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Record W4376642360 · doi:10.1242/jcs.260768

Nuclear segmentation facilitates neutrophil migration

2023· article· en· W4376642360 on OpenAlexafffund
Connie Shen, Eva Mulder, Wiebe Buitenwerf, Jérémy Postat, Aron Jansen, Matthijs Kox, Judith N. Mandl, Nienke Vrisekoop

Bibliographic record

VenueJournal of Cell Science · 2023
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchRadboud Universitair Medisch CentrumRadboud UniversiteitMcGill University
KeywordsBiologyCell biologyImmune systemCell nucleusCell migrationImmunologyFunction (biology)NucleusCellGenetics

Abstract

fetched live from OpenAlex

Neutrophils are among the fastest-moving immune cells. Their speed is critical to their function as 'first responder' cells at sites of damage or infection, and it has been postulated that the unique segmented nucleus of neutrophils functions to assist their rapid migration. Here, we tested this hypothesis by imaging primary human neutrophils traversing narrow channels in custom-designed microfluidic devices. Individuals were given an intravenous low dose of endotoxin to elicit recruitment of neutrophils into the blood with a high diversity of nuclear phenotypes, ranging from hypo- to hyper-segmented. Both by cell sorting of neutrophils from the blood using markers that correlate with lobularity and by directly quantifying the migration of neutrophils with distinct lobe numbers, we found that neutrophils with one or two nuclear lobes were significantly slower to traverse narrower channels, compared to neutrophils with more than two nuclear lobes. Thus, our data show that nuclear segmentation in primary human neutrophils provides a speed advantage during migration through confined spaces.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.092
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.323
Teacher spread0.296 · 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 teacher head, 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

Citations13
Published2023
Admission routes2
Has abstractyes

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