Spectrum of bacterial pathogens in inflammatory and noninflammatory cutaneous ulcers of American tegumentary leishmaniasis
Bibliographic record
Abstract
Background: Cutaneous leishmaniasis (CL) ulcers exhibiting an inflammatory phenotype, characterized by purulent exudate, erythema, pain, and/or lymphatic involvement, are empirically treated with antibiotics. Objective: The spectrum of bacteria present in localized versus inflammatory phenotypes of CL is elucidated herein. Methods: Filter paper lesion impressions (FPLIs) from 39 patients with CL (19 inflammatory and 20 noninflammatory ulcers) were evaluated via real-time polymerase chain reaction (qPCR) and end-point PCR targeting: Staphylococcus aureus, Enterobacter cloacae, Streptococcus pyogenes, Enterococcus spp., Citrobacter freundii, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and 16S rDNA. Whole genome sequencing (WGS) was performed on six specimens. Results: In total, 30/39 (77%) patients’ ulcers had ⩾1 bacterium detected, which included the following species: S. aureus ( n = 16, 41%), C. freundii ( n = 13, 33%), P. aeruginosa ( n = 12, 31%), E. cloacae ( n = 12, 31%), K. pneumoniae ( n = 11, 28%), Enterococcus spp. ( n = 7, 18%), E. coli ( n = 6, 15%), and S. pyogenes ( n = 4, 10). Prevalence of bacterial species did not differ by CL phenotype ( p = 0.63). However, patients with inflammatory phenotypes were, on average, over a decade older than patients with noninflammatory phenotypes (42 years vs 27 years) ( p = 0.01). The inflammatory phenotype was more prevalent among ulcers of Leishmania Viannia braziliensis (58%) and L. V. panamensis (83%) compared to those of L. V. guyanensis (20%) ( p = 0.0369). Conclusion: The distribution of flora did not differ between inflammatory and noninflammatory CL phenotypes. Further prospective analysis, including additional WGS studies of all CL ulcers for nonbacterial organisms, is necessary to determine the role of empiric antibiotic therapy in inflammatory and purulent CL.
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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 | 0.000 |
| 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) | 0.001 | 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".