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Record W4411505752 · doi:10.1126/science.adx0043

Neuroinflammation across neurological diseases

2025· review· en· W4411505752 on OpenAlexaff
Fu‐Dong Shi, V. Wee Yong

Bibliographic record

VenueScience · 2025
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNeuroinflammationMicrogliaNeurodegenerationNeuropathologyNeuroscienceMultiple sclerosisStroke (engine)MedicineImmune systemDiseasePsychologyInflammationImmunologyPathology

Abstract

fetched live from OpenAlex

The brain's response to injury includes the activation of intrinsic microglia and the influx of leukocytes, collectively constituting neuroinflammation, the "flame" of the brain. Although details differ and matter, neuroinflammation exacerbating neurodegeneration has similarities across multiple sclerosis and other neurological disorders, such as stroke and neurodegenerative diseases. Thus, lessons from successful disease-modifying therapies in multiple sclerosis may provide insights into strategies for modulating neuroinflammation and reducing neural injury in other neurological conditions. In this Review, we discuss these lessons and potential strategies for counteracting neuroinflammation, including taming the microglia-orchestrated brain immune responses that contribute to progressing neuropathology.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.076
GPT teacher head0.394
Teacher spread0.318 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations178
Published2025
Admission routes1
Has abstractyes

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