A Live Cell Imaging Microfluidic Model for Studying Extravasation of Bloodborne Bacterial Pathogens
Bibliographic record
Abstract
Bacteria that migrate (extravasate) out of the bloodstream during vascular dissemination can cause secondary infections in many tissues and organs, including the brain, heart, liver, joints, and bone with clinically serious and sometimes fatal outcomes. The mechanisms by which bacteria extravasate through endothelial barriers in the face of blood flow-induced shear stress are poorly understood, in part because individual bacteria are rarely observed traversing endothelia in vivo, and in vitro model systems inadequately mimic the vascular environment. To enable the study of bacterial extravasation mechanisms, we developed a transmembrane microfluidics device mimicking human blood vessels. Fast, quantitative, three-dimensional live cell imaging in this system permitted single-cell resolution measurement of the Lyme disease bacterium Borrelia burgdorferi transmigrating through monolayers of primary human endothelial cells under physiological shear stress. This cost-effective, flexible method was 10,000 times more sensitive than conventional plate reader-based methods for measuring transendothelial migration. Validation studies confirmed that B. burgdorferi transmigrate actively and strikingly do so at similar rates under static and physiological flow conditions. This method has significant potential for future studies of B. burgdorferi extravasation mechanisms, as well as the transendothelial migration mechanisms of other disseminating bloodborne pathogens.
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) | 0.001 | 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".