Subclinical Antibody-Mediated Rejection
Bibliographic record
Abstract
Manuel Arias The role of anti-HLA antibodies and, thus, the importance of the humoral response have been relegated for decades in the pathogenesis of transplant rejection and graft loss due to the predominance of the cell theory (T-Centric). However, in recent years, epidemiological and clinical evidence has proven otherwise. In addition, neither the academy nor the industry has deepened in the investigation of new drugs directed to control antibody-mediated allogeneic response. Therefore, the immune management of organ transplant recipients is clearly unsatisfactory, especially in the long term. Beyond general dosing guidelines for immunosuppressive agents and clinical diagnostic tests for rejection, which are focused on organ function but not on immunological markers, there are few objective tools to determine the aggregate status of a patient's alloimmune response. Recent studies indicate that antibody-mediated rejection (ABMR) is among the most important barriers to improving long-term outcomes. In recent years, understanding the roles of acute and chronic ABMR has improved because of major progress in the technical ability to detect and quantify recipient anti-HLA antibody production. Additionally, new knowledge of the immunobiology of B cells and plasma cells that pertains to allograft rejection and tolerance has emerged. Still, questions regarding the classification of ABMR, the accuracy of diagnostic approaches, and the efficacy of various strategies for managing affected patients abound.1 These and other lesser known reasons contribute to the disappointing attrition of grafts on a slowly progressive downward slope observed with impotence by the shortage of effective drugs. Noninvasive biomarkers that could serve as predictive tools or surrogate end points for rejection might help clinicians to individualize immunosuppression (IS) and allow for early intervention, ideally before clinically evident organ dysfunction. Although the growing understanding of organ rejection has provided numerous candidate biomarkers, none has been confirmed in robust validation studies as sufficiently useful to guide clinical practice out of the boundaries of the traditional clinical methods. The development of ABMR caused by donor-specific antibodies (DSA) is considered a risk factor for low graft survival after kidney transplantation. Although less studied, the deleterious consequence of ABMR is being increasingly seen in other solid organ transplantations, such as heart and lung.2 Recent reports have shown that the occurrence of DSA in the liver, classically considered a relatively resistant organ to DSA-mediated injury, can result in lower graft and patient survival.3-8 The diagnosis of ABMR after transplantation has been defined using a combination of clinical, histological, and immunological criteria. Recently, a subclinical variety of ABMR which may also progress into chronic ABMR has been described.9 In these cases, and considering the lack of clinical evidence, the availability of more sensitive diagnostic tests allowing the early detection of low titers of DSA3 and the routine use of protocol biopsies10 could facilitate the early start of an appropriate immunosuppressive treatment.3 This review addresses the spectrum of ABMR after organ transplantation, including some aspects of its pathogenesis, risk factors, and outcomes. In this document, relevant professionals review and update different topics. In the first chapter, Dr Serón reviews the influence of IS in surveillance biopsies and subclinical kidney rejection. In his chapter, early inflammation and its relation with fibrosis and the development of de novo DSA (dnDSA) is assessed, stressing the importance of its prevention. In his chapter, a critical analysis of the role of DSA in liver graft loss is presented by Dr Herrero. Despite that liver transplantation has been considered poorly affected by humoral rejection, recent evidence shows a relationship between acute and chronic rejection and the presence of preformed and dnDSA. Considering the increased risk of graft fibrosis, graft loss, and lower patient survival, DSA should considered in liver transplantation. Considering the of the humoral rejection has been Dr on the importance of strategies to chronic humoral rejection an early ABMR in transplantation is by Dr liver transplantation, humoral rejection has been considered in this recent a role of ABMR in the of which is the most important of and after transplantation. of the clinical, and of rejection, ABMR diagnosis should considered a of of to a has been to among other Dr reviews the of such on graft the development of and the of a to detect as a of graft Despite the important role of ABMR in patient and after transplantation, understanding of the pathogenesis and of ABMR is that DSA has an important role in acute and chronic However, not DSA by in allograft injury, and not ABMR allograft has as a of Therefore, risk strategies for and ABMR are to guide and approaches, including and and cell is a of professionals are in the to organ transplantation. objective is the of the knowledge in organ transplantation, and to the of the of has a by This relevant provided a recent in Serón The presence of inflammation in early surveillance biopsies is with an of fibrosis and has shown to have a in patients with in patients inflammation has also been with a risk of dnDSA. The and of inflammation is with the of IS and with the to that patients are more to have less However, this relation is observed inflammation is in but not clinical have shown that IS with less inflammation after transplantation are also with less However, is early inflammation with is also with the of dnDSA. In this chapter, regarding early inflammation its relation with fibrosis and with and the of its by IS and the of allograft in surveillance for the of transplantation in recipients of observed that the a an early by the presence of subclinical inflammation and the of and, a by the of other chronic or transplant after the in of surveillance biopsies the presence of or subclinical rejection, with an increased risk for the of that the presence of inflammation in the first an increased risk for the of in the as shown in of inflammation on of and with risk factors, and of subclinical rejection in kidney transplantation. the surveillance biopsies in the first inflammation and fibrosis the most various studies early into to the presence or of inflammation and with inflammation is that to inflammation is defined as the presence of cells in and inflammation in is not into of studies that the presence of to a allograft survival in to patients with inflammation or fibrosis is a to different However, the of is with a has not been a of could inflammation may more in an kidney or the of may a more immune response. 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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".