On‐line sample preconcentration with chemical derivatization of bacterial biomarkers by capillary electrophoresis: A dual strategy for integrating sample pretreatment with chemical analysis
Bibliographic record
Abstract
Simple, selective yet sensitive methods to quantify low-abundance bacterial biomarkers derived from complex samples are required in clinical, biological, and environmental applications. In this report, a new strategy to integrate sample pretreatment with chemical analysis is investigated using on-line preconcentration with chemical derivatization by CE and UV detection. Single-step enantioselective analysis of muramic acid (MA) and diaminopimelic acid (DAP) was achieved by CE via sample enrichment by dynamic pH junction with ortho-phthalaldehyde/N-acetyl-L-cysteine labeling directly in-capillary. The optimized method resulted in up to a 100-fold enhancement in concentration sensitivity compared to conventional off-line derivatization procedures. The method was also applied toward the detection of micromolar levels of MA and DAP excreted in the extracellular medium of Escherichia coli bacterial cell cultures. On-line preconcentration with chemical derivatization by CE represents a unique approach for conducting rapid, sensitive, and high-throughput analyses of other classes of amino acid and amino sugar metabolites with reduced sample handling, where the capillary functions simultaneously as a concentrator, microreactor, and chiral selector.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".