Profiling of the bacteria responsible for pyogenic liver abscess by 16S rRNA gene pyrosequencing
Bibliographic record
Abstract
Pyogenic liver abscess (PLA) is a severe disease with considerable mortality and is often polymicrobial. Understanding the pathogens that cause PLA is the basis for PLA treatment. Here, we profiled the bacterial composition in PLA fluid by pyrosequencing the 16S ribosomal RNA (rRNA) gene based on next-generation sequencing (NGS) technology to identify etiological agents of PLA and to provide information of their 16S rRNA sequences for application to DNA-based techniques in the hospital. Twenty patients with PLA who underwent percutaneous catheter drainage, abscess culture, and blood culture for isolates were included. Genomic DNAs from abscess fluids were subjected to polymerase chain reaction and pyrosequencing of the 16S rRNA gene with a 454 GS Junior System. The abscess and blood cultures were positive in nine (45%) and four (20%) patients, respectively. Pyrosequencing of 16S rRNA gene showed that 90% of the PLA fluid samples contained single or multiple genera of known bacteria such as Klebsiella, Fusobacterium, Streptococcus, Bacteroides, Prevotella, Peptostreptococcus, unassigned Enterobacteriaceae, and Dialister. Klebsiella was predominantly found in the PLA fluid samples. All samples that carried unassigned bacteria had 26.8% reads on average. We demonstrated that the occurrence of PLA was associated with eight known bacterial genera as well as unassigned bacteria and that 16S rRNA gene sequencing was more useful than conventional culture methods for accurate identification of bacterial pathogens from PLA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".