Impedimetric Detection of Pathogenic Gram-Positive Bacteria Using an Antimicrobial Peptide from Class IIa Bacteriocins
Bibliographic record
Abstract
Real-time, label-free detection of Gram-positive bacteria with high selectivity and sensitivity is demonstrated using an interdigitated impedimetric array functionalized with naturally produced antimicrobial peptide from class IIa bacteriocins. The antimicrobial peptide, leucocin A, was chemically synthesized and covalently immobilized on interdigitated gold microelectrodes via the interaction between the C-terminal carboxylic acid of the peptide and free amines of a preattached thiolated linker. Exposing the peptide sensor to various concentrations of Gram-positive bacteria generated reproducible impedance spectra that detected peptide-bacteria interactions at a concentration of 1 cell/μL. The peptide sensor also selectively detected Listeria monocytogenes from other Gram-positive strains at a concentration of 10(3) cfu mL(-1). The study highlights that short peptide ligands from bacteriocin class offer high selectivity in bacterial detection and can be used in developing a robust, portable biosensor device to efficiently detect pathogenic Gram-positive bacteria in food 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".