Dynamics of fecal microbial communities in children with diarrhea of unknown etiology and genomic analysis of associated Streptococcus lutetiensis
Bibliographic record
Abstract
BACKGROUND: The sequences of the 16S rRNA genes extracted from fecal samples provide insights into the dynamics of fecal microflora. This potentially gives valuable etiological information for patients whose conditions have been ascribed to unknown pathogens, which cannot be accomplished using routine culture methods. We studied 33 children with diarrhea who were admitted to the Children's Hospital in Shanxi Province during 2006. RESULTS: Nineteen of 33 children with diarrhea could not be etiologically diagnosed by routine culture and polymerase chain reaction methods. Eleven of 19 children with diarrhea of unknown etiology had Streptococcus as the most dominant fecal bacterial genus at admission. Eight of nine children whom three consecutive fecal samples were collected had Streptococcus as the dominant fecal bacterial genus, including three in the Streptococcus bovis group and three Streptococcus sp., which was reduced during and after recovery. We isolated strains that were possibly from the S. bovis group from feces sampled at admission, which were then identified as Streptococcus lutetiensis from one child and Streptococcus gallolyticus subsp. pasteurianus from two children. We sequenced the genome of S. lutetiensis and identified five antibiotic islands, two pathogenicity islands, and five unique genomic islands. The identified virulence genes included hemolytic toxin cylZ of Streptococcus agalactiae and sortase associated with colonization of pathogenic streptococci. CONCLUSIONS: We identified S. lutetiensis and S. gallolyticus subsp. pasteurianus from children with diarrhea of unknown etiology, and found pathogenic islands and virulence genes in the genome of S. lutetiensis.
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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 | 0.001 |
| 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".