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Mycophenolate mofetil (MMF): Firing at the atherosclerotic plaque from different angles?

2005· review· en· W2106111886 on OpenAlexaff
J.J.P. Kastelein, Allen Allison, Michael R. Hayden

Bibliographic record

VenueCardiovascular Research · 2005
Typereview
Languageen
FieldImmunology and Microbiology
TopicAtherosclerosis and Cardiovascular Diseases
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMycophenolateMedicineCardiologyInternal medicineTransplantation

Abstract

fetched live from OpenAlex

Atherosclerosis is characterized by a persistent, low-grade inflammatory state in which immune cell activation is inseparably linked to plaque formation and destabilization. The T-lymphocyte in particular has emerged as a pivotal player throughout the course of atherogenesis. As a consequence, the concept that immune modulation is a suitable target for cardiovascular prevention is currently an important focus of research. Mycophenolate mofetil (MMF) has emerged as a non-competitive inhibitor of inosine monophosphate dehydrogenase (IMPDH) that exerts cytostatic effects, particularly on proliferating T-lymphocytes. In addition, MMF has other immune-modulating effects, such as downregulation of the expression of adhesion molecules and attenuation of monocyte and macrophage responses. Given the added benefit that MMF is well tolerated, this immunosuppressive agent constitutes an attractive candidate for the modulation of inflammatory activation in atherogenesis. The present review provides an overview of the potential anti-atherogenic properties of MMF.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.163
GPT teacher head0.364
Teacher spread0.201 · 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

Citations49
Published2005
Admission routes1
Has abstractyes

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