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Record W3024063149 · doi:10.1038/s41467-020-15476-6

Chemotaxing neutrophils enter alternate branches at capillary bifurcations

2020· article· en· W3024063149 on OpenAlexaff
Xiao Wang, Mokarram Hossain, Ania Bogoslowski, Paul Kubes, Daniel Irimia

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsUniversity of Calgary
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesShriners Hospitals for ChildrenU.S. Department of Health and Human Services
KeywordsChemotaxisCell biologyCapillary actionIn vivoBiologyIn vitroImmunologyComputer scienceChemistryPhysicsGeneticsReceptor

Abstract

fetched live from OpenAlex

Upon tissue injury or microbial invasion, a large number of neutrophils converge from blood to the sites of injury or infection in a short time. The migration through a limited number of paths through tissues and capillary networks seems efficient and 'traffic jams' are generally avoided. However, the mechanisms that guide efficient trafficking of large numbers of neutrophils through capillary networks are not well understood. Here we show that pairs of neutrophils arriving closely one after another at capillary bifurcations migrate to alternating branches in vivo and in vitro. Perturbation of chemoattractant gradients and the increased hydraulic resistance induced by the first neutrophil in one branch biases the migration of the following neutrophil towards the other branch. These mechanisms guide neutrophils to efficiently navigate through capillary networks and outline the effect of inter-neutrophil interactions during migration on overall lymphocyte trafficking patterns in confined environments.

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.265
Teacher spread0.236 · 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

Citations37
Published2020
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicNeutrophil, Myeloperoxidase and Oxidative MechanismsFrench-language works237,207