Selective Detection of Legionella pneumophila Serogroup 1 and 5 with a Digital Photocorrosion Biosensor Using Antimicrobial Peptide-Antibody Sandwich Strategy
Bibliographic record
Abstract
Rapid detection of Legionella pneumophila (L. pneumophila) is important for monitoring the presence of these bacteria in water sources and preventing the transmission of the Legionnaires’ disease. We report improved biosensing of L. pneumophila with a digital photocorrosion (DIP) biosensor functionalized with an innovative structure of cysteine-modified warnericin antimicrobial peptides for capturing bacteria that are subsequently decorated with anti-L. pneumophila polyclonal antibodies (pAbs). The application of peptides for the operation of a biosensing device was enabled by the higher bacterial-capture efficiency of peptides compared to other traditional ligands, such as those based on antibodies or aptamers. At the same time, the significantly stronger affinity of pAbs decorating the L. pneumophila serogroup-1 (SG-1) compared to serogroup-5 (SG-5) allowed for the selective detection of L. pneumophila SG-1 at 50 CFU/mL. The results suggest that the attractive sensitivity of the investigated sandwich method is related to the flow of an extra electric charge between the pAb and a charge-sensing DIP biosensor. The method has the potential to offer highly specific and sensitive detection of L. pneumophila as well as other pathogenic bacteria and viruses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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, 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".