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Suppressing Interferon-γ Stimulates Microglial Responses and Repair of Microbleeds in the Diabetic Brain

2018· article· en· W2892381183 on OpenAlexafffund
Stephanie L. Taylor, Eslam M. F. Mehina, Emily R. White, Patrick Reeson, Kevin Yongblah, Kristian P. Doyle, Craig E. Brown

Bibliographic record

VenueJournal of Neuroscience · 2018
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 CanadaHeart and Stroke Foundation of Canada
KeywordsMicrogliaInflammationMedicineDiabetes mellitusDexamethasoneAngiogenesisImmune systemTumor necrosis factor alphaIntravital microscopyBrain damageImmunologyEndocrinologyInternal medicineMicrocirculation

Abstract

fetched live from OpenAlex

Microcirculatory damage is a common complication for those with vascular risk factors, such as diabetes. To resolve vascular insults, the brain's immune cells (microglia) must rapidly envelop the site of injury. Currently, it is unknown whether Type 1 diabetes, a condition associated with chronic immune system dysfunction, alters microglial responses to damage and what mechanisms are responsible. Usingin vivotwo-photon microscopy in adult male mice, we show that microglial envelopment of laser-induced cerebral microbleeds is diminished in a hyperglycemic mouse model of Type 1 diabetes, which could not be fully rescued with chronic insulin treatment. Microglia were important for vessel repair because reduced microglial accumulation in diabetic mice or near-complete depletion in healthy controls was associated with greater secondary leakage of the damaged vessel. Broadly suppressing inflammation with dexamethasone in diabetic mice but not healthy controls, significantly enhanced microglial responses to microbleeds and attenuated secondary vessel leakage. These enhancements were associated with changes in IFN-γ signaling because dexamethasone suppressed abnormally high levels of IFN-γ protein levels in brain and blood serum of diabetic mice. Further, blocking IFN-γ in diabetic mice with neutralizing antibodies restored normal microglial chemotaxic responses and purinoceptorP2ry12gene expression, as well as mitigated secondary leakage. These results suggest that abnormal IFN-γ signaling disrupts microglial function in the diabetic brain, and that immunotherapies targeting IFN-γ can stimulate microglial repair of damaged vessels. SIGNIFICANCE STATEMENTAlthough Type 1 diabetes is an established risk factor for vascular complications, such as microbleeds, and is known to hinder wound healing in the body, no study has examined how diabetes impacts the brain's innate immune reparative response (involving cells called microglia) to vascular injury. Here we show that microglial responses to brain microbleeds were diminished in diabetic animals, which also exacerbated secondary leakage from damaged vessels. These impairments were related to abnormally high levels of the proinflammatory cytokine IFN-γ because reducing IFN-γ with immunosuppressant drugs or blocking antibodies helped restore normal microglial responses and repair of damaged vessels. These data highlight the use of IFN-γ modulating therapeutics to enhance vascular repair in at-risk populations.

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 score0.002

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 integrity0.0000.001
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 head0.045
GPT teacher head0.305
Teacher spread0.259 · 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".

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Citations46
Published2018
Admission routes2
Has abstractyes

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