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Record W2063249182 · doi:10.1002/jnr.20562

Microglia in health and disease

2005· review· en· W2063249182 on OpenAlexaff
Seung Up Kim, Jean de Vellis

Bibliographic record

VenueJournal of Neuroscience Research · 2005
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsMicrogliaImmune systemMultiple sclerosisNeuroscienceInflammationNeuroinflammationNeurodegenerationProinflammatory cytokineNeurotrophic factorsDiseaseImmunologyBiologyCentral nervous systemMedicinePathologyReceptor

Abstract

fetched live from OpenAlex

Microglia, one of three glial cell types in the central nervous system (CNS), play an important role as resident immunocompetent and phagocytic cells in the CNS in the event of injury and disease. It was del Rio Hortega in 1927 who determined that microglia belong a distinct glial cell type apart from astrocytes and oligodendrocytes, and since 1970s there has been wide recognition that microglia are immune effectors in the CNS that respond to pathological conditions and participate in initiation and progression of neurological disorders including Alzheimer's disease, Parkinson's disease, multiple sclerosis, and acquired immune deficiency syndrome dementia complex by releasing potentially cytotoxic molecules such as proinflammatory cytokines, reactive oxygen intermediates, proteinases and complement proteins. There is also evidence to suggest that microglia are capable of secreting neurotrophic or neuron survival factors upon activation via inflammation or injury. It is thus timely to review current status of knowledge on biology and immunology of microglia, and consider new directions of investigation on microglia in health and disease.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.384
GPT teacher head0.511
Teacher spread0.127 · 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 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

Citations661
Published2005
Admission routes1
Has abstractyes

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