A Culture-Free Lipidomics-Based Screening Test for Uropathogens
Bibliographic record
Abstract
BACKGROUND: A rapid culture-free method is needed to improve diagnostic efficiency and guide timely antimicrobial therapy for urinary tract infections (UTIs). Previously, we utilized the lipidomics-based fast lipid analysis technique (FLAT) to screen for uropathogens by identifying distinctive microbial membrane lipid profiles, specifically lipid A in gram-negative and cardiolipin in gram-positive bacteria. This culture-free assay demonstrated high sensitivity (94%) in detecting gram-negative bacteria but poor sensitivity (51%) for gram-positive bacteria. METHODS: In this study, we pretreated urine pellets with lysozyme prior to FLAT analysis to break down the peptidoglycan layer bacteria, thereby promoting the efficient release of cardiolipin. The limit of detection (LOD) for 4 gram-positive bacteria and Escherichia coli was evaluated using contrived samples with known CFU/mL values and varying concentrations of lysozyme. Subsequently, we validated the optimized method in a clinical cohort of 76 urine samples known to contain gram-positive bacteria as confirmed by urine culture. RESULTS: Optimal sensitivity was achieved by treating 1 mL of urine pellets with 100 µg lysozyme and incubating for 60 minutes, resulting in a 100-fold increase in cardiolipin LOD and a 95% detection rate for gram-positive bacteria. Signal-to-noise ratio for lipid A was also improved. Polymicrobial urine cultures with gram-negative and gram-positive species were identified in 2 patients. CONCLUSIONS: The lysozyme-enhanced FLAT assay enables rapid and culture-free detection of both gram-negative and gram-positive uropathogens directly from urine. The unified workflow decreases the analytical turnaround time by at least 90% making it well-suited for high-throughput clinical laboratories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".