Electrochemical Detection of <i>Borrelia burgdorferi</i> Using a Biomimetic Flow Cell System
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Lyme disease, caused by infection with pathogenic Borrelia bacteria, has emerged as a pervasive illness throughout North America and many other regions of the world in recent years, owing in part to climate-mediated habitat expansion of the tick vectors. Standard diagnostic testing has remained largely unchanged over the past several decades and is indirect, relying on detection of antibodies against the Borrelia pathogen, rather than detection of the pathogen itself. The development of new rapid, point-of-care tests for Lyme disease that directly detects the pathogen could drastically improve patient health by enabling faster and more frequent testing that could better inform patient treatment. Here, we describe a proof-of-concept electrochemical sensing approach to the detection of the Lyme disease-causing bacteria, which utilizes a biomimetic electrode to interact with the Borrelia bacteria that induce impedance alterations. In addition, the catch-bond mechanism between bacterial BBK32 protein and human fibronectin protein, which exhibits improved bond strength with increased tensile force, is tested within an electrochemical injection flow-cell to achieve Borrelia detection under shear stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".