Bibliographic record
Abstract
This review examines the evidence, both from experimental models and numerous clinical settings, that has suggested that suppression of acute allograft rejection correlates with a relative decrease in type-1 cytokine production and an increase in type-2 cytokine production. This correlation has spurred studies on cytokine gene polymorphisms, to assess evidence that certain cytokine-producing genotypes are associated with increased incidence of transplant rejection, and also an interest in the value of monitoring cytokine profiles post-transplantation in long-term follow-up of transplant patients. An appraisal is given of the potential for cytokine gene therapy in transplantation, using both dendritic cells (DC) and graft tissue itself as target cells. Along the same lines, gene-targeting of DCs to increase or decrease expression of molecules (CD40/CD80/CD86/CD200) believed to control the polarization of T cell development (and cytokine production) is discussed. The review concludes with consideration of the possibility that one future goal of the pharmaceutical industry will be to develop novel immunosuppressants with selective inhibitory action on the production of distinct cytokines.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".