Chemotaxis for enhanced immobilization of <i>Escherichia coli</i> and <i>Legionella pneumophila</i> on biofunctionalized surfaces of GaAs
Bibliographic record
Abstract
The authors have investigated the effect of chemotaxis on immobilization of bacteria on the surface of biofunctionalized GaAs (001) samples. Escherichia coli K12 bacteria were employed to provide a proof-of-concept of chemotaxis-enhanced bacterial immobilization, and then, these results were confirmed using Legionella pneumophila. The recognition layer was based on a self-assembled monolayer of thiol functionalized with specific antibodies directed toward E. coli or L. pneumophila, together with the enzyme beta-galactosidase (β-gal). The authors hypothesized that this enzyme together with its substrate lactose would produce a gradient of glucose which would attract bacteria toward the biochip surface. The chemotaxis effect was monitored by comparing the number of bacteria bound to the biochip surface with and without attractant. The authors have observed that β-gal plus lactose enhanced the immobilization of bacteria on our biochips with a higher effect at low bacterial concentrations. At 100 and 10 bacteria/ml, respectively, for E. coli and L. pneumophila, the authors observed up to 11 and 8 times more bacteria bound to biochip surfaces assisted with the chemotaxis effect in comparison to biochips without chemotaxis. At 10(4) bacteria/ml, the immobilization enhancement rate did not exceed two times.
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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 | 0.000 | 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 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".