Characterization of Salmonella enterica serovar Enteritidis isolates recovered from blood and stool specimens in Thailand
Bibliographic record
Abstract
BACKGROUND: Bacteremia due to Salmonella spp. is a life-threatening condition and is commonly associated with immune compromise. A 2009 observational study estimated risk factors for the ten most common non-typhoidal Salmonella (NTS) serovars isolated from Thai patients between 2002-2007. In this study, 60.8% of Salmonella enterica serovar Enteritidis isolates (n = 1517) were recovered from blood specimens and infection with Salmonella serovar Enteritidis was a statistically significant risk factor for bacteremia when compared to other NTS serovars. Based on this information, we characterized a subset of isolates collected in 2008 to determine if specific clones were recovered from blood or stool specimens at a higher rate. Twenty blood isolates and 20 stool isolates were selected for antimicrobial resistance testing (MIC), phage typing, PFGE, and MLVA. RESULT: Eight antibiogrammes, seven MLVA types, 14 XbaI/BlnI PFGE pattern combinations, and 11 phage types were observed indicating considerable diversity among the 40 isolates characterized. Composite analysis based on PFGE and MLVA data revealed 22 genotypes. Seven of the genotypes containing two or more isolates were from both stool and blood specimens originating from various months and zones. Additionally, those genotypes were all further discriminated by phage type and/or antibiogramme. Ninety percent of the isolates were ciprofloxacin resistant. CONCLUSIONS: The increased percentage of bloodstream infections as described in the 2009 observational study could not be attributed to a single clone. Future efforts should focus on assessing the immune status of bacteriaemic patients and identifying prevention and control measures, including attribution studies characterizing non-clinical (animal, food, and environmental) isolates.
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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 | 0.001 | 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".