Surface-Enhanced Raman Spectroscopy (SERS) for the Characterization of Bacterial Isolates in Pus
Bibliographic record
Abstract
This study utilized SERS to characterize and identify Klebsiella pneumonia, Staphylococcus aureus, and Pseudomonas aeruginosa which were collected from pus and confirmed using 16S rRNA sequencing. These bacteria are typically cultured and isolated from human wounds. SERS peaks at 575, 629, 930, 1008, 1038, 1099, 1134, 1221, 1283, 1294, 1374, 1589, and 1707 cm−1 were shown to be distinguishing features of these strains. Principal component analysis (PCA) and partial least squares – discriminant analysis (PLS-DA) was applied to SERS spectral datasets. This study demonstrates that combining SERS with PCA and PLS-DA is effective for recognizing and distinguishing these bacterial isolates.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".