Selective and High Dynamic Range Assay Format for Multiplex Detection of Pathogenic <i>Pseudomonas aeruginosa</i>, <i>Salmonella typhimurium</i>, and <i>Legionella pneumophila</i> RNAs Using Surface Plasmon Resonance Imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to its well-characterized and highly conserved structure, as well as its relative abundance in metabolically active cells, bacterial 16S rRNA sequence plays an important role in microbial identification. In this work, a biosensing strategy has been developed for simultaneous detection of 16S rRNA analytes of three pathogenic bacterial strains: Legionella pneumophila, Pseudomonas aeruginosa, and Salmonella typhimurium . Surface plasmon resonance imaging (SPRi) was used as a detection technique coupled with DNA probe sandwich assemblies and gold nanoparticles (GNPs) for signal amplification. The targets 16S rRNA were selectively captured at the interface of the biosensor by surface-bound DNA probes through a hybridization process. GNP-grafted DNA detection probes were then introduced and were hybridized with a defined 16S rRNA region on the long DNA–RNA sandwich assemblies, resulting in a significant increase of the SPR signal. The results demonstrated the successful implementation of this strategy for detecting 16S rRNA sequences in total RNA mixed samples extracted from the three pathogenic strains at a concentration down to 10 pg mL –1 with a large dynamic range of 0.01–100 ng mL –1 and high selectivity. Since no particular optimization of the probe design was applied, this method should be relatively easy to adapt for quantification of a wide range of bacteria in various liquids.
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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 | 0.000 | 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) | 0.000 | 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 it