Bibliographic record
Abstract
Background: Urinary tract infections (UTI) are among the most common bacterial infections in the United States.The gold standard for diagnosis is urine culture.This provides bacteria identification and sensitivity, but is labor intensive and time-consuming.As such, many UTIs are treated empirically contributing to resistance and cost.There is an urgent need for a diagnostic tool that can rapidly identify uropathogens and their sensitivities.We present the development of a PCR-based assay for Enterobacter cloacae, and evidence for its further use in the context of NanoLantern™ technology.The NanoLantern™ is a chip sensor that utilizes DNA hairpins immobilized onto a gold surface.In the presence of complementary (target) DNA, the hairpin stem is opened, exposing a quantifiable fluorophore.Methods: A synthetic DNA hairpin, unique to Enterobacter cloacae, was immobilized onto 9 gold coated chips.Baseline fluorescence intensity was recorded.The chip was exposed to synthetic target and then imaged with a fluorescence microscope.Fluorescence intensity was recorded and compared to baseline.PCR, asymmetric PCR and linear after the exponential PCR were used to amplify the target DNA from the bacteria.The amplicon was exposed to the NanoLantern™ chip and fluorescence intensity was quantified.Results: Pre-synthetic target fluorescence imaging of 9 chips exposed to synthetic DNA resulted in a mean raw count of 665 (SD ± 244).Postsynthetic target fluorescence imaging of 9 chips resulted in a mean raw count of 8197 (SD ± 648), a greater than 12-fold increase in intensity.See Fig. 1.Conclusions: We have demonstrated the feasibility of a novel DNA hairpin probe.Work is ongoing to develop a PCR assay and to begin testing the NanoLantern™ chip with bacteria derived amplicon and urine samples.Ultimately, these integrated assays will provide rapid analysis, as part of an automated system, to be used in the clinical setting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.576 | 0.390 |
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".