (superficial scrapings) submitted by dermatologists across the United States with a clinically suspected dermatitis. As per physician’s order, specimens were tested for infections either fungal (N = 4262) or bacterial (N = 1707) in origin. All unique specimens (one per patient) were subjected to real-time PCR assays where cases suspected of a fungal etiology were tested for dermatophytes, Malassezia and Candida, and cases suspected of a bacterial etiology were tested for Streptococcus pyogenes, Staphylococcus aureus, and the mecA gene potentially conferring β-lactam resistance. Results: Fungal agents were detected in 32.8% (SD: 4.5) of the submitted specimens, with most attributed to dermatophytes (19.3% (SD: 4.9)), followed by Malassezia (8.7% (SD: 2.8)) and Candida (2.9% (SD: 1.0)). Dermatophyte detection was more common in the elderly (≥65 years) compared to young adults (18–44 years) (OR: 1.8 (95% CI: 1.5, 2.2)), whereas Malassezia was more commonly detected in younger age groups (12.1–13.6%) than the elderly (5.6%). Candida was more frequently observed in females while dermatophytes and Malassezia were more frequently observed in males. Approximately one quarter of the submitted skin specimens tested positive for S. aureus (23.6% (SD: 3.4)), of which 34.4% (SD: 9.8) exhibited concurrent detection of the mecA gene. An S. aureus detection was more frequently observed in males (OR: 1.5 (95% CI: 1.2, 1.9)) and in children (OR: 1.7 (95% CI: 1.2, 2.5)). Streptococcus pyogenes was rarely detected. Among specimens positive for dermatophytes, 12.0% (20/166) showed co-detection of S. aureus and mecA, which is in contrast to 6.8% (70/1023) detected in samples without a fungal co-detection and 6.2% (8/130) in samples positive for Malassezia. Conclusions: PCR testing, when available, can be valuable as a part of routine care for diagnosing patients with clinically suspected skin infections. Further studies are warranted to survey the prevalence of resistant S. aureus isolates in dermatology outpatients, in particular with regard to the association with dermatophyte 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".