Fungal Infections in Renal Transplant Patients
Bibliographic record
Abstract
Organ transplantation has always been considered to be the standard therapeutic interventions in patients with end-stage organ failure. In 2008, more than 29,000 organ transplants were performed in US. Survival rates among transplant recipients have greatly improved due to better understanding of transplant biology and more effective immunosuppressive agents. After transplant, the extent of the immune response is influenced by the amount of interleukin 2 (IL-2) being produced by the T-helper cells. Transplant immunosuppressive therapy primarily targets T cell-mediated graft rejection. Calcineurin inhibitor, which includes cyclosporine, pimecrolimus and tacrolimus, impairs calcineurin-induced up-regulation of IL-2 expression, resulting in increased susceptibility to invasive fungal diseases. This immunosuppressive state allows infectious complication, leading to a high mortality rate. Currently, overall mortality due to invasive fungal infections (IFIs) in solid organ transplant recipients ranges between 25% and 80%. The risk of IFI following renal transplant is associated with the dosage of immunosuppressive agents given, environmental factors and post-transplant duration. Most fungal infections occur in the first 6 months after transplant because of the use of numerous immunosuppressors. Candida spp. and Cryptococcus spp. are the yeasts most frequently isolated, while most frequent filamentous fungi (molds) isolated are Aspergillus spp. The symptoms of systemic fungal infections are non-specific and early detection of fungal infections and proper therapy are important in improving survival and reducing mortality. This article will provide an insight on the risk factors and clinical presentation, compare variation in treatment of IFIs in renal transplant patients, and evaluate the role of prophylactic therapy in this group of patients. We also report the course and management of two renal transplant recipients admitted to Staten Island University Hospital, both of whom developed pulmonary complications secondary to Aspergillus infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".