Myeloid-derived suppressor cells in transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature cells that are considered as potential therapeutic targets. Indeed, MDSCs have been shown to suppress immune responses to several types of tumor cells and blocking their suppressive activity may adequately enhance immune response against tumor antigens. On the contrary, the activity of MDSCs may be desirable in suppressing unwanted immune responses such as allograft rejection and might be involved as non-T regulatory cells in the induction and maintenance of transplantation tolerance. In addition, recent data reported that MDSC also control innate immune responses suggesting that MDSC might be important players in controlling ischemia reperfusion injury. RECENT FINDINGS: Herein, we focused on the few recent studies questioning the possible role played by MDSCs in solid-organ transplantation as well as in experimental models of graft versus host disease. SUMMARY: A growing body of evidence demonstrates that MDSCs are important physiological regulators of innate and adaptive immunity. Now, accumulating studies suggest that this concept can be transposed to the early and late transplantation immunity. Nevertheless, additional studies with mechanistic approaches in animal together with studies in human are required to better define their position and their interactions with immunosuppressive drugs.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".