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Record W2514394561 · doi:10.1016/j.celrep.2016.08.013

Biomechanics of Borrelia burgdorferi Vascular Interactions

2016· article· en· W2514394561 on OpenAlexafffund
Rhodaba Ebady, Alexandra F. Niddam, Anna E. Boczula, Yae Ram Kim, Nupur Gupta, Tian Tang, Tanya Odisho, Hui Zhi, Craig A. Simmons, Jon T. Skare, Tara J. Moriarty

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsBorrelia burgdorferiBiologyBacterial adhesinImmune systemBorreliaCell biologyMicrobiologyImmunologyGeneticsGeneVirulence

Abstract

fetched live from OpenAlex

Systemic dissemination of microbes is critical for progression of many infectious diseases and is associated with most mortality due to bacterial infection. The physical mechanisms mediating a key dissemination step, bacterial association with vascular endothelia in blood vessels, remain unknown. Here, we show that endothelial interactions of the Lyme disease spirochete Borrelia burgdorferi under physiological shear stress mechanistically resemble selectin-dependent leukocyte rolling. Specifically, these interactions are mediated by transfer of mechanical load along a series of adhesion complexes and are stabilized by tethers and catch bond properties of the bacterial adhesin BBK32. Furthermore, we found that the forces imposed on adhesive bonds under flow may be small enough to permit active migration driven by bacterial flagellar motors. These findings provide insight into the biomechanics of bacterial-vascular interactions and demonstrate that disseminating bacteria and circulating host immune cells share widely conserved mechanisms for interacting with endothelia under physiological 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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 designBench or experimental
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

Citations62
Published2016
Admission routes2
Has abstractyes

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