Invasive <i>Streptococcus iniae</i> Infections Outside North America
Bibliographic record
Abstract
Streptococcus iniae, a fish pathogen causing infections in aquaculture farms worldwide, has only been reported to cause human infections in North America. In this article, we report the first two cases of invasive S. iniae infections in two Chinese patients outside North America. While the first patient presented with bacteremic cellulitis, which is the most common presentation in previous cases, the second patient represents the first recognized case of S. iniae osteomyelitis. Both S. iniae strains isolated from the two patients were either misidentified or unidentified by three commercial systems and were only identified by 16S rRNA gene sequencing. Since no currently available commercial system for bacterial identification includes S. iniae in its database, 16S rRNA gene sequencing is the most practical and reliable method to identify the bacterium at the moment. In contrast to the distinct genetic profile described previously in clinical isolates from Canada, the present two isolates and a clinical isolate from a Canadian patient were found to be genetically unrelated, as demonstrated by pulsed-field gel electrophoresis. Morphologically, colonies of both isolates were also larger, more beta-hemolytic and mucoid, which differ from the usual morphotype described for S. iniae. Owing to their habit of cooking and eating fresh fish, the Asian population is strongly associated with S. iniae infections. As a result of the difficulty in making microbiological diagnosis in patients with cellulitis and the problem of identification in most clinical microbiology laboratories, the prevalence of S. iniae infections, especially in the Asian population, may have been under-estimated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".