Whole-genome sequencing confirms Shigella flexneri infection in a child with shock and suspected multisystem inflammatory syndrome in children: A case report
Bibliographic record
Abstract
Background Differentiating severe systemic inflammatory syndromes from sepsis can be challenging. The diagnostic process may be further complicated by concurrent infection and hyperinflammation, with important management implications. We report a child with suspected multisystem inflammatory syndrome in children, who was unexpectedly diagnosed with Shigella gastroenteritis. Case presentation A previously healthy 6-year-old boy acutely presented with fever, vomiting, diarrhea, fluid-refractory shock, cardiac dysfunction, biochemical inflammation, and coagulopathy. He fulfilled diagnostic criteria for multisystem inflammatory syndrome in children, including SARS-CoV-2 exposure 8 weeks prior. He received both antibiotics and pulsed intravenous methylprednisolone, with rapid improvement. Stool molecular testing using a lab-developed multiplex qPCR assay revealed Shigella flexneri infection, confirmed by culture, MALDI-TOF mass spectrometry, and whole-genome sequencing. Serologic testing confirmed prior infection by the SARS-CoV-2 virus. The child fully recovered. Immunological investigations were normal. The case was investigated by the Public Health Department, but the source of Shigella infection was not identified. Conclusions This case underscores the importance of systematic microbiological workup in suspected systemic inflammatory syndromes, to identify infections that are treatable and of public health relevance. Given the rarity of Shigella septic shock in immunocompetent individuals, this case raises the possibility that recent SARS-CoV-2 infection led to immune dysregulation and exaggerated inflammatory responses to Shigella . It also demonstrates the utility of molecular testing for rapid diagnosis and confirmation of gastrointestinal infection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".