The evolution of patient-specific precision biomarkers to guide personalized heart-transplant care

Isolation of Biomolecules Using MXenes

Biomolecule isolation is a crucial process in diverse biomedical and biochemical applications, including diagnostics, therapeutics, research, and manufacturing. Recently, MXenes, a novel class of two-dimensional nanomaterials, have emerged as promising adsorbents for this purpose due to their unique physicochemical properties. These biocompatible and antibacterial nanomaterials feature a high aspect ratio, excellent conductivity, and versatile surface chemistry. This timely review explores the potential of MXenes for isolating a wide range of biomolecules, such as proteins, nucleic acids, and small molecules, while highlighting key future research trends and innovative applications poised to transform the field. This review provides an in-depth discussion of various synthesis methods and functionalization techniques that enhance the specificity and efficiency of MXenes in biomolecule isolation. In addition, the mechanisms by which MXenes interact with biomolecules are elucidated, offering insights into their selective adsorption and customized separation capabilities. This review also addresses recent advancements, identifies existing challenges, and examines emerging trends that may drive the next wave of innovation in this rapidly evolving area.

Transplant Vasculopathy Versus Native Atherosclerosis: Similarities and Differences

Cardiac allograft vasculopathy (CAV) is one of the leading causes of graft failure and death after heart transplantation. Alloimmune-dependent and -independent factors trigger the pathogenesis of CAV through activation of the recipients' (and to a lesser extent donor-derived) immune system. Early diagnosis of CAV is complicated by the lack of clinical symptoms for ischemia in the denervated heart, by the impact of early functional coronary alterations, by the insensitivity of coronary angiography, and by the involvement of small intramyocardial vessels. CAV in general is a panarterial disease confined to the allograft and characterized by diffuse concentric longitudinal intimal hyperplasia in the epicardial coronary arteries and concentric medial disease in the microvasculature. Plaque composition in CAV may include early fibrous and fibrofatty tissue and late atheromatous calcification. In contrast, native coronary atherosclerosis usually develops over decades, is focal, noncircumferential, and typically diminishes proximal parts of the epicardial vessels. The rapid and early development of CAV has an adverse prognostic impact, and current prevention and treatment strategies are of limited efficacy compared with established strategies in native atherosclerosis. Following acute coronary syndromes, patients after heart transplantation were more likely to have accompanying cardiogenic shock and higher mortality compared with acute coronary syndromes patients with native hearts.

Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model

Aims

Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG).

Methods and results

Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78-0.90] and 95% (19/20; 95% CI: 75-100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61-0.96) and 100% (2/2; 95% CI: 16-100%) sensitivity.

Conclusion

An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

Variability in Donor-Derived Cell-Free DNA Scores to Predict Mortality in Heart Transplant Recipients - A Proof-of-Concept Study

BACKGROUND

Over the last decade, expanding use of molecular diagnostics in heart transplantation has allowed implementation of non-invasive surveillance strategies for monitoring allograft health. The commercially available HeartCare platform combines the AlloMap gene expression profiling assay and the AlloSure donor-derived cell-free DNA test (dd-cfDNA). Beyond their established use for assessment of rejection, evidence is building for predictive utility, with the longitudinal AlloMap Variability score previously shown to correlate with the risk of future rejection, graft dysfunction, re-transplantation, or death. In this single-center, retrospective pilot study, we evaluated the performance of a novel AlloSure Variability metric in predicting mortality in a cohort of heart transplant recipients.

METHODS

Seventy-two adult heart transplant recipients with at least 3 concurrent AlloMap/AlloSure results were included. Demographic, clinical, imaging, and laboratory parameters were captured. Variability was defined as the standard deviation of longitudinal AlloMap/AlloSure results. A Cox multivariable adjusted proportional hazards model was used to evaluate the variability metrics as predictors of mortality. Associations between AlloMap/AlloSure variability and donor specific antibody (DSA) status were also assessed.

RESULTS

A total of 5 patients (6.9%) died during a median follow-up of 480 days. In a univariate Cox proportional hazards model, higher AlloSure variability (HR 1.66, 95%CI 1.14 - 2.41), but not AlloMap variability or the cross-sectional AlloSure/AlloMap results was associated with increased mortality risk. Longitudinal AlloSure variability was also higher among patients with both preformed DSA and those developing de novo DSA.

CONCLUSION

Our results suggest that increased variability of dd-cfDNA in heart transplant patients is associated with both mortality risk and the presence of donor specific antibodies. These findings highlight the added value of longitudinal data in the interpretation of AlloMap/AlloSure scores in this population and open the door to larger studies investigating the utility of these metrics in shaping post-transplant clinical care paradigms.

Noninvasive methods to reduce cardiac complications postheart transplant

PURPOSE OF REVIEW

Long-term success of heart transplantation is limited by allograft rejection and cardiac allograft vasculopathy (CAV). Classic management has relied on frequent invasive testing to screen for early features of rejection and CAV to allow for early treatment. In this review, we discuss new developments in the screening and prevention of allograft rejection and CAV.

RECENT FINDINGS

Newer noninvasive screening techniques show excellent sensitivity and specificity for the detection of clinically significant rejection. New biomarkers and treatment targets continue to be identified and await further studies regarding their utility in preventing allograft vasculopathy.

SUMMARY

Noninvasive imaging and biomarker testing continue to show promise as alternatives to invasive testing for allograft rejection. Continued validation of their effectiveness may lead to new surveillance protocols with reduced frequency of invasive testing. Furthermore, these noninvasive methods will allow for more personalized strategies to reduce the complications of long-term immunosuppression whereas continuing the decline in the overall rate of allograft rejection.