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Record W2120140934 · doi:10.1073/pnas.0303859101

Fibrin depletion decreases inflammation and delays the onset of demyelination in a tumor necrosis factor transgenic mouse model for multiple sclerosis

2004· article· en· W2120140934 on OpenAlexfundno aff
Katerina Akassoglou, Ryan A. Adams, Jan Bauer, Peter Mercado, Vivian Tseveleki, Hans Lassmann, Lesley Probert, Sidney Strickland

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2004
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersNational Institutes of HealthYork UniversityMultiple Sclerosis SocietyNational Center for Research ResourcesNational Institute of Neurological Disorders and StrokeNational Multiple Sclerosis Society
KeywordsFibrinInflammationMultiple sclerosisGenetically modified mouseImmunologyTumor necrosis factor alphaMedicinePathologyTransgeneNecrosisBiology

Abstract

fetched live from OpenAlex

In multiple sclerosis, in which brain tissue becomes permeable to blood proteins, extravascular fibrin deposition correlates with sites of inflammatory demyelination and axonal damage. To examine the role of fibrin in neuroinflammatory demyelination, we depleted fibrin in two tumor necrosis factor transgenic mouse models of multiple sclerosis, transgenic lines TgK21 and Tg6074. In a genetic analysis, we crossed TgK21 mice into a fibrin-deficient background. TgK21fib(-/-) mice had decreased inflammation and expression of major histocompatibility complex class I antigens, reduced demyelination, and a lengthened lifespan compared with TgK21 mice. In a pharmacologic analysis, fibrin depletion, by using the snake venom ancrod, in Tg6074 mice also delayed the onset of inflammatory demyelination. Overall, these results indicate that fibrin regulates the inflammatory response in neuroinflammatory diseases. Design of therapeutic strategies based on fibrin depletion could potentially benefit the clinical course of demyelinating diseases such as multiple sclerosis.

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)0.003
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 score0.365
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.123
GPT teacher head0.334
Teacher spread0.211 · 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

Citations125
Published2004
Admission routes1
Has abstractyes

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