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MIP-1α, MCP-1, GM-CSF, and TNF-α Control the Immune Cell Response That Mediates Rapid Phagocytosis of Myelin from the Adult Mouse Spinal Cord

2001· article· en· W4322377755 on OpenAlexafffund
Shalina S. Ousman, Samuel David

Bibliographic record

VenueJournal of Neuroscience · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsMcGill UniversityMontreal General Hospital
FundersCanadian Institutes of Health ResearchMultiple Sclerosis SocietyMultiple Sclerosis Society of Canada
KeywordsImmune systemMyelinTumor necrosis factor alphaImmunologyBiologyMicrogliaAxonInflammationCell biologyCentral nervous systemNeuroscience

Abstract

fetched live from OpenAlex

The slow immune response in the adult mammalian CNS results in slow myelin phagocytosis along degenerating white matter after injury. This has important consequences for axon regeneration because of the presence of axon growth inhibitors in myelin. In addition, abnormal immune cell responses in the CNS lead to demyelinating disease. Lysophosphatidylcholine (LPC) can induce an inflammatory response in the CNS, producing rapid demyelination without much damage to adjacent cells. In this study, we searched for the molecular switches that turn on this immune cell response. Using reverse transcription PCR analysis, we show that mRNA expression of macrophage inflammatory protein-1α (MIP-1α), macrophage chemotactic protein-1 (MCP-1), granulocyte macrophage-colony stimulating factor (GM-CSF), and tumor necrosis factor-α (TNF-α) in the spinal cord is rapidly and transiently upregulated after intraspinal injection of LPC. Neutralizing these signaling molecules with function-blocking antibodies suppresses recruitment of T-cells, neutrophils, and monocytes into the spinal cord, as well as significantly reduces the number of phagocytic macrophages and the demyelination induced by LPC. These findings will have important implications for CNS regeneration and demyelinating 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 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)0.001
metaresearch head score (Gemma)0.002
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 score0.031
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.038
GPT teacher head0.277
Teacher spread0.239 · 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

Citations133
Published2001
Admission routes2
Has abstractyes

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