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Record W4404567382 · doi:10.3390/idr16060087

Molecular Identification of Etiological Agents in Fungal and Bacterial Skin Infections: United States, 2020–2024

2024· article· en· W4404567382 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInfectious Disease Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsMalasseziaEtiologyMedicineStaphylococcus aureusDermatophyteMicrobiologyDermatologyBiologyInternal medicineBacteria

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.295
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it