Investigation of the Utility of Complementary Electrochemical Detection Techniques to Examine the in Vitro Affinity of Bacterial Flagellins for a Toll-Like Receptor 5 Biosensor
Bibliographic record
Abstract
An initial investigation of the fabrication of a novel biosensor utilizing toll-like receptor 5 (TLR5) has been conducted. The detection assay using this sensor platform has been carried out using two complementary electrochemical techniques. The electrochemical properties of the modified bare gold surface following TLR5 immobilization were characterized. The electrochemical response to changes in the sensor film resistance and electron charge-transfer permittivity triggered by independent exposures to flagellins from Salmonella typhimurium (S. typhimurium) and Bacillus subtilis (B. subtilis) were examined and observed. The quantified film resistance data gathered using electrochemical impedance spectroscopy (EIS) over a macroscopic scale are in significant agreement with the corresponding electron charge-transfer permittivity measured locally by scanning electrochemical microscopy (SECM). Unlike other sensors that exploit pathogen recognition elements, TLR5 biosensors have the potential to carry out broad-spectrum detection of flagellated bacterial pathogens in near real time. This broad-spectrum detection platform is a significant step toward the development of fast, inexpensive clinical tools for early warning diagnoses and immediate on-site treatment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".