Virulent Factor‐Targeted Point‐of‐Care Biosensor for Detection of Staphylococcus Aureus Infections
Bibliographic record
Abstract
Abstract Rapid detection of pathogenic bacteria like Staphylococcus aureus ( S. aureus ) is crucial for timely diagnosis and infection control. Aureolysin (Aur), an extracellular metalloprotease involved in S. aureus pathogenesis, is a promising biomarker. This study presents a rapid, low‐cost, label‐free electrochemical immunosensor for aureolysin detection using antibody‐gold (Ab‐Au) bioconjugates. Anti‐aureolysin antibodies are immobilized on gold nanospikes via 1‐Ethyl‐3‐(3‐dimethylaminopropyl)carbodiimide / N‐Hydroxysuccinimide (EDC/NHS) chemistry and screen‐printed gold electrodes (SPGEs). The detection relied on changes in peak current from antigen‐antibody complex formation, measured through differential pulse voltammetry (DPV). Selectivity tests confirmed the sensor's specificity for S. aureus , with no cross‐reactivity against Escherichia coli or Pseudomonas aeruginosa . A strong linear correlation (R 2 = 0.9739) between peak current and logarithmic S. aureus concentrations is observed, with a detection limit of 5 pg·mL⁻¹ in buffer and 2 Colony‐forming unit (CFU) mL⁻¹ in bacterial cultures. The sensor also detected S. aureus in biofilms, highlighting its potential for real‐world use. Offering rapid detection within 1h, high sensitivity, and specificity, this immunosensor is a promising point‐of‐care tool for S. aureus detection in clinical settings. This approach greatly enhances the sensor's effectiveness in real‐world clinical applications, where biofilm formation often complicates diagnosis and treatment.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".