New Immunosuppressants: Immunosuppression and Immunomodulation
Bibliographic record
Abstract
Immunosuppressive therapy can be used to prevent graft rejection and to treat autoimmune diseases. Recent advances in the understanding of this immune response have focused on the development of new immunosuppressive medications and new approaches to induction of immunological tolerance and reduction of late graft losses. In this overview, preclinical and clinical studies of the new immunosuppressive agents and their analogs are reviewed from the discovery of cyclosporine. More recently, certain classical immunosuppressants tacrolimus and sirolimus were well used to prevent acute rejection of transplanted organs and to ensure long-term survival of the allografts. However, some immunosuppressants have specific and significant toxic effects, so that drug combination therapy has been of great interest in addition to the introduction of novel small molecule agents, including mycophenolate mofetil; sirolimus analogs, SDZ RAD; 15-deoxyspergualin (DSG) and its analogs, FTY720; malononitrilamide analogs, FK778 and leflunimide; Sanglifehrins A; PG490-88; FK330 and 4-amino-analog of tetrahydrobiopterin of nitric oxide synthase inhibitors; genistein, baohuoside- 1 and apigenin of flavonoid family; Prostaglandin E2; CYP3A4, CYP3A5, and P-glycoprotein; vitamin E analogs, α-tocopheryl (PEG-1000) succinate (TPGS). A newer immunomodulation concept and their new drugs will also be described. Keywords: immunosuppressant, immunoregulation, transplantation, rejection, autoimmune diseases, fk, pg and baohuoside
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".