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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".