Bibliographic record
Abstract
The mammalian target of rapamycin (mTOR) inhibitor drugs rapamycin (sirolimus) and everolimus have undergone extensive clinical trials for a variety of organ grafts and have been licensed for use in human transplantation. Uniquely, they block the function of a master chemical switch, the protein kinase mTOR, which integrates the multiple biochemical pathways that are necessary for growth factors to induce cell proliferation. Many of these pathways are abnormal in tumorigenesis, and the role of mTOR and its inhibitors in cancer treatment is undergoing intense investigation. There are pharmacokinetic differences between the rapamycins, however, in all major respects, their actions are the same. They show synergy with the calcineurin inhibitors in antirejection effects but also augment the nephrotoxicity of both cyclosporine and tacrolimus. They allow marked reduction in calcineurin inhibitor drug doses, which also reduces the nephrotoxicity of the combinations. In clinical trials in kidney, heart, lung, small bowel, pancreas, islet and liver transplantation in combination with cyclosporine and tacrolimus, rejection rates are equivalent or superior to those achieved with mycophenolate mofetil combinations. Despite this, its clinical usage remains limited. The side-effect profile, especially elevations in serum lipids and nephrotoxicity when administered in combination with calcineurin inhibitors, are the major factors. However, these drugs are finding an increasing place in other areas of medicine, including incorporation into endovascular coronary artery and peripheral arterial stents and in cancer therapy. Their ability to reduce fibrosis and neovascularization suggests other areas of potential use.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".