Real-Time High Resolution 3D Imaging of the Lyme Disease Spirochete Adhering to and Escaping from the Vasculature of a Living Host
Bibliographic record
Abstract
Pathogenic spirochetes are bacteria that cause a number of emerging and re-emerging diseases worldwide, including syphilis, leptospirosis, relapsing fever, and Lyme borreliosis. They navigate efficiently through dense extracellular matrix and cross the blood-brain barrier by unknown mechanisms. Due to their slender morphology, spirochetes are difficult to visualize by standard light microscopy, impeding studies of their behavior in situ. We engineered a fluorescent infectious strain of Borrelia burgdorferi, the Lyme disease pathogen, which expressed green fluorescent protein (GFP). Real-time 3D and 4D quantitative analysis of fluorescent spirochete dissemination from the microvasculature of living mice at high resolution revealed that dissemination was a multi-stage process that included transient tethering-type associations, short-term dragging interactions, and stationary adhesion. Stationary adhesions and extravasating spirochetes were most commonly observed at endothelial junctions, and translational motility of spirochetes appeared to play an integral role in transendothelial migration. To our knowledge, this is the first report of high resolution 3D and 4D visualization of dissemination of a bacterial pathogen in a living mammalian host, and provides the first direct insight into spirochete dissemination in vivo.
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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 | 0.001 | 0.000 |
| 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".