Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres
Bibliographic record
Abstract
Bacterial vaginosis (BV) is a recurring polymicrobial syndrome that is characterized by a change in the "normal" microbiota from Lactobacillus-dominated to a microbiota dominated by a number of bacterial species, including Gardnerella vaginalis, Atopobium vaginae, and others. This condition is associated with a range of negative health outcomes, including HIV acquisition, and it can be difficult to manage clinically. Furthermore, diagnosis of BV has relied on the use of Gram stains of vaginal swab smears that are scored on various numerical criteria. While this diagnostic is simple, inexpensive, and well suited to resource-limited settings, it can suffer from problems related to subjective interpretations and it does not give a detailed profile of the composition of the vaginal microbiota. Recent deep sequencing efforts have revealed a rich, diverse vaginal microbiota with clear differences between samples taken from individuals that are diagnosed with BV compared to those individuals that are considered normal, which has resulted in the identification of a number of potential targets for molecular diagnosis of BV. These studies have provided a wealth of useful information, but deep sequencing is not yet practical as a diagnostic method in a clinical setting. We have recently described a method for rapidly profiling the vaginal microbiota in a multiplex format using oligonucleotide-coupled fluorescent beads with detection on a Luminex platform. This method, like current Gram stain-based methods, is rapid and simple but adds the additional advantage of exploiting molecular knowledge arising from sequencing studies in probe design. This method therefore provides a way to profile the major microorganisms that are present in a vaginal swab that can be used to diagnose BV with high specificity and sensitivity compared to Gram stain while providing additional information on species presence and abundance in a semi-quantitative and rapid manner. This multiplex method is expandable well beyond the range of current quantitative PCR assays for particular organisms, which is currently limited to 5 or 6 different assays in a single sample. Importantly, the method is not limited to the detection of bacteria in vaginal swabs and can be easily adapted to rapidly profile nearly any microbial community of interest. For example, we have recently begun to apply this methodology to the development of diagnostic tools for use in wastewater treatment plants.
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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) | 0.001 | 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 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".