Bibliographic record
Abstract
Mycophenolate mofetil (MMF), is the morpholinoethyl ester of mycophenolic acid (MPA). Though initially developed as an anti-rejection treatment, MMF is beginning to find application in more common immune-mediated diseases. MMF has been shown to be effective against transplant-associated vascular disease, lupus and other inflammatory diseases via multiple mechanisms in several animal models. MMF treatment blocks the proliferation of T cells and B cells, attenuates the production of autoreactive IgG and IgM, diminishes complement deposition, and reduces the production of multiple proinflammatory cytokines including TNF-α, IFN-γ, IL-2, IL-3, IL-4, IL-5, IL-6 and IL-12. It also increases production of the anti-inflammatory mediator IL-10. In addition, MMF reduces the infiltration of immune cells into sites of inflammation by interfering with the expression of cell-surface molecules critical for this process, including MHC class II, CD40, CD80, CD86, I-A, and ICAM-1. Additional mechanisms involving mannosylation and N-linked glycosylation of cell-surface molecules are only beginning to be investigated. This article will focus on the contribution of animal models of disease as investigational tools in the development of MMF as an immunomodulatory drug. The use of mice, rats, rabbits, monkeys, baboons and interspecific xenografts will be discussed.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".