Metabolite microextraction on surface-enhanced Raman scattering nanofibres and D2O probing accelerate antibiotic susceptibility testing
Bibliographic record
Abstract
Rapid antibiotic susceptibility tests (AST) are vital for the effective treatment of disease, necessitating the development of analytical tools to address unmet needs in healthcare. Leveraging the sensitivity of plasmonic nanosensors and isotopic labelling has the potential to accelerate AST. Here, we developed surface-enhanced Raman scattering (SERS)-based nanofibre sensors and heavy water [deuterium oxide (D 2 O)] labelling (SERS-DIP) for detecting the minimum inhibitory concentration (MIC) and AST for trimethoprim (TMP) against E. coli . SERS-DIP rapidly detected the MIC of TMP for the susceptible strain in 2 h. TMP-resistant cells retained the metabolic activity regardless of TMP levels, confirming the resistance phenotype. Kinetic analysis of D uptake by resistant cells treated with TMP (2 × MIC) revealed increasing D levels proportional to peak redshifts over time, confirmed by machine learning-driven data exploration. Our results demonstrate the utility of nanofibre-enabled SERS-DIP for robust AST, uncovering new spectral biomarkers that may impact clinical medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".