Molecular Characterization of Shigella sonnei: An Increasingly Prevalent Etiologic Agent of Shigellosis in Guizhou Province, Southwest of China
Bibliographic record
Abstract
BACKGROUND: Shigellosis is a serious problem in Guizhou and Shigella sonnei is an increasingly prevalent etiologic agent of local shigellosis cases. No data, however, are available about the molecular characterization of the local isolates of S. sonnei. We have conducted this study to molecularly characterize the clinical isolates of S. sonnei in Guizhou Province. RESULTS: 76 S. sonnei isolates, including four isolates from 1974-1982 and 72 isolates from 2008-2010, were used for analysis in this study. Pulsed-field gel electrophoresis (PFGE) based on XbaI digestion divided the 76 isolates into 38 PFGE patterns (PT) and 15 PTs were represented by more than one isolates with PT31 (N = 8) containing the most number of isolates, followed with PT2 (N = 6). Multiple-Locus Variable number tandem repeat (VNTR) Analysis (MLVA) based on seven VNTR loci discriminated them into 19 different MLVA types (MTs), and four MTs were represented by more than one isolate with MT4 (N = 39) containing the most number of isolates, followed with MT12 (N = 18). 15 Multilocus sequence typing (MLST) base on 15 loci differentiated the isolates into six sequence types (STs), among which four STs were novel. The most common STs are ST76 (N = 43) and ST116 (N = 25), accounting for 92.1%. Correlation between genetic relationships and geographical origins or isolation years was observed among the isolates studied. Majority of isolates were clustered in accordance with the origin of isolation years based on the genetic data, which were also from similar geographical origins. CONCLUSIONS: Our results revealed the molecular characteristics including the specific genotypes such as four novel STs, clonal relationship, and genetic changes of local isolates from different years, which enhances our understanding of molecular characteristics of S. sonnei and contributes to the prevention and control of shigellosis in Guizhou Province.
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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 | 0.000 |
| 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) | 0.000 | 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".