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Record W2080652296 · doi:10.1186/1742-2094-10-89

Reduction of microglial activity in a model of multiple sclerosis by dipyridamole

2013· article· en· W2080652296 on OpenAlexafffund
Scott Sloka, Luanne M. Metz, Walter Hader, Yves Starreveld, V. Wee Yong

Bibliographic record

VenueJournal of Neuroinflammation · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchFondation pour la Recherche MédicaleMultiple Sclerosis SocietyBiogenMultiple Sclerosis Society of CanadaHeart and Stroke Foundation of Canada
KeywordsDipyridamoleMultiple sclerosisNeurologyNeuroscienceMedicineMicrogliaPsychologyCardiologyInternal medicineImmunologyInflammation

Abstract

fetched live from OpenAlex

BACKGROUND: Despite extensive and persistent activation of microglia in multiple sclerosis (MS), microglia inhibitors have not yet been identified for treatment of the disorder. We sought to identify medications already in clinical use that could inhibit the activation of microglia. On the basis of the reported inhibitory effects of dipyridamole on phosphodiesterase activity that result in the production of various anti-inflammatory outcomes, we selected it for study. Dipyridamole is used clinically for secondary prevention in stroke. In this study, dipyridamole was examined using microglia in culture and in the mouse model of MS, experimental autoimmune encephalomyelitis (EAE). RESULTS: We found that dipyridamole attenuated the elevation of several cytokines and chemokines in human microglia caused by Toll-like receptor stimulation. Morphological characteristics of activated microglia in culture were also normalized by dipyridamole. In mice, dipyridamole decreased the clinical severity of EAE and reduced microglial activity and other histological indices of EAE in the spinal cord. CONCLUSIONS: Dipyridamole is an inhibitor of microglia activation and may have a role in MS and other neurological conditions to attenuate microglial activity.

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)0.001
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 score0.089
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.055
GPT teacher head0.238
Teacher spread0.183 · 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

Citations51
Published2013
Admission routes2
Has abstractyes

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