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Record W4383753275 · doi:10.1073/pnas.2302892120

Sex and interferon gamma signaling regulate microglia migration in the adult mouse cortex in vivo

2023· article· en· W4383753275 on OpenAlexafffund
Roobina Boghozian, Sorabh Sharma, Kamal Narayana, Manjinder S. Cheema, Craig E. Brown

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMicrogliaIn vivoNeuroscienceBiologyInterferon gammaReceptorPopulationInterferonImmunologyCytokineMedicineInflammationGenetics

Abstract

fetched live from OpenAlex

Although microglia possess the unique ability to migrate, whether mobility is evident in all microglia, is sex dependent, and what molecular mechanisms drive this, is not well understood in the adult brain. Using longitudinal in vivo two-photon imaging of sparsely labeled microglia, we find a relatively small population of microglia (~5%) are mobile under normal conditions. Following injury (microbleed), the fraction of mobile microglia increased in a sex-dependent manner, with male microglia migrating significantly greater distances toward the microbleed relative to their female counterparts. To understand the signaling pathways involved, we interrogated the role of interferon gamma (IFNγ). Our data show that in male mice, stimulating microglia with IFNγ promotes migration whereas inhibiting IFNγ receptor 1 signaling inhibits them. By contrast, female microglia were generally unaffected by these manipulations. These findings highlight the diversity of microglia migratory responses to injury, its dependence on sex and the signaling mechanisms that modulate this behavior.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.049
GPT teacher head0.292
Teacher spread0.243 · 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

Citations23
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of Sciences→Same topicNeuroinflammation and Neurodegeneration Mechanisms→French-language works237,207→