Vascular tropism models of blood-borne microbial dissemination
Bibliographic record
Abstract
SUMMARY Similar to circulating tumour and immune cells, many blood-borne microbes preferentially “home” to specific vascular sites and tissues during hematogenous dissemination 1–5 . For many pathogens, the “postal codes” and mechanisms responsible for tissue-specific vascular tropism are unknown and have been challenging to unravel. Members of the Lyme disease Borreliella burgdorferi species complex infect a broad range of mammalian tissues and exhibit complex strain-, species- and host-specific tissue tropism patterns. Intravenous perfusion experiments and intravital microscopy studies suggest that heterogeneous tissue tropism properties may depend on tissue-specific differences in host and microbial molecules supporting vascular interaction and extravasation. However, interpreting these studies can be complicated because of the immune-protective moonlighting (multitasking) properties of many B. burgdorferi adhesins. Here, we investigated whether B. burgdorferi vascular interaction properties measured by live cell imaging and particle tracking in aorta, bladder, brain, joint and skin microvascular flow chamber models predict strain- and tissue-specific dissemination patterns in vivo These studies identified strain- and endothelial cell type-specific interaction properties that accurately predicted in vivo dissemination of B. burgdorferi to bladder, brain, joint and skin but not aorta, and indicated that dissemination mechanisms in all of these tissues are distinct. Thus, the ability to interact with vascular surfaces under physiological shear stress is a key determinant of tissue-specific tropism for Lyme disease bacteria. The methods and model systems reported here will be invaluable for identifying and characterizing the diverse, largely undefined molecules and mechanisms supporting dissemination of Lyme disease bacteria. These methods and models may be useful for studying tissue tropism and vascular dissemination mechanisms of other blood-borne microbes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".