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Record W3159957088 · doi:10.1101/2021.05.05.442761

Vascular tropism models of blood-borne microbial dissemination

2021· preprint· en· W3159957088 on OpenAlexafffund
Anna E. Boczula, Amy Ly, Rhodaba Ebady, Janet Cho, Zoha Anjum, Nataliya Zlotnikov, Henrik Persson, Tanya Odisho, Craig A. Simmons, Tara J. Moriarty

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsTropismBiologyBorrelia burgdorferiTissue tropismExtravasationImmune systemCell typeCell biologyPathologyCellImmunologyMedicineGenetics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.210
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2021
Admission routes2
Has abstractyes

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