Detection of Legionella pneumophila with antimicrobial peptide-based GaAs/AlGaAs biosensor
Bibliographic record
Abstract
Biosensor-based detection of pathogenic bacteria has gained attention since it could be fast, portable, cost effective and potentially easy to use. In this study, we investigated the detection of L. pneumophila using an antimicrobial peptide (AMP) and antibody (Ab) functionalized GaAs/AlGaAs biochips. The AMP attachment on GaAs surface was evaluated using Fourier-transform infrared spectroscopy (FTIR) and atomic force microscopy (AFM). The peptide-related absorbance bands in IR (1588 cm-1, 1653 cm-1, and 1734 cm-1) suggest the successful immobilization of AMP on GaAs. The bacterial capture efficiency/affinity on GaAs surface was evaluated for several peptides such as warnericin RK, clavanin, parasin, magainin, melittin and it was observed that the warnericin RK obtained ~4 times higher capture efficiency compared to the other peptides. We successfully detected L. pneumophila using AMP, as well as Ab conjugated GaAs/AlGaAs biosensors. The AMP functionalized biosensors, however, allowed higher sensitivity compared to Ab based bioarchitecture. The proposed AMP functionalized GaAs/AlGaAs biosensor is attractive for rapid and sensitive detection of L. pneumophila in water samples.
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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) | 0.001 | 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".