Granulocyte colony-stimulating factor, granulocyte–macrophage colony-stimulating factor, and other immunomodulatory therapies for the treatment of infectious diseases in solid organ transplant recipients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Infections continue to cause significant morbidity and mortality in SOT recipients despite major advances in immunosuppressive and antimicrobial regimens. Immunomodulatory cytokines provide a potential means to augment the host immune response to infection. This review will focus on cytokine therapy for the prophylaxis and treatment of infections in solid organ transplant recipients, and will speculate on the potential for further advances in the field. RECENT FINDINGS: In kidney and liver transplant recipients, granulocyte colony-stimulating factor (G-CSF) has been used successfully to reverse ganciclovir-induced neutropenia or cytomegalovirus-induced neutropenia. Although G-CSF also reversed corticosteroid-induced suppression of the neutrophil respiratory burst in vitro, prophylactic G-CSF failed to reduce infections or mortality in nonneutropenic solid organ transplant recipients. Published clinical experience with granulocyte-macrophage colony-stimulating factor (GM-CSF) in this population has been limited to case reports and a small case series, whereas the use of macrophage colony-stimulating factor (M-CSF) or interferon-gamma (IFN-gamma) has not been systematically investigated in controlled clinical trials. SUMMARY: Despite encouraging results in vitro and in preclinical models, immunomodulatory cytokines have not met expectations when administered to SOT recipients. Nonetheless, the principle of selective enhancement of innate immunity for the prevention and treatment of infections in this patient population has promise and warrants further study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".