MétaCan
Menu
Back to cohort

Inflammation in Ischemic Brain Injury: Timing Is Important

2006· review· en· W1982308497 on OpenAlexaff
Jasna Križ

Bibliographic record

VenueCritical Reviews in Neurobiology · 2006
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsCentre hospitalier de l'Université LavalWilfrid Laurier University
Fundersnot available
KeywordsInflammationNeuroprotectionMedicineIschemiaMicrogliaChemokineNeuroscienceBrain ischemiaImmune systemImmunologyPharmacologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Inflammation is a defense reaction against diverse insults that serves to remove noxious agents and to limit their detrimental effects. There is increasing evidence that post-ischemic inflammation plays an important role in brain ischemia. However, whether inflammatory processes are deleterious or beneficial to recovery is presently a matter of debate and controversy. Experimentally and clinically, stroke is followed by an acute and a prolonged inflammatory response characterized by the production of inflammatory cytokines, leukocyte and monocyte infiltration in the brain, and the activation of resident glial cells. These events may contribute to ischemic brain injury. Several groups report conflicting results regarding the role of inflammation and effects of anti-inflammatory treatments in cerebral ischemia. Experimental studies employing knockout mice for different cytokines and chemokines provide only partial answers. This highlights the importance of clarifying the role of the immune response in pathological changes at the site of ischemic lesions in the brain. Here, we describe dual effects of the brain's inflammatory response and new evidence for a neuroprotective role of proliferating microglial cells in ischemia. In addition, we discuss a potential role of post-ischemic inflammation in brain regeneration and modulation of synaptic plasticity.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-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 score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.135
GPT teacher head0.413
Teacher spread0.279 · 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

Citations240
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCritical Reviews in NeurobiologySame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207