A microbiome approach to sepsis: development and case-study application of novel methods for detection and isolation of microbes from whole blood
Bibliographic record
Abstract
The application of molecular profiling methods to a wide variety of infections suggests that a polymicrobial community is much more common than suggested by standard clinical culture [ 1 ]. Our goal was to develop methods, using a microbiome approach, to improve culture and molecular diagnostics for bacterial sepsis. Culture and DNA extraction protocols were evaluated using synthetic bacterial communities inoculated into whole blood. Disruption of blood cells with a blood cell lysing detergent, with or without hypotonic osmotic shock, was carried out and evaluated for the ability to recover the community. Viable bacterial cells were recovered on solid media. Total DNA was examined by terminal-restriction fragment-length polymorphism (TRFLP) profiling. Efficiencies of recovery and limits of detection were determined. The optimized methodology was applied to clinical samples collected from consented patients in both the ICU and ED from two Calgary hospitals. Cultured organisms were identified by 16S rRNA gene sequencing. Molecular profiling was carried out using TRFLP and bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). Treatment of synthetic community organisms with a 5% wt/vol detergent added at a 1:1 or 1:5 ratio did not significantly impact their viability. TRFLP analysis indicated that the DNA from these communities could be recovered from whole blood following lysis and removal of host cells. Whole blood samples were analysed from septic patients. In three case studies we identified two to 16 bacterial species in the primary infection samples using direct culture and molecular methods. Conventional diagnostics only reported one organism. Molecular profiling of blood samples from these patients also identified correlating polymicrobial communities. Blood cultures for these samples were either negative (two of three cases) or monomicrobial (one of three cases), thereby underestimating the diversity seen with the TRFLP and bTEFAP analysis. These methods have been applied to 88 adult ICU blood samples, 20 primary infection samples, 36 adult ED samples, and seven pediatric ED samples with analysis ongoing. We have successfully developed a novel method to analyse whole blood in order to characterize the microbiome of sepsis infections. Preliminary results indicate that sepsis infections are polymicrobial in nature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".