In vivo virulence of Staphylococcus aureus in native versus prosthetic left-sided valve endocarditis
Bibliographic record
Abstract
Objectives Staphylococcus aureus infective endocarditis is commonly associated with invasive pathology and is worse in prosthetic valve endocarditis. In this study, we aim to examine S. aureus virulence and pathological features of native and prosthetic valve infective endocarditis. Methods Between 2002 and 2020, 438 patients underwent surgery for left-sided endocarditis caused by S. aureus at our center (59% native and 41% prosthetic valve endocarditis). Endocarditis registry was queried, and pathological features were based on the echocardiography and operative findings. In addition, vegetation samples were collected from 6 patients undergoing surgery for infective endocarditis (3 native and 3 prosthetic valve endocarditis). Total RNA was extracted from all specimens, and messenger RNA sequencing was executed for transcriptomic analysis. Data were pooled into STAR aligner, and gene expression related to virulence factors was compared between 2 groups. Results Rates of invasive pathology were higher in prosthetic versus native valve infective endocarditis (76% vs 40%, P < .0001), which impacted the complexity of surgical procedures and perioperative course, but not in-hospital mortality. Transcriptomic analysis has shown differences in gene expression between vegetation specimens of native and prosthetic valve endocarditis, including genes for stress response, biofilm formation, and virulence factors. The gene aur (encodes for aureolysin) was highly upregulated in prosthetic valve vegetations compared with native valve vegetations ( P = .023). Conclusions Prosthetic valve endocarditis caused by S. aureus is associated with further invasive pathology compared with native valve endocarditis, which could be related to upregulation of genes responsible for biofilm formation and metalloproteinase production.
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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 | 0.000 | 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) | 0.001 | 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".