Clinical immune‐monitoring strategies for predicting infection risk in solid organ transplantation
Bibliographic record
Abstract
Infectious complications remain a leading cause of morbidity and mortality after solid organ transplantation (SOT), and largely depend on the net state of immunosuppression achieved with current regimens. Cytomegalovirus (CMV) is a major opportunistic viral pathogen in this setting. The application of strategies of immunological monitoring in SOT recipients would allow tailoring of immunosuppression and prophylaxis practices according to the individual's actual risk of infection. Immune monitoring may be pathogen-specific or nonspecific. Nonspecific immune monitoring may rely on either the quantification of peripheral blood biomarkers that reflect the status of a given arm of the immune response (serum immunoglobulins and complement factors, lymphocyte sub-populations, soluble form of CD30), or on the functional assessment of T-cell responsiveness (release of intracellular adenosine triphosphate following a mitogenic stimulus). In addition, various methods are currently available for monitoring pathogen-specific responses, such as CMV-specific T-cell-mediated immune response, based on interferon-γ release assays, intracellular cytokine staining or main histocompatibility complex-tetramer technology. This review summarizes the clinical evidence to date supporting the use of these approaches to the post-transplant immune status, as well as their potential limitations. Intervention studies based on validated strategies for immune monitoring still need to be performed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".