Electrochemical Biosensor Array for the Identification of Microorganisms Based on Lectin−Lipopolysaccharide Recognition
Bibliographic record
Abstract
Rapid identification of bacterial strains remains a well-known problem in applied medicine and, for viable pathogens, is an important diagnostic goal. We have investigated an electrochemical biosensor array, in which transduction is based on respiratory cycle activity measurements, where the microorganism's native respiratory chain is interrupted with non-native external oxidants. The selective biochemical recognition agents employed in this study are lectins that, once immobilized, recognize and bind to cell surface lipopolysaccharides. Porous membranes with different surface properties were examined as potential immobilization supports for these lectins. Optimizations performed using concanavalin A and E. coli JM105 show that immobilization methods involving pre-activated membranes significantly reduce the time required to create a functional lectin layer on the membrane surface. Overall, we found general agreement between agglutination test results and the electrochemical assessment of lectin-cell binding. Chronocoulometric measurements were made for cells captured on lectin-modified Immunodyne ABC membranes physically affixed to Pt working electrodes. This lectin-based sensor array was exposed to viable cells of gram-negative and gram-positive bacteria as well as yeast, and chronocoulometric measurements were used to generate a pattern of responses for each organism toward each lectin. Principal component analysis was used to classify the chronocoulometric results for the different microbial strains. With this new method, six microbial species (Baccilus cereus, Staphylococcus aureus, Proteus vulgaris, Escherichia coli, Enterobacter aerogenes, Saccharomyces cerevisiae) were readily distinguished.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".