Short Ligand, Cysteine-Modified Warnericin RK Antimicrobial Peptides Favor Highly Sensitive Detection of <i>Legionella pneumophila</i>
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Culture-based methods for the detection of Legionella pneumophila are prohibitively slow and frequently inadequate. The problem has been addressed with biosensing technology that employs a variety of ligands for the specific capture of bacteria. However, the limited success of the application of mammalian antibodies, aptamers, and nucleic acid-based probes for sensitive biosensing has generated growing interest in exploring alternative biosensing architectures, such as those based on antimicrobial peptides (AMP) that are known for their attractive therapeutic applications. We report on the successful employment of cysteine-modified warnericin RK AMP for the operation of a highly sensitive biosensor of L. pneumophila based on digital photocorrosion of GaAs/AlGaAs nanoheterostructures. The replacement of the relatively cumbersome procedure commonly applied for the attachment of antibodies to COOH-terminated mercaptohexadecanoic acid self-assembled monolayers has allowed for a significant reduction in the distance at which bacteria are immobilized above the biosensor surface. An important consequence of this approach is the attractive limit of detection of L. pneumophila estimated at 2 × 10 2 CFU/mL. The target bacteria were captured four times more efficiently than P. fluorescens, B. subtilis, and E. coli, which is highly promising for environmental monitoring.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".