High throughput LSPR and SERS analysis of aminoglycoside antibiotics
Bibliographic record
Abstract
Aminoglycoside antibiotics are used in the treatment of infections caused by Gram-negative bacteria, and are often dispensed only in severe cases due to their adverse side effects. Patients undergoing treatment with these antibiotics are therefore commonly subjected to therapeutic drug monitoring (TDM) to ensure a safe and effective personalised dosage. The ability to detect these antibiotics in a rapid and sensitive manner in human fluids is therefore of the utmost importance in order to provide effective monitoring of these drugs, which could potentially allow for a more widespread use of this class of antibiotics. Herein, we report on the detection of various aminoglycosides, by exploiting their ability to aggregate gold nanoparticles. The number and position of the amino groups of aminoglycoside antibiotics controlled the aggregation process. We investigated the complementary techniques of surface enhanced Raman spectroscopy (SERS) and localized surface plasmon resonance (LSPR) for dual detection of these aminoglycoside antibiotics and performed an in-depth study of the feasibility of carrying out TDM of tobramycin using a platform amenable to high throughput analysis. Herein, we also demonstrate dual detection of tobramycin using both LSPR and SERS in a single platform and within the clinically relevant concentration range needed for TDM of this particular aminoglycoside. Additionally we provide evidence that tobramycin can be detected in spiked human serum using only functionalised nanoparticles and SERS analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".