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Record W4411619679 · doi:10.1002/advs.202503722

Neutrophil Mobilization Triggers Microglial Functional Change to Exacerbate Cerebral Ischemia‐Reperfusion Injury

2025· article· en· W4411619679 on OpenAlexaff
Huijuan Jin, Zhifang Li, Senwei Tan, Qinghui Xiao, Qingcan Li, Jiao Ye, Yifan Zhou, Qiang Liu, Bijoy K. Menon, Bo Hu

Bibliographic record

VenueAdvanced Science · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsMicrogliaNeuroinflammationMedicineParenchymaIschemiaImmunologyPhagocytosisPathogenesisNeuroscienceInflammationPathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Acute ischemic stroke is a leading cause of mortality and disability worldwide. Neuroinflammation following ischemia-reperfusion plays a critical role in the disease's pathogenesis. Neutrophil aggregation and clearance within the brain parenchyma influence neuroinflammatory damage during ischemic stroke. Microglia-mediated phagocytosis plays a pivotal role in mitigating neuroinflammation and promoting brain parenchyma recovery. However, the mechanisms underlying the cross-talk between neutrophils and microglia remain poorly understood. Here, this study demonstrates that neutrophils can trigger microglial functional change to inhibit microglial phagocytosis and promote pyroptosis, which is regulated by neutrophil-derived myeloid-related protein 14. Additionally, interleukin-1β released by pyroptotic microglia further upregulates myeloid-related protein 14 expression and facilitates neutrophil mobilization from the bone marrow, establishing a self-sustaining inflammatory loop. Therefore, neutrophils accumulate in the brain parenchyma and further exacerbate microglial neuroinflammation in the ischemic brain. These findings reveal a previously unknown interaction between neutrophils and microglia after acute ischemic stroke and suggest that targeting myeloid-related protein 14 may provide a novel therapeutic strategy for ischemic stroke therapy.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.298
Teacher spread0.264 · 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

Citations16
Published2025
Admission routes1
Has abstractyes

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