Recent advances in understanding and managing infectious diseases in solid organ transplant recipients
Bibliographic record
Abstract
Background:Undergoing solid organ transplantation (SOT) exposes the recipient to various infectious risks, including possible transmission of pathogen by the transplanted organ, post-surgical infections, reactivation of latent pathogens, or novel infections. Recent advances:In the last few years, the emergence of Zika virus has raised concerns in the transplant community. Few cases have been described in SOT patients, and these were associated mainly with moderate disease and favorable outcome; the notable exception is a recent case of fatal meningo-encephalopathy in a heart transplant recipient. Because of the advances in treating hepatitis C, several teams recently started to use organs from hepatitis C-positive donors. The worldwide increasing incidence of multidrug-resistant pathogens, as well as the increasing incidence ofClostridioidesdifficileinfection, is of particular concern in SOT patients. In the field of mycology, the main recent therapeutic advance is the availability of isavuconazole for the treatment of invasive aspergillosis and mucormycosis. This drug has the advantage of minimal interaction with calcineurin inhibitors. Regarding the viral reactivations occurring after transplant, cytomegalovirus (CMV) infection is still a significant issue in SOT patients. The management of resistant CMV remains particularly difficult. The approval of letermovir, albeit in bone marrow transplantation, and the therapeutic trial of maribavir bring a ray of hope. Another advancement in management of post-transplant infections is the development ofin vitrotests evaluating pathogen-specific immune response, such as immunodiagnostics for CMV and, more recently, tests for monitoring immunity against BK virus. Conclusion:The increasing number of organ transplantations, the use of newer immunosuppressive drugs, and high-risk donors continue to define the landscape of transplant infectious diseases in the current era.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".