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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".